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Editor’s Choice Articles

Editor’s Choice articles are based on recommendations by the scientific editors of MDPI journals from around the world. Editors select a small number of articles recently published in the journal that they believe will be particularly interesting to readers, or important in the respective research area. The aim is to provide a snapshot of some of the most exciting work published in the various research areas of the journal.

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14 pages, 2984 KiB  
Article
Transient Tests for Checking the Trieste Subsea Pipeline: Diving into Fault Detection
by Silvia Meniconi, Bruno Brunone, Lorenzo Tirello, Andrea Rubin, Marco Cifrodelli and Caterina Capponi
J. Mar. Sci. Eng. 2024, 12(3), 391; https://doi.org/10.3390/jmse12030391 - 24 Feb 2024
Cited by 10 | Viewed by 1072
Abstract
Fault detection in subsea pipelines is a difficult problem for several reasons, and one of the most important is the inaccessibility of the system. This criticality can be overcome by using transient test-based techniques. Such an approach is based on the execution of [...] Read more.
Fault detection in subsea pipelines is a difficult problem for several reasons, and one of the most important is the inaccessibility of the system. This criticality can be overcome by using transient test-based techniques. Such an approach is based on the execution of safe transients that result in small over pressures (i.e., on the order of a few meters of water column). In our companion paper, the procedure involving the transient tests was described in detail. This paper analyses the results of the field tests carried out and identifies wall deterioration in some sections of the pipeline. Attention is focused on the numerical procedure based on the joint use of a 1-D numerical model simulating transients in the pressurized flow and analytical relationships and providing the transient response of anomalies such as leaks and wall deterioration. The results obtained are essentially confirmed by the survey carried out by divers. Full article
(This article belongs to the Special Issue Underwater Pipe System Fault Detection)
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<p>Sketch of the Trieste subsea pipeline (SP) (from [<a href="#B2-jmse-12-00391" class="html-bibr">2</a>]).</p> Full article ">Figure 2
<p>Reservoir pipe valve (RPV) system: anomaly effect in the pressure signal during the first characteristic time of the pipe: (<b>a</b>) leak or branch, e.g., [<a href="#B31-jmse-12-00391" class="html-bibr">31</a>]; (<b>b</b>) partially closed in-line valve, e.g., [<a href="#B16-jmse-12-00391" class="html-bibr">16</a>]; (<b>c</b>) extended partial blockage, e.g., [<a href="#B16-jmse-12-00391" class="html-bibr">16</a>]; and (<b>d</b>) wall deterioration (modified from [<a href="#B35-jmse-12-00391" class="html-bibr">35</a>]).</p> Full article ">Figure 3
<p>Pressure signals acquired at sections M and P.</p> Full article ">Figure 4
<p>Pressure signal acquired at section M during the first characteristic time.</p> Full article ">Figure 5
<p>Flow chart illustrating the trial-and-error procedure for diagnosing the pipe system.</p> Full article ">Figure 6
<p>Comparison of experimental and numerical pressure signals at section M, with the numerical model assuming an intact pipeline.</p> Full article ">Figure 7
<p>Comparison of experimental and numerical pressure signals at section M, with the numerical model assuming a leaky pipeline.</p> Full article ">Figure 8
<p>Cross-sectional area of the Trieste subsea pipeline.</p> Full article ">Figure 9
<p>Reflection coefficients for wall deterioration vs. (<b>a</b>) the wall thickness, for a partial external lining deterioration with a constant internal diameter, and (<b>b</b>) the internal diameter for reduced internal wall thickness.</p> Full article ">Figure 10
<p>Comparison of experimental and numerical pressure signals at section M, with the numerical model incorporating assumptions of partial wall deterioration.</p> Full article ">Figure 11
<p>External wall deterioration located in Section A3 (it is worth noting that the poor resolution of the image is due to the peculiarity of the subsea environment).</p> Full article ">
26 pages, 9978 KiB  
Article
Hydrodynamic Modeling of a Large, Shallow Estuary
by Zhen-Gang Ji, M. Zaki Moustafa and John Hamrick
J. Mar. Sci. Eng. 2024, 12(3), 381; https://doi.org/10.3390/jmse12030381 - 23 Feb 2024
Cited by 1 | Viewed by 1099
Abstract
Florida Bay, a large and shallow estuary, serves as a vital habitat for a diverse range of marine species and holds significant environmental, commercial, and recreational value. The Florida Bay ecosystem is under extensive stress due to decades of increased nutrient loads. Based [...] Read more.
Florida Bay, a large and shallow estuary, serves as a vital habitat for a diverse range of marine species and holds significant environmental, commercial, and recreational value. The Florida Bay ecosystem is under extensive stress due to decades of increased nutrient loads. Based on the Environmental Fluid Dynamics Code (EFDC), a hydrodynamic model was developed in this study. The model was calibrated with a comprehensive dataset, including measurements over 7 years from 34 tidal stations, 42 current stations, and 14 temperature and salinity stations. Key findings include the following: (1) the bay exhibits a shift in the tidal regime, transitioning from macro-tidal in the western region to micro-tidal in the central and eastern/northeast regions; (2) local winds and the subtidal variations from the coastal ocean are the primary drivers for the hydrodynamic processes in the eastern and central regions; (3) salinity changes in the bay are primarily controlled by three processes: the net supply of freshwater, the processes that drive mixing within the estuary (e.g., wind, topography, currents), and the exchange of salinity with the coastal ocean. This hydrodynamic model is essential for providing a comprehensive tool to address environmental challenges and sustain the bay’s ecosystem health. Full article
(This article belongs to the Section Coastal Engineering)
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<p>Florida Bay showing major sub-divisions, bathymetry, and mud banks.</p> Full article ">Figure 2
<p>(<b>a</b>): model grid with northeast wetland; (<b>b</b>): blow-up of model grid with the northeast wetland; (<b>c</b>): blow-up of model grid truncated at nominal coastline in the northeast region.</p> Full article ">Figure 2 Cont.
<p>(<b>a</b>): model grid with northeast wetland; (<b>b</b>): blow-up of model grid with the northeast wetland; (<b>c</b>): blow-up of model grid truncated at nominal coastline in the northeast region.</p> Full article ">Figure 2 Cont.
<p>(<b>a</b>): model grid with northeast wetland; (<b>b</b>): blow-up of model grid with the northeast wetland; (<b>c</b>): blow-up of model grid truncated at nominal coastline in the northeast region.</p> Full article ">Figure 3
<p>Bathymetry and topography of the Florida Bay Model.</p> Full article ">Figure 4
<p>(<b>a</b>): Locations of 34 tidal gauges. (<b>b</b>): Locations of 42 current meters. (<b>c</b>): Locations of 14 ENP stations utilized for salinity and temperature calibration.</p> Full article ">Figure 4 Cont.
<p>(<b>a</b>): Locations of 34 tidal gauges. (<b>b</b>): Locations of 42 current meters. (<b>c</b>): Locations of 14 ENP stations utilized for salinity and temperature calibration.</p> Full article ">Figure 4 Cont.
<p>(<b>a</b>): Locations of 34 tidal gauges. (<b>b</b>): Locations of 42 current meters. (<b>c</b>): Locations of 14 ENP stations utilized for salinity and temperature calibration.</p> Full article ">Figure 5
<p>Modeled M<sub>2</sub> tidal amplitudes.</p> Full article ">Figure 6
<p>Observed and predicted low-frequency sea levels at Trout Cove.</p> Full article ">Figure 7
<p>Temperatures at Duck Key for a time period containing observations.</p> Full article ">Figure 8
<p>Salinities at Duck Key for a time period containing observations.</p> Full article ">Figure 9
<p>Modeled salinity on Day 1404 (5 November 1999).</p> Full article ">Figure 10
<p>Comparison of low-frequency water surface elevations at Key West (the western boundary of the FBM).</p> Full article ">Figure 11
<p>Comparison of low-frequency water surface elevations at Trout Cove: observed, modeled with observed OBC, and modeled with HYCOM OBC.</p> Full article ">
15 pages, 20258 KiB  
Article
Transient Tests for Checking the Trieste Subsea Pipeline: Toward Field Tests
by Silvia Meniconi, Bruno Brunone, Lorenzo Tirello, Andrea Rubin, Marco Cifrodelli and Caterina Capponi
J. Mar. Sci. Eng. 2024, 12(3), 374; https://doi.org/10.3390/jmse12030374 - 22 Feb 2024
Cited by 12 | Viewed by 1160
Abstract
Subsea pipelines are vital arteries transporting oil, gas, and water over long distances and play a critical role in the global resource supply chain. However, they are the most vulnerable to damage from both human-made and natural causes and are characterized by inherent [...] Read more.
Subsea pipelines are vital arteries transporting oil, gas, and water over long distances and play a critical role in the global resource supply chain. However, they are the most vulnerable to damage from both human-made and natural causes and are characterized by inherent inaccessibility. As a result, routine inspection and monitoring technologies, both reliable and at the lowest possible cost, are needed to ensure their longevity. To fill this need, the use of transient-test-based techniques is proposed. In this first paper of a set of two companion papers, attention is focused on the selection of the appropriate maneuver that generates pressure waves and then on the planned steps—i.e., the sequence of actions—functional to the execution of the transient tests in the best flow conditions for effective fault detection. A brief review of the available fault detection technologies with their limitations is also offered. Finally, the performance of the proposed procedure is evaluated mainly in terms of the stability of the pressure regime prior to the execution of the transient test. Full article
(This article belongs to the Special Issue Underwater Pipe System Fault Detection)
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<p>Typical example of the environment around a subsea pipeline (SP) and the condition of its outer surface (the SP is illuminated by the divers’ powerful torches).</p> Full article ">Figure 2
<p>The Trieste water supply system managed by AcegasApsAmga SpA (Hera Group).</p> Full article ">Figure 3
<p>Sketch of the Trieste subsea pipeline (SP).</p> Full article ">Figure 4
<p>Hydraulic characterization of the side discharge valve (SDV): (<b>a</b>) the experimental set-up at the Water Engineering Laboratory (WEL) of the University of Perugia, Italy, and (<b>b</b>) the SDV flow-rate curve.</p> Full article ">Figure 5
<p>Transient test carried out at the Water Engineering Laboratory. The time-history of the dimensionless pressure signal, <span class="html-italic">h</span>, is shown, highlighting the evaluation of the maneuver duration, <math display="inline"><semantics> <msub> <mi>τ</mi> <mrow> <mi>S</mi> <mi>D</mi> <mi>V</mi> </mrow> </msub> </semantics></math>, which is about 0.03 s.</p> Full article ">Figure 6
<p>Inserted pressure wave, <math display="inline"><semantics> <mrow> <mo>Δ</mo> <mi>H</mi> </mrow> </semantics></math>, vs. SDV relative opening degree, <math display="inline"><semantics> <mi>δ</mi> </semantics></math>, and pressure wave speed, <span class="html-italic">a</span>.</p> Full article ">Figure 7
<p>Characteristics of the measurement sections: (<b>a</b>) VP and measurement section P; (<b>b</b>) VM and measurement section M; (<b>c</b>) typical equipment for pressure measurement.</p> Full article ">Figure 8
<p>Pressure signals at sections P and M acquired during the scheduled survey (<a href="#jmse-12-00374-t001" class="html-table">Table 1</a>).</p> Full article ">Figure 9
<p>Analysis of the system’s transient response: (<b>a</b>) pressure signal at M, acquired during the VM closing; and (<b>b</b>) dimensionless pressure variation, <math display="inline"><semantics> <mrow> <mo>Δ</mo> <msub> <mi>h</mi> <mrow> <mi>p</mi> <mi>e</mi> <mi>a</mi> <mi>k</mi> <mi>s</mi> </mrow> </msub> </mrow> </semantics></math>.</p> Full article ">Figure 10
<p>Pressure signals, <span class="html-italic">H</span>, in section M, acquired during the scheduled and extemporaneous surveys: (<b>a</b>) time-domain analysis and (<b>b</b>) frequency-domain analysis.</p> Full article ">
15 pages, 4009 KiB  
Article
Population Genetics Assessment of the Model Coral Species Stylophora pistillata from Eilat, the Red Sea
by Elad Nehoray Rachmilovitz, Jacob Douek and Baruch Rinkevich
J. Mar. Sci. Eng. 2024, 12(2), 315; https://doi.org/10.3390/jmse12020315 - 12 Feb 2024
Cited by 1 | Viewed by 1507
Abstract
The successful management of coral reefs necessitates understanding the genetic characteristics of reefs’ populations since levels of genetic diversity play a critical role in their resilience, enabling them to withstand environmental changes with greater efficacy. To assess the genetic diversity and connectivity of [...] Read more.
The successful management of coral reefs necessitates understanding the genetic characteristics of reefs’ populations since levels of genetic diversity play a critical role in their resilience, enabling them to withstand environmental changes with greater efficacy. To assess the genetic diversity and connectivity of the widespread Indo-Pacific coral, Stylophora pistillata, eight microsatellite loci were employed on 380 tissue samples collected from eight sites along the northern Gulf of Eilat, Red Sea. We documented deviations from the Hardy–Weinberg equilibrium and observed low heterozygosity and high values of expected heterozygosity (0.59 and 0.82, respectively). The relatively high FST values and STRUCTURE analysis results showed population fragmentation along the short coastline (<12 km). These results signify isolation by distance, low gene flow between most populations, and possible non-random mating. These results are connected to this species’ sexual reproduction traits, a brooding coral species with planulae that settle shortly upon release with limited connectivity that are most probably further exacerbated by anthropogenic impacts imposed on Eilat’s reefs. This study provides insights into the connectivity and population genetics of S. pistillata residing in an urbanized northern Red Sea reef and reinforces the need for better management of the current MPA, employing future active coral reef restoration in the area. Full article
(This article belongs to the Section Marine Biology)
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<p><span class="html-italic">Stylophora pistillata</span> (Esper 1797) colonies in the Gulf of Eilat.</p> Full article ">Figure 2
<p>Sampling sites along the Gulf of Eilat’s coastline: Jordan Border beach (JB), North Shore beach (NB), Kisoski beach (KI), Dekel beach shallow (DES), Dekel beach deep (DED), Tour Yam beach (TY), Underwater Observatory beach (UO), Lighthouse beach shallow (LIS), Lighthouse beach deep (LID), and Egypt Border beach (EB). *—Study area.</p> Full article ">Figure 3
<p>Gene flow trajectories for each sampling site [%], assessing the likelihood of the gene pool (microsatellite alleles) to be the ‘self-site’ or ‘other-site’ population. Arrows indicate gene flow from one site to the others. Numbers are percentage [%] of contribution. Values for all 10 sampling sites of <span class="html-italic">Stylophora pistillata</span> in the Gulf of Eilat, Israel: Jordan Border beach (JB), North Shore beach (NB), Kisoski beach (KI), Dekel beach shallow (DES), Dekel beach deep (DED), Tour Yam beach (TY), Underwater Observatory beach (UO), Lighthouse beach shallow (LIS), Lighthouse beach deep (LID), and Egypt Border beach (EB).</p> Full article ">Figure 4
<p>A 3D factorial analysis of correspondence (FAC) of <span class="html-italic">Stylophora pistillata</span> populations in the Gulf of Eilat, Israel, generated by GENETIX on the sampled individuals per site. Jordan Border beach (JB), North Shore beach (NB), Kisoski beach (KI), Dekel beach shallow (DES), Dekel beach deep (DED), Tour Yam beach (TY), Underwater Observatory beach (UO), Lighthouse beach shallow (LIS), Lighthouse beach deep (LID), and Egypt Border beach (EB).</p> Full article ">Figure 5
<p>Cluster analysis for all 10 <span class="html-italic">Stylophora pistillata</span> sampling sites in Eilat, Israel. (<b>Top panel</b>) was created by STRUCTURE (optimal K = 4) and (<b>bottom panel</b>) BAPS (optimal K = 7). The y-axis indicates the allocation probability of each sampling site into a distinct cluster, as indicated by the assigned colors. Sampling sites are shown on the x-axis. Jordan Border beach (JB), North Shore beach (NB), Kisoski beach (KI), Dekel beach shallow (DES), Dekel beach deep (DED), Tour Yam beach (TY), Underwater Observatory beach (UO), Lighthouse beach shallow (LIS), Lighthouse beach deep (LID), and Egypt Border beach (EB).</p> Full article ">
27 pages, 12387 KiB  
Article
Enhanced Mild-Slope Wave Model with Parallel Implementation and Artificial Neural Network Support for Simulation of Wave Disturbance and Resonance in Ports
by Michalis K. Chondros, Anastasios S. Metallinos and Andreas G. Papadimitriou
J. Mar. Sci. Eng. 2024, 12(2), 281; https://doi.org/10.3390/jmse12020281 - 4 Feb 2024
Cited by 1 | Viewed by 1136
Abstract
Ensuring sea surface tranquility within port basins is of paramount importance for safe and efficient port operations and vessels’ accommodation. The present study aims to introduce a robust numerical model based on mild-slope equations, capable of accurately simulating wave disturbance and resonance in [...] Read more.
Ensuring sea surface tranquility within port basins is of paramount importance for safe and efficient port operations and vessels’ accommodation. The present study aims to introduce a robust numerical model based on mild-slope equations, capable of accurately simulating wave disturbance and resonance in ports. The model is further enhanced by the integration of an artificial neural network (ANN) to address partial reflection, and its efficiency is optimized by developing a parallel algorithm based on OpenMP, allowing for a reduction in the required simulation times for real port areas spanning several kilometers horizontally. Numerous numerical experiments focusing on wave reflection against a vertical wall were conducted to develop the ANN. This neural network was designed to determine the appropriate value of the eddy viscosity coefficient, a crucial parameter in the momentum equation of the mild-slope model, tailored to incident wave characteristics. The model’s validity was confirmed through rigorous validation against experimental measurements, covering wave disturbance, rectangular harbor resonance, and Bragg resonance. The model consistently demonstrated a more than satisfactory performance across all considered scenarios. In a practical application, the model was deployed in the Port of Rethymno, Crete Island, Greece, effectively capturing and describing dominant phenomena within the port area. The implementation of a parallel algorithm significantly reduced the simulation times by ~92%, compared to the serial algorithm, thereby enhancing the model’s efficiency and applicability in real-world port environments. Full article
(This article belongs to the Section Coastal Engineering)
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<p>Flowchart depicting the integration of the ANN into the simulation process of partial reflection using the proposed mild-slope model.</p> Full article ">Figure 2
<p>Graphical representation of the parallel implementation working principle.</p> Full article ">Figure 3
<p>Sketch of idealized 1DH wave flume used for numerical experiments.</p> Full article ">Figure 4
<p>Envelope plot of the surface elevation (lower bound: dark blue line, upper bound: light blue line) for a regular wave scenario (<span class="html-italic">d</span>/<span class="html-italic">L</span> = 0.16, <span class="html-italic">H</span>/<span class="html-italic">L</span> = 0.01). (<b>a</b>) No reflection—sponge layers on both sides of the flume (<math display="inline"><semantics> <mrow> <msub> <mrow> <mi>v</mi> </mrow> <mrow> <mi>h</mi> </mrow> </msub> <mo>=</mo> <mn>0</mn> </mrow> </semantics></math> m<sup>2</sup>/s, <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>C</mi> </mrow> <mrow> <mi>R</mi> <mi>r</mi> <mi>e</mi> <mi>g</mi> </mrow> </msub> <mo>=</mo> <mn>0.0</mn> </mrow> </semantics></math>); (<b>b</b>) full reflection—vertical wall on east side of the flume without eddy viscosity (<math display="inline"><semantics> <mrow> <msub> <mrow> <mi>v</mi> </mrow> <mrow> <mi>h</mi> </mrow> </msub> <mo>=</mo> <mn>0</mn> </mrow> </semantics></math> m<sup>2</sup>/s, <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>C</mi> </mrow> <mrow> <mi>R</mi> <mi>r</mi> <mi>e</mi> <mi>g</mi> </mrow> </msub> <mo>=</mo> <mn>1.0</mn> </mrow> </semantics></math>); (<b>c</b>) partial reflection—vertical wall on east side of the flume with eddy viscosity (<math display="inline"><semantics> <mrow> <msub> <mrow> <mi>v</mi> </mrow> <mrow> <mi>h</mi> </mrow> </msub> <mo>=</mo> <mn>20</mn> </mrow> </semantics></math> m<sup>2</sup>/s, <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>C</mi> </mrow> <mrow> <mi>R</mi> <mi>r</mi> <mi>e</mi> <mi>g</mi> </mrow> </msub> <mo>=</mo> <mn>0.77</mn> </mrow> </semantics></math>).</p> Full article ">Figure 5
<p>Error and performance metrics of the training procedure: (<b>a</b>) regular waves; (<b>b</b>) unidirectional irregular waves.</p> Full article ">Figure 6
<p>Numerical layout of the Davies and Heathersaw [<a href="#B84-jmse-12-00281" class="html-bibr">84</a>] experiments.</p> Full article ">Figure 7
<p>Comparison of simulated reflection coefficients and experimental data for the case of 4 sinusoidal bars of the experiments of Davies &amp; Heathersaw [<a href="#B84-jmse-12-00281" class="html-bibr">84</a>].</p> Full article ">Figure 8
<p>Simulated and measured response curve at the center of the backwall (point A) of a fully open rectangular harbor [<a href="#B89-jmse-12-00281" class="html-bibr">89</a>,<a href="#B90-jmse-12-00281" class="html-bibr">90</a>].</p> Full article ">Figure 9
<p>Numerical layout for the experiments of Yu et al. [<a href="#B83-jmse-12-00281" class="html-bibr">83</a>], indicating the positions of the two sections (marked by dashed lines) from which wave characteristics were extracted.</p> Full article ">Figure 10
<p>Comparison of simulated (solid blue lines) and measured (dashdot lines) results of the diffraction coefficients at the two cross sections for the simulated cases of Yu et al. [<a href="#B83-jmse-12-00281" class="html-bibr">83</a>] experiments.</p> Full article ">Figure 11
<p>Numerical layout of the experiments of Van Mierlo [<a href="#B95-jmse-12-00281" class="html-bibr">95</a>], and positions of measuring wave gauges.</p> Full article ">Figure 12
<p>Relationship between reflection and eddy viscosity coefficient as predicted by the ANN: (<b>a</b>) breakwater; (<b>b</b>) gravel slopes.</p> Full article ">Figure 13
<p>Significant wave height for case T079 (Van Mierlo [<a href="#B95-jmse-12-00281" class="html-bibr">95</a>]) as simulated by the HMS model in prototype scale, superimposed with wave gauge locations.</p> Full article ">Figure 14
<p>Comparison between measured (circular markers) and simulated (triangle markers) of the wave heights at each wave gauge. The error bars correspond to a 15% percent margin.</p> Full article ">Figure 15
<p>Speedup factor of parallel algorithm versus number of threads.</p> Full article ">Figure 16
<p>Simulation results of nearshore wave field at the Port of Rethymno for an incident wave propagating from NNW (330<sup>o</sup> N) with the following offshore characteristics: <span class="html-italic">H<sub>s</sub></span> = 2 m and <span class="html-italic">T<sub>p</sub></span> = 7s.</p> Full article ">
35 pages, 19756 KiB  
Article
Dense Water Formation in the North–Central Aegean Sea during Winter 2021–2022
by Manos Potiris, Ioannis G. Mamoutos, Elina Tragou, Vassilis Zervakis, Dimitris Kassis and Dionysios Ballas
J. Mar. Sci. Eng. 2024, 12(2), 221; https://doi.org/10.3390/jmse12020221 - 25 Jan 2024
Cited by 4 | Viewed by 1369
Abstract
The evolution and drivers of dense water formation (DWF) in the North–Central Aegean Sea (NCAeg) during winter 2021–2022 are studied using observations from two Argo floats and the output of an operational data-assimilating model. Dense water with [...] Read more.
The evolution and drivers of dense water formation (DWF) in the North–Central Aegean Sea (NCAeg) during winter 2021–2022 are studied using observations from two Argo floats and the output of an operational data-assimilating model. Dense water with σθ>29.1 kgm−3 was produced over most of the NCAeg, except for the northeastern part covered by Black Sea water (BSW), where the maximum surface density was <29 kgm−3. The highest density waters were produced over the central and southern parts of the Lemnos Plateau and in the shallow coastal areas between Chios Island and the Edremit Gulf. Atmospherically driven transformation to the east of Lesvos Island resulted in the production of waters with anomalously high density and salinity, which flowed inside Skiros Basin, thus partly explaining its historically higher density and salinity compared to the rest of the NCAeg subbasins. The Skiros and Athos Basins were ventilated down to σθ∼29.35 kgm−3 horizons. The 29.1 kgm−3 isopycnal rose by ∼200 m, and the 29.25 kgm−3 isopycnal overflowed above the ∼400 m sill depth filling the southern depressions of the NCAeg. Combining data from Argo floats, vessel casts, gliders, and a fixed-point observatory, the dense water produced in the NCAeg was observed spreading in the deep layer of the Central Cretan Sea for at least one and a half years after the formation. The cyclonic circulation of the newly formed water in the NCAeg has been observed directly for the first time using deep-drifting floats. The Eastern Mediterranean warming and salinification signal has propagated below the NCAeg sill depth. The winter average buoyancy loss was comparable to that of the peak of the Eastern Mediterranean transient (EMT) and other known years of DWF in the NCAeg; however, the high temperature of the upper layers due to long-term warming prevented the widespread formation of denser water. Full article
(This article belongs to the Special Issue Air-Sea Interaction and Marine Dynamics)
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Figure 1
<p>(<b>a</b>) Bathymetry map of the Aegean Sea and neighbouring seas. The black box denotes the model domain. Coloured arrows denote the main inflowing water masses from the Cretan straits. (<b>b</b>) Schematic diagram of the surface and intermediate circulation. Yellow arrows denote the pathways of brackish and cold Black Sea water, red arrows the pathways of the Levantine intermediate water, and orange arrows the pathways of the mixture of the Black Sea and Levantine intermediate waters.</p> Full article ">Figure 2
<p>Map of the study area. (<b>a</b>) North–Central Aegean Sea (NCAeg) bathymetry, along with topographic and geographic features referred to in the text. (<b>b</b>) Trajectories of the two Argo floats, which sampled the study area from autumn 2021 to summer 2022. Only selected bathymetry contours which make the NCAeg sill depth easier to identify are shown in (<b>b</b>). The EMODnet bathymetry has been used for the production of the map [<a href="#B82-jmse-12-00221" class="html-bibr">82</a>].</p> Full article ">Figure 3
<p>North–Central Aegean (NCAeg) <math display="inline"><semantics> <mrow> <mi>θ</mi> <mo>/</mo> <mi>S</mi> </mrow> </semantics></math> diagrams and probability density functions of temperature and salinity from the sea surface down to 600 <math display="inline"><semantics> <mi mathvariant="normal">m</mi> </semantics></math> depth for the periods (<b>a</b>) 1985–1998 and (<b>b</b>) 2018–2022. The inset maps of panels (<b>a</b>,<b>b</b>) show the spatial distribution of stations for each period. Ionian and Levantine Seas year-mean (<b>c</b>) temperature and (<b>d</b>) salinity depth-averaged from the surface down to 400 <math display="inline"><semantics> <mi mathvariant="normal">m</mi> </semantics></math>. The inset map in (<b>d</b>) denotes the area of spatial averaging.</p> Full article ">Figure 4
<p>Time-mean and field-mean net buoyancy fluxes and their components. Time-mean (<b>a</b>) freshwater, (<b>b</b>) thermal, and (<b>c</b>) net buoyancy flux for the period 2 December 2021–30 April 2022. Field-mean (<b>d</b>) freshwater, (<b>e</b>) thermal, and (<b>f</b>) net buoyancy flux averaged over the North Aegean. Both two-hourly and daily-averaged field-mean fluxes are shown.</p> Full article ">Figure 5
<p>(<b>a</b>) Net, (<b>b</b>) thermal, and (<b>c</b>) freshwater atmospherically driven water mass transformation rate (WMT) over North–Central Aegean Sea (NCAeg) from 02 December 2021 to 30 April 2022 as a function of outcrop density and time. The time-mean values, along with ± at one standard deviationin, are shown in the left panels. Note the different freshwater scale. Net and thermal rates <math display="inline"><semantics> <mrow> <mo>&lt;</mo> <mn>5</mn> <mo>×</mo> <msup> <mn>10</mn> <mrow> <mo>−</mo> <mn>4</mn> </mrow> </msup> <mspace width="4pt"/> <mi mathvariant="normal">Sv</mi> </mrow> </semantics></math> and freshwater rates <math display="inline"><semantics> <mrow> <mo>&lt;</mo> <msup> <mn>10</mn> <mrow> <mo>−</mo> <mn>4</mn> </mrow> </msup> <mspace width="4pt"/> <mi mathvariant="normal">Sv</mi> </mrow> </semantics></math> have been masked.</p> Full article ">Figure 6
<p>Trajectories and hydrographic observations of Argo float 6903297. Time–depth diagrams of (<b>a</b>) potential temperature, (<b>b</b>) salinity, and (<b>c</b>) potential density. (<b>d</b>) <math display="inline"><semantics> <mrow> <mi>θ</mi> <mo>/</mo> <mi>S</mi> </mrow> </semantics></math> diagram and (<b>e</b>,<b>f</b>) maps with the location of the profiles. To facilitate the visual inspection of time–depth diagrams, only selected contours are shown and labelled. For clarity, the range of axes of the <math display="inline"><semantics> <mrow> <mi>θ</mi> <mo>/</mo> <mi>S</mi> </mrow> </semantics></math> diagram is restricted only to a portion of the observed temperature and salinity range. Float observations have been distinguished in three phases (preconditioning, convection, restratification), which are indicated by different colours in the stand-alone time axis and the legend of the <math display="inline"><semantics> <mrow> <mi>θ</mi> <mo>/</mo> <mi>S</mi> </mrow> </semantics></math> diagram. The first profile of the float (marked with red in the profile location maps) serves technical check purposes and does not follow the sampling plan.</p> Full article ">Figure 7
<p>Same as <a href="#jmse-12-00221-f006" class="html-fig">Figure 6</a> but for Argo float 6903298. Notice the different colour scales of time–depth plots between this figure and <a href="#jmse-12-00221-f006" class="html-fig">Figure 6</a>.</p> Full article ">Figure 8
<p>(<b>a</b>) Hypsometric curve of the North–Central Aegean Sea. (<b>b</b>) Time evolution of cumulative histogram of MLD normalised by the number of model grid points showing the fraction of total area over which each MLD value was observed. (<b>c</b>) Time evolution of cumulative histogram of potential density at depth <math display="inline"><semantics> <mrow> <mi>z</mi> <mo>=</mo> <mn>10</mn> <mo> </mo> <mi mathvariant="normal">m</mi> </mrow> </semantics></math> normalised by the number of model grid points showing the fraction of total area over which each potential density value was observed. (<b>d</b>) Fraction of total time (2 December 2021–30 April 2022) for which <math display="inline"><semantics> <mrow> <mn>29</mn> <mo> </mo> <mrow> <msup> <mrow> <mrow> <mi>kg</mi> <mi mathvariant="normal">m</mi> </mrow> </mrow> <mrow> <mo>−</mo> <mn>3</mn> </mrow> </msup> </mrow> <mo>&lt;</mo> <msub> <mi>σ</mi> <mi>θ</mi> </msub> <mo>&lt;</mo> <mn>29.1</mn> <mo> </mo> <mrow> <msup> <mrow> <mrow> <mi>kg</mi> <mi mathvariant="normal">m</mi> </mrow> </mrow> <mrow> <mo>−</mo> <mn>3</mn> </mrow> </msup> </mrow> </mrow> </semantics></math>, (<b>e</b>) fraction of total time for which <math display="inline"><semantics> <mrow> <msub> <mi>σ</mi> <mi>θ</mi> </msub> <mo>&gt;</mo> <mn>29.1</mn> <mo> </mo> <mrow> <msup> <mrow> <mrow> <mi>kg</mi> <mi mathvariant="normal">m</mi> </mrow> </mrow> <mrow> <mo>−</mo> <mn>3</mn> </mrow> </msup> </mrow> </mrow> </semantics></math>, (<b>f</b>) fraction of total time for which <math display="inline"><semantics> <mrow> <msub> <mi>σ</mi> <mi>θ</mi> </msub> <mo>&gt;</mo> <mn>29.1</mn> <mo> </mo> <mrow> <msup> <mrow> <mrow> <mi>kg</mi> <mi mathvariant="normal">m</mi> </mrow> </mrow> <mrow> <mo>−</mo> <mn>3</mn> </mrow> </msup> </mrow> </mrow> </semantics></math>, and mixed layer depth that reached the bottom. The cumulative histogram of potential density was low-passed with a 26 <math display="inline"><semantics> <mi mathvariant="normal">h</mi> </semantics></math> window.</p> Full article ">Figure 9
<p>Variability of the thermohaline properties and depth of isopycnal <math display="inline"><semantics> <mrow> <mn>29.1</mn> </mrow> </semantics></math> <math display="inline"><semantics> <mrow> <msup> <mrow> <mrow> <mi>kg</mi> <mi mathvariant="normal">m</mi> </mrow> </mrow> <mrow> <mo>−</mo> <mn>3</mn> </mrow> </msup> </mrow> </semantics></math> before and after the convective period. Time-mean (<b>a</b>) potential temperature, (<b>b</b>) salinity, and (<b>c</b>) depth of isopycnal <math display="inline"><semantics> <mrow> <mn>29.1</mn> </mrow> </semantics></math> <math display="inline"><semantics> <mrow> <msup> <mrow> <mrow> <mi>kg</mi> <mi mathvariant="normal">m</mi> </mrow> </mrow> <mrow> <mo>−</mo> <mn>3</mn> </mrow> </msup> </mrow> </semantics></math> from 2 to 15 December 2021. Time-mean (<b>d</b>) potential temperature, (<b>e</b>) salinity, and (<b>f</b>) depth of isopycnal <math display="inline"><semantics> <mrow> <mn>29.1</mn> </mrow> </semantics></math> <math display="inline"><semantics> <mrow> <msup> <mrow> <mrow> <mi>kg</mi> <mi mathvariant="normal">m</mi> </mrow> </mrow> <mrow> <mo>−</mo> <mn>3</mn> </mrow> </msup> </mrow> </semantics></math> from 15 to 30 April 2022.</p> Full article ">Figure 10
<p>(<b>a</b>) Volumetric <math display="inline"><semantics> <mrow> <mi>θ</mi> <mo>/</mo> <mi>S</mi> </mrow> </semantics></math> diagram at depth of <math display="inline"><semantics> <mrow> <mi>z</mi> <mo>=</mo> <mn>10</mn> <mo> </mo> <mi mathvariant="normal">m</mi> </mrow> </semantics></math> from 2 December 2021 to 30 April 2022. (<b>b</b>) Volumetric <math display="inline"><semantics> <mrow> <mi>θ</mi> <mo>/</mo> <mi>S</mi> </mrow> </semantics></math> diagram at depth of <math display="inline"><semantics> <mrow> <mi>z</mi> <mo>=</mo> <mn>10</mn> <mo> </mo> <mi mathvariant="normal">m</mi> </mrow> </semantics></math> during the period for which the highest <math display="inline"><semantics> <mrow> <mn>10</mn> <mo>%</mo> </mrow> </semantics></math> values of potential density were observed and boxes that define the water mass patches. (<b>c</b>) Spatial distribution of patches defined in panel (<b>b</b>) overlaid with the average circulation for the same period. Time-mean (<b>d</b>) potential density, (<b>e</b>) potential temperature, and (<b>f</b>) salinity at depth of <math display="inline"><semantics> <mrow> <mi>z</mi> <mo>=</mo> <mn>10</mn> <mo> </mo> <mi mathvariant="normal">m</mi> </mrow> </semantics></math> during the period for which the highest <math display="inline"><semantics> <mrow> <mn>10</mn> <mo>%</mo> </mrow> </semantics></math> values of potential density are observed. Only potential density values above 29 <math display="inline"><semantics> <mrow> <msup> <mrow> <mrow> <mi>kg</mi> <mi mathvariant="normal">m</mi> </mrow> </mrow> <mrow> <mo>−</mo> <mn>3</mn> </mrow> </msup> </mrow> </semantics></math> are shown.</p> Full article ">Figure 11
<p>Time-mean ratio and field-mean ratio of heat, salt, and buoyancy content change explained by surface fluxes in the mixed layer and in the whole water column. Time-mean ratio of (<b>a</b>) heat, (<b>b</b>) salt, and (<b>c</b>) buoyancy content in the mixed layer. Time-mean ratio of (<b>d</b>) heat, (<b>e</b>) salt, and (<b>f</b>) buoyancy from surface to bottom. Daily-averaged field-mean ratio of (<b>g</b>) heat, (<b>h</b>) salt, and (<b>i</b>) buoyancy in the mixed layer and from surface to bottom.</p> Full article ">Figure 12
<p>Spatiotemporal variability of the mixing regime (free, forced, mixed) <span class="html-italic">R</span> in the surface mixed layer. Spatial distribution of fraction of total time (2 December 2021–30 April 2022) for which turbulence in the mixed layer was attributed to (<b>a</b>) buoyancy loss, (<b>b</b>) wind stress, and (<b>c</b>) both buoyancy loss and wind stress. Temporal variability of fraction of North–Central Aegean (NCAeg) surface area for which turbulence in the mixed layer was attributed to (<b>d</b>) buoyancy loss, (<b>e</b>) wind stress, and (<b>f</b>) both buoyancy loss and wind stress. The daily averaged fractions of the NCAeg surface area are also shown.</p> Full article ">Figure 13
<p>(<b>a</b>) Map with the location of E1-M3A observatory, Argo profiles, and one CTD cast from vessel next to E1-M3A. Vessel CTD cast (<b>b</b>) salinity and (<b>c</b>) potential temperature from 600 m to 1400 m at E1-M3A on 13 September 2021. (<b>d</b>) <math display="inline"><semantics> <mrow> <mi>θ</mi> <mo>/</mo> <mi>S</mi> </mrow> </semantics></math> diagram with Argo profiles, vessel CTD cast, monthly mean/std E1-M3A time series at 20 m from January 2022 to April 2022 and at 1000 m from December 2021 to July 2023. E1-M3A monthly mean/std time series of (<b>e</b>) potential temperature, (<b>f</b>) salinity, and (<b>g</b>) potential density at 1000 m from May 2007 to July 2023. The values of the vessel CTD cast at 1000 m in September 2021 are also shown in panels (<b>e</b>–<b>g</b>).</p> Full article ">
16 pages, 5462 KiB  
Article
Beach Litter Variability According to the Number of Visitors in Cádiz Beaches, SW Spain
by Gonzalo Fernández García, Francisco Asensio-Montesinos, Giorgio Anfuso and Pedro Arenas-Granados
J. Mar. Sci. Eng. 2024, 12(2), 201; https://doi.org/10.3390/jmse12020201 - 23 Jan 2024
Cited by 2 | Viewed by 1025
Abstract
The amount and composition of litter was evaluated during May and June 2021 at two urban beaches, i.e., La Victoria and La Cortadura, in Cádiz, SW Spain. Surveys were carried out daily in the morning and in the evening during the weekends to [...] Read more.
The amount and composition of litter was evaluated during May and June 2021 at two urban beaches, i.e., La Victoria and La Cortadura, in Cádiz, SW Spain. Surveys were carried out daily in the morning and in the evening during the weekends to quantify the daily accumulation of beach litter and relate it to the number of beach users, which was assessed at around 1:00 p.m. Litter amount was also related to cleanup operations that were very mechanically and manually carried out each day very early in the morning. A total of 8108 items were collected at the two investigated sectors during the study period and beach visitors were quantified in 22 surveys. Plastic was the most common material, representing 82% in La Victoria and 68% in La Cortadura. The most common items were cigarette butts and small, hard plastic fragments. Some litter items that were hazardous to beach visitors were identified, such as broken glass. The number of visitors was positively related to the amount of litter. Significant differences were seen in the litter abundance between the morning and evening assessments since the beaches were cleaned daily and bins were available to facilitate trash disposal. Cleaning operations remove many of the litter items but always leave small quantities of small items uncollected. Efforts to prevent litter on these beaches should focus on informing visitors properly in order to avoid littering and on improving cleanup operations. Full article
(This article belongs to the Special Issue Natural and Human Impacts in Coastal Areas)
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<p>Location map of the study areas: La Victoria and La Cortadura beaches.</p> Full article ">Figure 2
<p>Box plots of the total number of beach litter in each of the 22 surveys at La Victoria and La Cortadura.</p> Full article ">Figure 3
<p>Numbers of beach visitors and amount of beach litter (recorded during the morning and evening surveys) during the study period at the investigated sectors: (<b>a</b>) La Victoria and (<b>b</b>) La Cortadura beaches.</p> Full article ">Figure 4
<p>Beach visitors versus litter amount recorded during the morning and evening surveys at La Victoria and La Cortadura beaches.</p> Full article ">Figure 5
<p>Beach litter composition at the investigated sites of La Victoria and La Cortadura.</p> Full article ">Figure 6
<p>Efficacy of beach cleanup operations at the two beach sectors in La Victoria and La Cortadura, expressed as a percentage of items not collected. The level of efficacy was obtained by comparing the amount of morning beach litter, i.e., the data recorded after the beach cleanup operations, with the amount of beach litter observed the previous evening and due to the use of the beach during the day.</p> Full article ">Figure 7
<p>Items generated daily (litter amount in the evening minus in the morning) divided by the number of visitors at La Victoria and La Cortadura beaches.</p> Full article ">Figure A1
<p>(<b>a</b>) Numbers of beach visitors during the study period at the investigated sectors at La Victoria and La Cortadura beaches. Regression lines and R<sup>2</sup> values are also presented. Amount of beach litter at La Victoria (<b>b</b>) and La Cortadura (<b>c</b>) recorded during the morning and the evening surveys. Regression lines and R<sup>2</sup> values are also presented.</p> Full article ">Figure A1 Cont.
<p>(<b>a</b>) Numbers of beach visitors during the study period at the investigated sectors at La Victoria and La Cortadura beaches. Regression lines and R<sup>2</sup> values are also presented. Amount of beach litter at La Victoria (<b>b</b>) and La Cortadura (<b>c</b>) recorded during the morning and the evening surveys. Regression lines and R<sup>2</sup> values are also presented.</p> Full article ">Figure A2
<p>Normalized beach litter content recorded during the evening survey versus normalized number of visitors at the La Victoria and La Cortadura beach sectors.</p> Full article ">Figure A3
<p>Some of the most common litter items observed during the study.</p> Full article ">
16 pages, 9534 KiB  
Article
Prediction of Beach Sand Particle Size Based on Artificial Intelligence Technology Using Low-Altitude Drone Images
by Ho-Jun Yoo, Hyoseob Kim, Tae-Soon Kang, Ki-Hyun Kim, Ki-Young Bang, Jong-Beom Kim and Moon-Sang Park
J. Mar. Sci. Eng. 2024, 12(1), 172; https://doi.org/10.3390/jmse12010172 - 16 Jan 2024
Cited by 2 | Viewed by 1389
Abstract
Coastal erosion is caused by various factors, such as harbor development along coastal areas and climate change. Erosion has been accelerated recently due to sea level rises, increased occurrence of swells, and higher-power storm waves. Proper understanding of the complex coastal erosion process [...] Read more.
Coastal erosion is caused by various factors, such as harbor development along coastal areas and climate change. Erosion has been accelerated recently due to sea level rises, increased occurrence of swells, and higher-power storm waves. Proper understanding of the complex coastal erosion process is vital to prepare measures when they are needed. Monitoring systems have been widely established around a high portion of the Korean coastline, supported by several levels of governments, but valid analysis of the collected data and the following preparation of measures have not been highly effective yet. In this paper, we use a drone to obtain bed material images, and an analysis system to predict the representative grain size of beach sands from the images based on artificial intelligence (AI) analysis. The predicted grain sizes are verified via field samplings. Field bed material samples for the particle size analysis are collected during two seasons, while a drone takes photo images and the exact positions are simultaneously measured at Jangsa beach, Republic of Korea. The learning and testing results of the AI technology are considered satisfactory. Finally, they are used to diagnose the overall stability of Jangsa beach. A beach diagnostic grade is proposed here, which reflects the topography of a beach and the distribution of sediments on the beach. The developed beach diagnostic grade could be used as an indicator of any beach stability on the east coast of the Republic of Korea. When the diagnostic grade changes rapidly at a beach, it is required to undergo thorough investigation to understand the reason and foresee the future of the beach conditions, if we want the beach to function as well as before. Full article
(This article belongs to the Special Issue Recent Experiences and Monitoring in Coastal, Fluvial and Marine Hydrography)
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<p>Location, bathymetry and reference profiles of Jangsa beach, Republic of Korea.</p> Full article ">Figure 2
<p>Particle size analysis and triangular diagram of the size distribution in July 2022 and November, weight ratio by particle size during all periods, and sediment images at the mean sea level, beach face, and dune using a drone.</p> Full article ">Figure 3
<p>Drone images from various altitudes scale correction is needed (modified after Kim et al., 2022) [<a href="#B26-jmse-12-00172" class="html-bibr">26</a>].</p> Full article ">Figure 4
<p>SNN architecture for recognizing the sand diameter from different size images.</p> Full article ">Figure 5
<p>Beach slope extraction for seasonal cross-section measurements using RTK-GNSS.</p> Full article ">Figure 6
<p>Comparison of scenarios on the assessment index as the epoch repeats: value 0 means 100% accuracy.</p> Full article ">Figure 7
<p>Comparison of the observation sediment particle sizes and SNN model predictions.</p> Full article ">Figure 8
<p>Frequency distribution and cumulative curves of the be ach slopes.</p> Full article ">Figure 9
<p>Frequency distribution and cumulative curves of the beach sediment particle sizes.</p> Full article ">Figure 10
<p>July 2022 survey results: (<b>a</b>) beach elevation using drones, (<b>b</b>) beach slope, and (<b>c</b>) mean particle size distribution spatially mapped from the SNN simulation results.</p> Full article ">Figure 11
<p>November 2022 survey results: (<b>a</b>) beach elevation using drones, (<b>b</b>) beach slope, and (<b>c</b>) mean particle size distribution spatially mapped from the SNN simulation results.</p> Full article ">Figure 12
<p>Calculation of the beach diagnostic grade using the grain size-slope relationship and classifying into 5 grades.</p> Full article ">Figure 13
<p>Beach diagnostic grade distribution at Jangsa based on the particle size and slope spatial mapping data.</p> Full article ">Figure 14
<p>Percentage for each grade statistically analyzed from the beach diagnostic grade distribution.</p> Full article ">
22 pages, 8185 KiB  
Article
Morphodynamic Response of Open and Embayed Beaches to Winter Conditions: Two Case Studies from the North Atlantic Iberian Coast
by Ángela Fontán-Bouzas, Tiago Abreu, Caroline C. Ferreira, Paulo A. Silva, Laura López-Olmedilla, José Guitián, Ana M. Bernabeu and Javier Alcántara-Carrió
J. Mar. Sci. Eng. 2024, 12(1), 168; https://doi.org/10.3390/jmse12010168 - 15 Jan 2024
Cited by 1 | Viewed by 1639
Abstract
The morphological responses of two mesotidal beaches located in different coastal settings (embayed and open sandy beaches) on the northwestern Iberian coast were monitored during the winter of 2018/19. The offshore wave time series analysis is related to high-resolution topo-bathymetric measurements to explore [...] Read more.
The morphological responses of two mesotidal beaches located in different coastal settings (embayed and open sandy beaches) on the northwestern Iberian coast were monitored during the winter of 2018/19. The offshore wave time series analysis is related to high-resolution topo-bathymetric measurements to explore spatial-temporal morphological variability at monthly to seasonal scales. Both locations are subjected to the North Atlantic wave climate which exhibits a pronounced seasonality. Throughout the last decade (2010–2020), significant wave heights reached values of up to Hs~9 m during winters and up to Hs~6 m during summers. On average, approximately 12 storms occurred annually in this region. The results clearly reveal divergent morphological responses and sediment transport behaviors at the upper beach and the intertidal zone during the winter for each location. In the embayed beach (Patos), sediment transport in the nearshore is governed by cross-shore processes between the beach berm and a submerged sandbar. In contrast, the open beach (Mira) showed dynamic sediment exchanges and three-dimensional morphologies alternating between accumulation and erosion zones. Overall, both beaches exhibited an erosional trend after the winter, particularly concerning berm erosion and the subaerial beach volume/shoreline retreat. This study highlights the contrasting morphodynamic response on open and embayed beaches to winter conditions, integrating both the subaerial and submerged zones. Local geological and environmental factors, as well as the coastal management strategies applied, will influence how the beach responds to winter wave events. Monitoring and understanding these responses are essential for effective coastal management and adaptation to changing climate. Full article
(This article belongs to the Section Geological Oceanography)
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<p>Geographical setting of the study area: (<b>a</b>) location of the NW Iberian Region, (<b>b</b>) location of the study sites (red dots) and SIMAR wave points (yellow dots) on the NW Iberian Peninsula, (<b>c</b>) Patos Beach, (<b>d</b>) Mira Beach.</p> Full article ">Figure 2
<p>Images showing the RTK_DGPS beach profiles (black dashed lines) and topo bathymetric survey areas (marked in green) in Patos (<b>left</b>) and Mira (<b>right</b>) beaches.</p> Full article ">Figure 3
<p>Winter (diamonds) and summer (circles) wave characteristics for Mira and Patos: (<b>a</b>) mean significant wave height (<b>b</b>) mean peak periods (<b>c</b>) mean wave energy, (<b>d</b>) cumulative wave energy, (<b>e</b>) mean wave energy fluxes and (<b>f</b>) total wave energy fluxes.</p> Full article ">Figure 4
<p>Winter storm characteristics for (<b>a</b>) Patos and (<b>b</b>) Mira: total storm duration (black), number of storms (orange), and number of storm clusters (green).</p> Full article ">Figure 5
<p>Hydrodynamic conditions (Hs, Tp, and θm) at Patos (<b>above</b>) and Mira (<b>below</b>). Red stars represent the surveys, and the orange and green lines mark individual storms and storm clusters, respectively. Orange and blue sections in the top timeline box identifies storm and non-storm conditions, respectively.</p> Full article ">Figure 6
<p>Morphologic beach changes at Patos due to winter wave conditions of 2018/19. The top panels show the pre-winter (September 2018) and post-winter (May 2019) topo-bathymetric maps (DEMs, altitudes above MSL). The bottom-left panel shows the maps of differences in altitude, with positive and negative values for accretion and erosion, respectively. The bottom-right panel displays two subaerial and subtidal cross-shore profiles for the representative site (see location in the bottom-left panel), for the pre-winter (blue line) and post-winter (orange line) morphologies, and the differences in elevation (dashed black line) between them, again with positive and negative values for accretion and erosion, respectively.</p> Full article ">Figure 7
<p>Morphologic beach changes at Mira due to winter wave conditions of 2018/19. The top panels show the Pre-winter (September 2018) and post-winter (May 2019) topo-bathymetric maps (DEMs, altitudes above MSL). The bottom-left panel shows the maps of elevation differences in altitude, with positive and negative values for accretion and erosion, respectively. The bottom-right panel displays two subaerial and subtidal cross-shore profiles for the representative site (see location in the bottom-left panel), with blue, orange, and black dashed lines for the pre-winter, (blue line) and post-winter (orange line) morphologies, and the differences in elevation (dashed black line) between them, again with positive and negative values for accretion and erosion, respectively.</p> Full article ">Figure 8
<p>Temporal evolution of beach profiles of Patos (<b>left panels</b>) and Mira (<b>right panels</b>) throughout winter 2018/19. In the top panels, the dataset includes all topobathymetric and subaerial profiles. The bottom panels detail the subaerial beach, with pre-winter (blue lines), monthly measurements (colors-dashed lines), and post-winter (orange lines).</p> Full article ">Figure 9
<p>Significant wave height versus vertical and horizontal erosion indicators: beach volume (black line), shoreline positions (blue line), beach volume and shoreline changes throughout the winter and Linear trends (dashed black, blue lines) for Patos and Mira.</p> Full article ">
35 pages, 103328 KiB  
Article
Assessment of Shoreline Change from SAR Satellite Imagery in Three Tidally Controlled Coastal Environments
by Salvatore Savastano, Paula Gomes da Silva, Jara Martínez Sánchez, Arnau Garcia Tort, Andres Payo, Mark E. Pattle, Albert Garcia-Mondéjar, Yeray Castillo and Xavier Monteys
J. Mar. Sci. Eng. 2024, 12(1), 163; https://doi.org/10.3390/jmse12010163 - 15 Jan 2024
Cited by 2 | Viewed by 3194
Abstract
Coasts are continually changing and remote sensing from satellites has the potential to both map and monitor coastal change at multiple scales. Unlike optical technology, synthetic aperture radar (SAR) is uninfluenced by darkness, clouds, and rain, potentially offering a higher revision period to [...] Read more.
Coasts are continually changing and remote sensing from satellites has the potential to both map and monitor coastal change at multiple scales. Unlike optical technology, synthetic aperture radar (SAR) is uninfluenced by darkness, clouds, and rain, potentially offering a higher revision period to map shoreline position and change, but this can only be feasible if we have a better interpretation of what shorelines as extracted from SAR imagery represent on the ground. This study aims to assess the application of shorelines extracted from SAR from publicly available satellite imagery to map and capture intra-annual to inter-annual shoreline variability. This is assessed in three tidally controlled coastal study areas that represent sand and gravel beaches with different backshore environments: low-lying dunes and marsh; steep, rocky cliff; and urban environments. We have found that SAR shorelines consistently corresponded to positions above the high-water mark across all three sites. We further discuss the influence of the scene geometry, meteorological and oceanographic conditions, and backshore environment and provide a conceptual interpretation of SAR-derived shorelines. In a low-lying coastal setting, the annual change rate derived through SAR presents a high degree of alignment with the known reference values. The present study contributes to our understanding of the poorly known aspect of using shorelines derived from publicly available SAR satellite missions. It outlines a quantitative approach to automatically assess their quality with a new automatic detection method that is transferable to shoreline evolution assessments worldwide. Full article
(This article belongs to the Section Coastal Engineering)
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<p>Overall methodology used to assess shoreline change from SAR satellite imagery in three macro-tidal coastal environments. SAR-S1 data, combined with auxiliary data, are used to extract SAR-SLs, distances from the reference line (RL), and annual change rates. Interpretation of SAR-SLs is performed for three different locations.</p> Full article ">Figure 2
<p>Location of study sites (<b>a</b>) on a map of Great Britain, Ireland, and Spain: (<b>b</b>) the Bull Island study site in Dublin Bay in Ireland; (<b>c</b>) Salinas beach in the north of Spain; and (<b>d</b>) the Start Bay study site in the south of England, Great Britain. Source of aerial imagery: Esri, Maxar, GeoEye, Earthstar Geographics, CNES/Airbus DS, USDA, USGS, AeroGRID, IGN, and the GIS User Community (v10.6). BGS ©UKRI.</p> Full article ">Figure 3
<p>Available S1 Level-1 Ground Range Detected datasets for Start Bay as of 31 July 2023. In brackets, the number of available images is reported (<b>a</b>); examples of S1 VV (<b>b</b>) and VH (<b>c</b>) acquisition in Start Bay (UK), 10 June 2023. Credit: European Union, contains modified Copernicus Sentinel data 2023, processed with EO Browser.</p> Full article ">Figure 3 Cont.
<p>Available S1 Level-1 Ground Range Detected datasets for Start Bay as of 31 July 2023. In brackets, the number of available images is reported (<b>a</b>); examples of S1 VV (<b>b</b>) and VH (<b>c</b>) acquisition in Start Bay (UK), 10 June 2023. Credit: European Union, contains modified Copernicus Sentinel data 2023, processed with EO Browser.</p> Full article ">Figure 4
<p>SAR processing flowchart used in this study showing the three main consecutive steps of georeferencing, SL extraction, and filtering.</p> Full article ">Figure 5
<p>Shoreline filtering for Salinas beach: (<b>a</b>) heatmap distribution considering all SL points; (<b>b</b>) points following an initial filtering process, revealing a distinct and clear SL pattern; (<b>c</b>) polygon creation; (<b>d</b>) distance between each SL point and the RL (<b>b</b>). Source of aerial imagery: Google, ©2023 Maxar Technologies.</p> Full article ">Figure 6
<p>Illustration of Gaussian mixture distribution (GMD) technique for two examples showing a single distribution (<b>a</b>,<b>b</b>) and a GMD with multiple components (<b>c</b>,<b>d</b>) for the Start Bay study case. Panels (<b>a</b>),(<b>c</b>) show the polygons as light-orange-shaded areas and the SL points after filters. The points after the heatmap and polygon filters are indicated as blue circles, and the the ones after the GMD as pink circles. Panels (<b>b</b>,<b>d</b>) show the histograms of the distances from the reference line distribution for the examples shown in panels a and c, respectively. Source of aerial imagery: Google Satellite Hybrid.</p> Full article ">Figure 7
<p>SAR polygons and beach profiles, indicated by numbers, for Salinas beach. Source of aerial imagery: Esri, World Imagery Metadata.</p> Full article ">Figure 8
<p>Subaerial and inter-tidal beach profile for Salinas beach from in situ surveys (profile No. 6). The horizontal distance is referenced to the baseline in <a href="#jmse-12-00163-f007" class="html-fig">Figure 7</a> (HAT is the high astronomical tide; MHW is the mean high water; MSL is the mean sea level; MLW is the mean low water; and LAT is the low astronomical tide).</p> Full article ">Figure 9
<p>SLs produced for Bull Island including both ASC and DESC tracks (<b>a</b>). Filtering applied to the scene and points selected to produce time series and change rate (<b>b</b>). Source of aerial imagery: Google, ©2023 Maxar Technologies.</p> Full article ">Figure 10
<p>Profile format comparison between DSAS and SAR CRs. Annual SL CRs were calculated for each polygon using the two methodologies described previously. The profiles show very strong agreement between SAR ASC and DESC tracks and strong agreement between DSAS and SAR change rates.</p> Full article ">Figure 11
<p>SLs produced for Salinas including both ASC and DESC tracks (<b>a</b>). Filtering applied to the scene and points selected to produce time series (<b>b</b>). Source of aerial imagery: Google, ©2023 Maxar Technologies.</p> Full article ">Figure 12
<p>Topographic contour lines in Salinas beach for 2022 (MLW = mean low water, MSL = mean sea level and MHW = mean high water). The numbers indicate the beach profiles in the scene. Source of aerial imagery: Esri, World Imagery Metadata.</p> Full article ">Figure 13
<p>Time series of SAR-SL distance from the baseline in Salinas beach.</p> Full article ">Figure 14
<p>SAR-SL distance from the baseline in Salinas beach (<b>top</b> and <b>middle</b> panels); TWL moving average (TWL M.A.; <b>bottom</b> panel).</p> Full article ">Figure 15
<p>Measured beach profile (No. 6) and SAR-SL distance from the baseline in Salinas beach (<b>top</b> panel); heatmaps of concentration of SAR DESC SLs across the beach profile (<b>bottom</b> panel).</p> Full article ">Figure 16
<p>Measured beach profile (No. 6) and CoastSat WL distance from the baseline in Salinas beach (top panel <b>a</b>); heatmaps of concentration of CoastSat WLs across the beach profile (bottom panel <b>a</b>). Measured beach profile (No. 6) and CoastSat SL distance from the baseline in Salinas beach (top panel <b>b</b>); heatmaps of concentration of CoastSat SLs across the beach profile (bottom panel <b>b</b>).</p> Full article ">Figure 17
<p>Astronomical tide elevation at Start Bay study site for the years 2015 to 2021 from the daily high and low tides, and subsets of when ASC and DESC images were taken. Elevations are obtained using POLTIPS software at the Start Point tide gauge station and refer to Ordnance Datum Newlyn. BGS ©UKRI.</p> Full article ">Figure 18
<p>Qualitative assessment of SAR shorelines detecting beach rotation at Blackpool Sands embayment in Start Bay. (<b>a</b>) Shorelines from ascending and descending orbits for the years 2016 and 2018 shown on top of the digital elevation model of difference. (<b>b</b>) Points filtered out from the shorelines obtained from ASC (red points) and DESC (blue points) that are used to calculate the annual recession rate using all data available for the period 2016 to 2021. (<b>c</b>,<b>d</b>) Polygons showing the annual change rate obtained from DESC and ASC filtered points, respectively. Source of aerial imagery: Esri, Maxar, GeoEye, Earthstar Geographics, CNES/Airbus DS, USDA, USGS, AeroGRID, IGN, and the GIS User Community (v10.6). BGS ©UKRI.</p> Full article ">Figure 19
<p>Annual CRs derived from ASC and DESC shorelines at Start Bay shown on top of the 2016–2018 digital elevation model of difference (DoD). Histograms show the annual rate value distribution in three categories (erosive in red, neutral in yellow, and accretive in blue). Neutral changes (yellow bins) are shown as transparent polygons in the maps for clarity. BGS ©UKRI.</p> Full article ">Figure 20
<p>Interpretation of the differences between DESC (<b>a</b>) and ASC (<b>b</b>) SAR-SLs for a beach–cliff/dune system oriented west to east. Total water levels indicated in light blue, and beach surface, in yellow. Blue and red vertical arrows represent the location at which the proposed algorithm will delineate the SAR-SL.</p> Full article ">Figure A1
<p>Wave, tide, and surge time series in Salinas beach.</p> Full article ">Figure A2
<p>Digital terrain models used for validation for Start Bay. For each of the time-stamped DTMs, the elevation is shown hillshaded and color-coded. Source of DTM: ©Teignbridge District Council copyright courtesy of the Southwest Regional Coastal Monitoring Programme (URL). Source of aerial imagery: Esri, Maxar, GeoEye, Earthstar Geographics, CNES/Airbus DS, USDA, USGS, AeroGRID, IGN, and the GIS User Community (v10.6). BGS ©UKRI.</p> Full article ">Figure A3
<p>Total water level (TWL) time series and moving average (M.A.) based on a 24 h sampling window.</p> Full article ">Figure A4
<p>Location of all SLs derived from ASC (<b>a</b>) and DESC (<b>b</b>) orbits at Start Bay study site against aerial imagery. Source of aerial imagery: Esri, Maxar, GeoEye, Earthstar Geographics, CNES/Airbus DS, USDA, USGS, AeroGRID, IGN, and the GIS User Community (v10.6). BGS ©UKRI.</p> Full article ">Figure A5
<p>Location of the ASC and DESC shorelines relative to the ground-truth DTMs used in this analysis. DTMs are shown hillshaded for the years 2016, 2018, and 2020. Source of DTM: © Teignbridge District Council copyright courtesy of the Southwest Regional Coastal Monitoring Programme (URL). BGS ©UKRI.</p> Full article ">Figure A6
<p>SLs in ASC and DESC tracks in two possible scenarios (flat beach (<b>a</b>) and cliffs (<b>e</b>)). (<b>b</b>,<b>f</b>) SLs points from ASC tracks. (<b>c</b>,<b>g</b>) SLs points from DESC tracks. (<b>d</b>,<b>h</b>) Overlapping of SLs points from ASC and DESC tracks.</p> Full article ">Figure A7
<p>Geometric distortions inherent in SAR data due to the side-looking nature of the sensor, particularly in non-flat terrain. SAR detects ground points A, B, C, D, E, F as A’, B’, C’, D’, E’, F’ in the image plane, depending on the incidence angle between the satellite’s line of sight and terrain elevation. (<b>a</b>) <b>Foreshortening</b>: Slope facing the sensor is compressed in the SAR image, appearing as narrow and bright bands. (<b>b</b>) <b>Layover</b>: Mountain tops can overlay the ground ahead of the mountain in the image. (<b>c</b>) <b>Shadow</b>: Slope not facing the sensor results in dark areas in the SAR image, as the sensor cannot capture these back slopes.</p> Full article ">
22 pages, 9457 KiB  
Article
Deep Learning Applications in Vessel Dead Reckoning to Deal with Missing Automatic Identification System Data
by Atefe Sedaghat, Homayoon Arbabkhah, Masood Jafari Kang and Maryam Hamidi
J. Mar. Sci. Eng. 2024, 12(1), 152; https://doi.org/10.3390/jmse12010152 - 12 Jan 2024
Cited by 4 | Viewed by 1432
Abstract
This research introduces an online system for monitoring maritime traffic, aimed at tracking vessels in water routes and predicting their subsequent locations in real time. The proposed framework utilizes an Extract, Transform, and Load (ETL) pipeline to dynamically process AIS data by cleaning, [...] Read more.
This research introduces an online system for monitoring maritime traffic, aimed at tracking vessels in water routes and predicting their subsequent locations in real time. The proposed framework utilizes an Extract, Transform, and Load (ETL) pipeline to dynamically process AIS data by cleaning, compressing, and enhancing it with additional attributes such as online traffic volume, origin/destination, vessel trips, trip direction, and vessel routing. This processed data, enriched with valuable details, serves as an alternative to raw AIS data stored in a centralized database. For user interactions, a user interface is designed to query the database and provide real-time information on a map-based interface. To deal with false or missing AIS records, two methods, dead reckoning and machine learning techniques, are employed to anticipate the trajectory of the vessel in the next time steps. To evaluate each method, several metrics are used, including R squared, mean absolute error, mean offset, and mean offset from the centerline. The functionality of the proposed system is showcased through a case study conducted in the Gulf Intracoastal Waterway (GIWW). Three years of AIS data are collected and processed as a simulated API to transmit AIS records every five minutes. According to our results, the Seq2Seq model exhibits strong performance (0.99 R squared and an average offset of ~1400 ft). However, the second scenario, dead reckoning, proves comparable to the Seq2Seq model as it involves recalculating vessel headings by comparing each data point with the previous one. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence in Maritime Transportation)
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<p>Dashboard of VesselFinder.</p> Full article ">Figure 2
<p>Proposed system (ETL pipeline) diagram.</p> Full article ">Figure 3
<p>Generated segments along waterways in the AoI.</p> Full article ">Figure 4
<p>Segment and vessel vectors.</p> Full article ">Figure 5
<p>Updated Trip number using a time-lagged window.</p> Full article ">Figure 6
<p>ETL pipeline log messages for a couple of hours.</p> Full article ">Figure 7
<p>Dead reckoning.</p> Full article ">Figure 8
<p>The Seq2Seq model structure.</p> Full article ">Figure 9
<p>An example of input and target sequences with different lengths feed into the Seq2Seq model.</p> Full article ">Figure 10
<p>Chronological split.</p> Full article ">Figure 11
<p>Seq2Seq model architecture.</p> Full article ">Figure 12
<p>Mean offset from the centerline.</p> Full article ">Figure 13
<p>Online traffic vessel monitoring at a section at GIWW region.</p> Full article ">Figure 14
<p>Real vs. predicted locations.</p> Full article ">Figure 15
<p>Sensitivity analysis on different lookback and lookahead values.</p> Full article ">Figure 16
<p>Accuracy (<b>a1</b>–<b>d1</b>) and loss (<b>a2</b>–<b>d2</b>) plots for a lookback value of 5 with different lookahead values {3, 5, 7, 10}. The subfigures (<b>a1</b>–<b>d1</b>) depict the accuracy plots, while (<b>a2</b>–<b>d2</b>) represent the corresponding loss plots under varying lookahead conditions.</p> Full article ">Figure 16 Cont.
<p>Accuracy (<b>a1</b>–<b>d1</b>) and loss (<b>a2</b>–<b>d2</b>) plots for a lookback value of 5 with different lookahead values {3, 5, 7, 10}. The subfigures (<b>a1</b>–<b>d1</b>) depict the accuracy plots, while (<b>a2</b>–<b>d2</b>) represent the corresponding loss plots under varying lookahead conditions.</p> Full article ">Figure 17
<p>The boxplot of test months vs. mean offset.</p> Full article ">Figure 18
<p>The boxplot of vessel type vs. mean offset.</p> Full article ">Figure 19
<p>The boxplot of the segments at the Galveston port and the Houston ship channel area vs. mean offset.</p> Full article ">
17 pages, 7887 KiB  
Article
CFD Analysis of Microplastic Transport over the Slopes
by Le Duc Quyen, Young-Gyu Park, In-cheol Lee and Jun Myoung Choi
J. Mar. Sci. Eng. 2024, 12(1), 145; https://doi.org/10.3390/jmse12010145 - 11 Jan 2024
Cited by 1 | Viewed by 1446
Abstract
Microplastics, ubiquitous in our environment, are significantly impacted by the hydrodynamic conditions around them. This study utilizes CFD to explore how various breaker types influence the dispersion and accumulation of microplastics in nearshore areas. A special focus is given to the impact of [...] Read more.
Microplastics, ubiquitous in our environment, are significantly impacted by the hydrodynamic conditions around them. This study utilizes CFD to explore how various breaker types influence the dispersion and accumulation of microplastics in nearshore areas. A special focus is given to the impact of wave dynamics and particle size, particularly on buoyant microplastics in spilling breakers. It was discovered that spilling breakers, common on gently sloping seabeds, encourage broad dispersion of microplastics, notably for smaller-sized particles. Plunging breakers exhibit a similar pattern but with less dispersion and an initial forward movement of neutral and heavy particles. Surging breakers feature minimal dispersion and a distinct oscillatory motion. It has been observed that medium-sized particles with a 1 mm diameter in this work exhibit the most substantial forward movement, likely due to an optimal balance between inertia and viscosity, enabling an effective response to wave momentum. Larger particles, influenced mainly by inertia, tend to show less dispersion and advection. Meanwhile, smaller particles, more affected by viscosity, demonstrate greater dispersion, interacting extensively with wave-induced turbulence. This study reveals the significance of inertia in the behavior of microplastics over slopes, emphasizing the importance of considering inertial effects for precise modeling of microplastic movement in nearshore areas. Full article
(This article belongs to the Section Coastal Engineering)
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<p>Snapshots of waveforms from the simulation for different breaker types: spilling breaker with <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>β</mi> </mrow> <mrow> <mn>1</mn> </mrow> </msub> <mo>=</mo> <mn>2.5</mn> <mo>°</mo> </mrow> </semantics></math> (<b>a</b>), plunging breaker with <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>β</mi> </mrow> <mrow> <mn>2</mn> </mrow> </msub> <mo>=</mo> <mn>10</mn> <mo>°</mo> </mrow> </semantics></math> (<b>b</b>), and surging breaker with <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>β</mi> </mrow> <mrow> <mn>3</mn> </mrow> </msub> <mo>=</mo> <mn>35</mn> <mo>°</mo> </mrow> </semantics></math> (<b>c</b>). Red, blue, green, and black circles indicate four particle release locations indicating ‘offshore’, ‘shoaling’, ‘breaking’, and ‘surfing’, respectively. The four locations are located at x = 2.0, 3.2, 7.0, and 9.0 m (spilling breaker), x = 8.0, 9.7, 10.5, and 11.3 m (plunging breaker), and x = 10, 11, 11.5, and 11.7 m (surging breaker).</p> Full article ">Figure 2
<p>Time-average horizontal velocity (<b>a</b>–<b>c</b>) and time-averaged vertical velocity (<b>d</b>–<b>f</b>). Colored contour indicates positive velocity, and the regions of negative velocity were replaced as nulls. Log-scale was applied only for v-velocity.</p> Full article ">Figure 3
<p>Streamlines from the simulations corresponding to the spilling breaker with <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>β</mi> </mrow> <mrow> <mn>1</mn> </mrow> </msub> <mo>=</mo> <mn>2.5</mn> <mo>°</mo> </mrow> </semantics></math> (<b>a</b>), plunging breaker with <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>β</mi> </mrow> <mrow> <mn>1</mn> </mrow> </msub> <mo>=</mo> <mn>10</mn> <mo>°</mo> </mrow> </semantics></math> (<b>b</b>), and surging breaker with <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>β</mi> </mrow> <mrow> <mn>1</mn> </mrow> </msub> <mo>=</mo> <mn>35</mn> <mo>°</mo> </mrow> </semantics></math> (<b>c</b>).</p> Full article ">Figure 4
<p>Mean horizontal particle trajectory for a spilling breaker originating from four different release locations: ‘surfing’ (<b>a</b>–<b>c</b>), ‘breaking’ (<b>d</b>–<b>f</b>), ‘shoaling’ (<b>g</b>–<b>i</b>), and ‘offshore’ (<b>j</b>–<b>l</b>). The <span class="html-italic">x</span>-axis represents the simulation time, and the <span class="html-italic">y</span>-axis represents the horizontal coordinate, as illustrated in <a href="#jmse-12-00145-f001" class="html-fig">Figure 1</a>. The four initial release locations are marked with black dots on the <span class="html-italic">y</span>-axis in the left panel. The dashed line indicates the shoreline, and the dashed-dot line indicates the position where the slope starts. So, the region between the dashed and dash-dot lines denotes the sloping region. ‘A’ indicates the particle size, and <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>ρ</mi> </mrow> <mrow> <mn>1</mn> </mrow> </msub> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>ρ</mi> </mrow> <mrow> <mn>2</mn> </mrow> </msub> </mrow> </semantics></math>, and <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>ρ</mi> </mrow> <mrow> <mn>3</mn> </mrow> </msub> </mrow> </semantics></math> represent the particle densities of 900, 998.2, and 1100 kg/m<sup>3</sup>, respectively.</p> Full article ">Figure 5
<p>Mean horizontal particle trajectory for a plunging breaker, originating from four different release locations: surfing (<b>a</b>–<b>c</b>), breaking (<b>d</b>–<b>f</b>), shoaling (<b>g</b>–<b>i</b>), and offshore (<b>j</b>–<b>l</b>). The rest of the details are the same as described in <a href="#jmse-12-00145-f004" class="html-fig">Figure 4</a>.</p> Full article ">Figure 6
<p>Mean horizontal particle trajectory for a surging breaker, originating from four different release locations: surfing (<b>a</b>–<b>c</b>), breaking (<b>d</b>–<b>f</b>), shoaling (<b>g</b>–<b>i</b>), and offshore (<b>j</b>–<b>l</b>). The rest of the details are the same as described in <a href="#jmse-12-00145-f004" class="html-fig">Figure 4</a>.</p> Full article ">Figure 7
<p>Momentum fluxes <span class="html-italic">S<sub>xx</sub></span>, <span class="html-italic">S<sub>xy</sub></span>, and <span class="html-italic">S<sub>yy</sub></span> for spilling (<b>a</b>–<b>c</b>), plunging (<b>d</b>–<b>f</b>), and surging (<b>g</b>–<b>i</b>) breakers. x′ and y′ are dimensionless distances. x′ = 0 indicates the ‘shoaling’ location, and x′ = 1 indicates the intersection between slope and mean water level. y′ = 0 and y′ = 1 indicate the bottom and still water level (h = 0.4 m). Negative values were replaced with nulls. All contours were log-scaled.</p> Full article ">Figure 8
<p>Time series of standard deviation (STD), skewness, and kurtosis of buoyant particles (<math display="inline"><semantics> <mrow> <mi>ρ</mi> </mrow> </semantics></math> = 900 kg/m<sup>3</sup>) distribution for spilling (<b>a</b>–<b>c</b>), plunging (<b>d</b>–<b>f</b>), and surging (<b>g</b>–<b>i</b>) breakers during the simulation time. <span class="html-italic">x</span>-axis indicates the nondimensional mean location of particles released from the ‘shoaling’ location. x′ = 0 indicates the ‘shoaling’ location, and x′ = 1 indicates the intersection between the slope and still water level. The dashed lines for skewness and kurtosis indicate y = 0 and y = 3, respectively. STD and kurtosis are scaled as log scales.</p> Full article ">Figure 9
<p>Lagrangian velocity of a buoyant particle (represented by red lines) and Eulerian velocity at the particle’s location (represented by black lines) for particles of sizes 0.2 mm (<b>a</b>,<b>d</b>), 1 mm (<b>b</b>,<b>e</b>), and 5 mm (<b>c</b>,<b>f</b>) in the spilling breaker. The upper plots show the horizontal velocity, while the lower plots depict the vertical velocity component. The particle was released from the ‘offshore’ location.</p> Full article ">Figure 10
<p>Differences in kinetic energy spectrum between Lagrangian velocity and Eulerian velocity for different particle sizes (A) of 0.2 mm (<b>a</b>), 1 mm (<b>b</b>), and 5 mm (<b>c</b>). <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>u</mi> </mrow> <mrow> <mi>L</mi> </mrow> </msub> <mo>−</mo> <msub> <mrow> <mi>u</mi> </mrow> <mrow> <mi>E</mi> </mrow> </msub> </mrow> </semantics></math> indicates the difference between Lagrangian and Eulerian horizontal velocity spectra, and <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>v</mi> </mrow> <mrow> <mi>L</mi> </mrow> </msub> <mo>−</mo> <msub> <mrow> <mi>v</mi> </mrow> <mrow> <mi>E</mi> </mrow> </msub> </mrow> </semantics></math> indicates the difference between Lagrangian and Eulerian vertical velocity spectra. The Lagrangian velocity was measured by the particle released from the ‘offshore’ location, and the Eulerian velocity was measured at the location of the Lagrangian particle.</p> Full article ">Figure 11
<p>Advection and dispersion of buoyant particles in the spilling breaker: (<b>a</b>) mean location of particles (<math display="inline"><semantics> <mrow> <mover accent="true"> <mrow> <msub> <mrow> <mi>x</mi> </mrow> <mrow> <mi>p</mi> </mrow> </msub> </mrow> <mo>¯</mo> </mover> </mrow> </semantics></math>), (<b>b</b>) STD of particle locations (STD(<math display="inline"><semantics> <mrow> <msub> <mrow> <mi>x</mi> </mrow> <mrow> <mi>p</mi> </mrow> </msub> </mrow> </semantics></math>)), where <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>x</mi> </mrow> <mrow> <mi>p</mi> </mrow> </msub> </mrow> </semantics></math> is particle locations in the x-direction.</p> Full article ">
35 pages, 7412 KiB  
Review
Unlocking the Ocean 6G: A Review of Path-Planning Techniques for Maritime Data Harvesting Assisted by Autonomous Marine Vehicles
by Liang Zhao and Yong Bai
J. Mar. Sci. Eng. 2024, 12(1), 126; https://doi.org/10.3390/jmse12010126 - 8 Jan 2024
Cited by 16 | Viewed by 1872
Abstract
Seamless integration of both terrestrial and non-terrestrial networks is crucial to providing full-dimensional wireless and ubiquitous coverage, particularly catering to those engaged in marine activities. Compared to terrestrial networks, wireless communications in the marine domain are still not satisfactory for ubiquitous connectivity. Featuring [...] Read more.
Seamless integration of both terrestrial and non-terrestrial networks is crucial to providing full-dimensional wireless and ubiquitous coverage, particularly catering to those engaged in marine activities. Compared to terrestrial networks, wireless communications in the marine domain are still not satisfactory for ubiquitous connectivity. Featuring agile maneuverability and strong adaptive capability, autonomous marine vehicles (AMVs) play a pivotal role in enhancing communication coverage by relaying or collecting data. However, path planning for maritime data harvesting is one of the most critical issues to enhance transmission efficiency while ensuring safe sailing for AMVs; yet it has rarely been discussed under this context. This paper provides a comprehensive and holistic overview of path-planning techniques custom-tailored for the purpose of maritime data collection. Specifically, we commence with a general portrayal of fundamental models, including system architectures, problem formulations, objective functions, and associated constraints. Subsequently, we summarize the various algorithms, methodologies, platforms, tools, coding environments, and their practical implementations for addressing these models. Furthermore, we delve into the burgeoning applications of path planning in the realm of maritime data harvesting and illuminate potential avenues for upcoming research endeavors. We believe that future research may focus on developing techniques to adapt more intricate and uncertain scenarios, such as sensor failures, inaccurate state estimations, complete modeling of communication channels, ocean dynamics, and application of heterogeneous systems. Full article
(This article belongs to the Section Ocean Engineering)
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<p>Mission types for AMV-assisted maritime applications.</p> Full article ">Figure 2
<p>Research questions and overview.</p> Full article ">Figure 3
<p>Distributed network.</p> Full article ">Figure 4
<p>Data collection with fixed path.</p> Full article ">Figure 5
<p>Clustering network.</p> Full article ">Figure 6
<p>Subject categories and keyword cloud.</p> Full article ">Figure 7
<p>Summary of the articles by publication year and titles. Since there are too many journals/conferences involved, we grouped them into a small number of categories.</p> Full article ">Figure 8
<p>Distribution of research groups around the world.</p> Full article ">Figure 9
<p>Summary of the path-planning techniques (based on the articles searched in <a href="#sec2-jmse-12-00126" class="html-sec">Section 2</a>, we eliminated some literature including the review papers, papers with irrelevant contents, and papers with poor/vague presentation).</p> Full article ">Figure 10
<p>Reinforcement learning in AMV-assisted path planning.</p> Full article ">Figure 11
<p>The lawn mower path depicted in [<a href="#B24-jmse-12-00126" class="html-bibr">24</a>], which resembles the approximate cellular decomposition in coverage planning.</p> Full article ">Figure 12
<p>The greedy search procedures depicted in [<a href="#B83-jmse-12-00126" class="html-bibr">83</a>].</p> Full article ">Figure 13
<p>UAV-USV system.</p> Full article ">Figure 14
<p>(<b>a</b>) UAV-USV system depicted in [<a href="#B146-jmse-12-00126" class="html-bibr">146</a>], where USV acted as an energy station; (<b>b</b>) USV-AUV system designed by [<a href="#B147-jmse-12-00126" class="html-bibr">147</a>], where USV acted as an energy station; (<b>c</b>) AUV is docking with the USV [<a href="#B148-jmse-12-00126" class="html-bibr">148</a>].</p> Full article ">Figure 15
<p>Research questions in this review and short answers.</p> Full article ">
33 pages, 2636 KiB  
Review
Developments in Modeling Techniques for Reliability Design of Aquaculture Cages: A Review
by Chien Ming Wang, Mingyuan Ma, Yunil Chu, Dong-Sheng Jeng and Hong Zhang
J. Mar. Sci. Eng. 2024, 12(1), 103; https://doi.org/10.3390/jmse12010103 - 4 Jan 2024
Cited by 2 | Viewed by 1790
Abstract
Offshore aquaculture is gaining traction due to space limitations in nearshore waters, more pristine water, cooler temperatures, and better waste dispersal. This move has spurred the development of new technologies for offshore aquaculture. Despite the numerous analysis methods for designing aquaculture infrastructure, limitations [...] Read more.
Offshore aquaculture is gaining traction due to space limitations in nearshore waters, more pristine water, cooler temperatures, and better waste dispersal. This move has spurred the development of new technologies for offshore aquaculture. Despite the numerous analysis methods for designing aquaculture infrastructure, limitations and challenges remain in modeling the influence of fish cages on flow fields and in addressing fluid–structure interaction. This paper presents a comprehensive review of analysis methods and modeling techniques applied in the design of offshore aquaculture systems, emphasizing the structural reliability analysis. This review includes statistical and predictive analysis of extreme sea conditions, evaluation of environmental loads and hydrodynamic analysis, structural reliability modeling and assessment, and seabed geotechnical responses to mooring anchors. For each design consideration, the relevant theories and applicability are elaborated upon and discussed. This review provides valuable insights for engineers involved in the development and design of offshore aquaculture infrastructure. Full article
(This article belongs to the Section Ocean Engineering)
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<p>Illustration of a typical aquaculture system: (<b>a</b>) structural components and (<b>b</b>) design considerations.</p> Full article ">Figure 2
<p>(<b>a</b>) Illustration of <span class="html-italic">H<sub>s</sub></span>–<span class="html-italic">T<sub>z</sub></span> environmental contours; (<b>b</b>) design conditions.</p> Full article ">Figure 3
<p>Diagram of wind load evaluations on exposed surface for a floating cage: (<b>a</b>) top view for orientation of structure exposed above water and (<b>b</b>) isometric view for area of exposed structure.</p> Full article ">Figure 4
<p>Configuration of a typical net structure.</p> Full article ">Figure 5
<p>Illustration of two empirical methods to evaluate hydrodynamic forces on a fish cage net: (<b>a</b>) Morison equation and (<b>b</b>) screen-type method, wherein the shaded area surrounded by dotted line represents a super element.</p> Full article ">Figure 6
<p>Illustrations of three methods to model fish cage net: (<b>a</b>) lumped-mass method, (<b>b</b>) truss-element method, and (<b>c</b>) shell-membrane method, wherein <span class="html-italic">N<sub>z</sub></span>, <span class="html-italic">N<sub>θ</sub></span>, and <span class="html-italic">N<sub>zθ</sub></span> are membrane stress resultants lying in tangential planes of shell element, and Δ<span class="html-italic">p</span> is the pressure drop normal to the shell-membrane [<a href="#B153-jmse-12-00103" class="html-bibr">153</a>].</p> Full article ">
34 pages, 6849 KiB  
Article
Assessing the Efficacy of a Bouchot-Style Shellfish Reef as a Restoration Option in a Temperate Estuary
by Charles Maus, Alan Cottingham, Andrew Bossie and James R. Tweedley
J. Mar. Sci. Eng. 2024, 12(1), 87; https://doi.org/10.3390/jmse12010087 - 1 Jan 2024
Cited by 1 | Viewed by 1592
Abstract
Shellfish reefs around the world have become degraded, and recent efforts have focused on restoring these valuable habitats. This study is the first to assess the efficacy of a bouchot-style reef, where mussels were seeded onto wooden stakes and deployed in a hypereutrophic [...] Read more.
Shellfish reefs around the world have become degraded, and recent efforts have focused on restoring these valuable habitats. This study is the first to assess the efficacy of a bouchot-style reef, where mussels were seeded onto wooden stakes and deployed in a hypereutrophic estuary in Australia. While >60% of translocated mussels survived one month, after ten months, only 2% remained alive, with this mortality being accompanied, at least initially, by declining body condition. Mussel survival, growth, body condition and recruitment were greater on the top section of the stake, implying that the distance from the substrate was important. More fish species inhabited the reefs (31) than unstructured control sites (17). Reefs were also colonised by a range of invertebrate species, including 11 native and six non-indigenous species. However, the number of individuals declined from 4495 individuals from 14 species in December 2019 to 35 individuals representing 4 species in March 2021, likely due to hypoxic bottom water conditions following unseasonal rainfall. Although the bouchot-style reefs were unable to sustain mussels and other invertebrates over sequential years, this approach has the potential to be successful if deployed in shallow water or intertidal zones, which are largely exempt from biotic and abiotic stressors characteristic of deeper waters in microtidal estuaries. Full article
(This article belongs to the Section Marine Ecology)
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<p>(<b>a</b>) Map of Swan-Canning Estuary showing three locations of the bouchot-style <span class="html-italic">Mytilus galloprovincialis</span> reefs (filled rectangles) constructed in 2019, the RUV control sites (open rectangles) and the water quality monitoring site (open circle) and (<b>b</b>) photograph showing one of the reefs several months after deployment.</p> Full article ">Figure 2
<p>Long-term average (line) and total monthly (bars) (<b>a</b>) rainfall (recorded at Perth Airport) [<a href="#B30-jmse-12-00087" class="html-bibr">30</a>] and (<b>b</b>) freshwater discharge into the Swan River (recorded at Walyunga) between 2019 and 2023 [<a href="#B41-jmse-12-00087" class="html-bibr">41</a>]. Weekly surface (0.5 m depth) and bottom (6.0 m depth) values for (<b>c</b>,<b>d</b>) water temperatures (<b>e</b>,<b>f</b>) salinities and (<b>g</b>,<b>h</b>) dissolved oxygen (DO) concentrations in the Swan-Canning Estuary basin (recorded at Heathcote) [<a href="#B41-jmse-12-00087" class="html-bibr">41</a>]. Black and white bars on <span class="html-italic">x</span>-axis refer to seasons.</p> Full article ">Figure 3
<p>Monthly shell length–frequency histograms (%) of 5 mm-length classes of translocated <span class="html-italic">Mytilus galloprovincialis</span> (and recruits) attached to the bottom, middle and top sections of wooden stakes used to create three bouchot-style reefs in the Swan-Canning Estuary. <span class="html-italic">n</span> = number of mussels sampled from three stakes from each reef in each month. Dashed vertical lines show shell length of 60 mm for reference.</p> Full article ">Figure 4
<p>Mean monthly (and 95% CI) shell lengths of translocated <span class="html-italic">Mytilus galloprovincialis</span> for each stake section (B = bottom, M = middle, T = top) at the three constructed bouchot-style reefs (<b>a</b>–<b>c</b>) in the Swan-Canning Estuary between April 2019 and February 2020. Black and white bars on <span class="html-italic">x</span>-axis refer to seasons.</p> Full article ">Figure 5
<p>Mean monthly (and 95% CI) mussel dry mass of translocated <span class="html-italic">M. galloprovincialis</span> (standardised for mean shell length of 61 mm) for each stake section (B = bottom, M = middle, T = top) at three bouchot-style reefs (<b>a</b>–<b>c</b>) in the Swan-Canning Estuary between April 2019 and February 2020. Black and white bars on <span class="html-italic">x</span>-axis refer to seasons.</p> Full article ">Figure 6
<p>Average number of translocated <span class="html-italic">Mytilus galloprovincialis</span> (ind. 300 mm<sup>−1</sup>) on each stake section (bottom, middle and top) at each of the three bouchot-style reefs (<b>a</b>–<b>c</b>) in the Swan-Canning Estuary in each month between April 2019, when they were seeded, and February 2020. Black and white bars on <span class="html-italic">x</span>-axis refer to seasons.</p> Full article ">Figure 7
<p>Two-dimensional mMDS ordination plot constructed from bootstrap averages for (<b>a</b>) Habitat and (<b>b</b>) Time, calculated from a Bray–Curtis resemblance matrix of the MaxN of each fish species in each replicate sample collected before the bouchot-style reefs were deployed in the Swan-Canning Estuary. Group averages (larger symbols) and approximate 95% region estimates fitted to the bootstrap averages are provided.</p> Full article ">Figure 8
<p>(<b>a</b>) Canonical analysis of principal coordinate plot illustrating differences in the fish composition on the bouchot-style reefs and control sites before and after reef deployment. (<b>b</b>) Two-dimensional mMDS ordination plot constructed from bootstrap averages for Habitat, calculated from a Bray–Curtis resemblance matrix of the MaxN of each fish species in each replicate sample collected after the bouchot-style reefs were deployed in the Swan-Canning Estuary. Group averages (larger symbols) and approximate 95% region estimates fitted to the bootstrap averages are provided. (<b>c</b>) Centroid nMDS ordination plot derived from a distance among centroid matrix of the MaxN of each fish species for each sampling occasion (reefs and control combined). Arrows denote direction of cycling.</p> Full article ">Figure 9
<p>(<b>a</b>) Number of NIS (non-indigenous species), native species and <span class="html-italic">Mytilus galloprovincialis</span> and (<b>b</b>) their abundance collected from wooden stakes used to create bouchot-style reefs in the Swan-Canning Estuary over time.</p> Full article ">Figure 10
<p>mMDS ordination plot constructed from bootstrap averages for (<b>a</b>) Time and (<b>b</b>) Site, calculated from a Bray–Curtis resemblance matrix of the MaxN of each invertebrate species in each replicate sample collected after the bouchot-style reefs were deployed in the Swan-Canning Estuary. Group averages (larger symbols) and approximate 95% region estimates fitted to the bootstrap averages are provided. (<b>c</b>) Centroid nMDS ordination plot derived from a distance among centroid matrix of the abundance of each invertebrate species for each sampling occasion. Arrows denote direction of cycling. (<b>d</b>) Canonical analysis of principal coordinates plot illustrating differences in the invertebrate composition on the bouchot-style reefs over time.</p> Full article ">Figure A1
<p>Mean (±95% confidence limits) number of species and total Max among habitats and dates before and after the deployment of the bouchot-style reefs in the Swan-Canning Estuary.</p> Full article ">Figure A2
<p>Shade plot constructed using transformed MaxN of each fish species recorded at the sites where the bouchot-style reefs would be deployed and the control sites over time.</p> Full article ">Figure A3
<p>Shade plot constructed using transformed MaxN of each fish species recorded on (<b>a</b>) the bouchot-style reefs and control sites and (<b>b</b>) among both habitats over time.</p> Full article ">Figure A4
<p>Shade plot constructed using transformed abundances of each invertebrate species recorded on each of the three bouchot-style reefs over time.</p> Full article ">
30 pages, 4130 KiB  
Review
Ensemble Neural Networks for the Development of Storm Surge Flood Modeling: A Comprehensive Review
by Saeid Khaksari Nezhad, Mohammad Barooni, Deniz Velioglu Sogut and Robert J. Weaver
J. Mar. Sci. Eng. 2023, 11(11), 2154; https://doi.org/10.3390/jmse11112154 - 11 Nov 2023
Cited by 3 | Viewed by 2609
Abstract
This review paper focuses on the use of ensemble neural networks (ENN) in the development of storm surge flood models. Storm surges are a major concern in coastal regions, and accurate flood modeling is essential for effective disaster management. Neural network (NN) ensembles [...] Read more.
This review paper focuses on the use of ensemble neural networks (ENN) in the development of storm surge flood models. Storm surges are a major concern in coastal regions, and accurate flood modeling is essential for effective disaster management. Neural network (NN) ensembles have shown great potential in improving the accuracy and reliability of such models. This paper presents an overview of the latest research on the application of NNs in storm surge flood modeling and covers the principles and concepts of ENNs, various ensemble architectures, the main challenges associated with NN ensemble algorithms, and their potential benefits in improving flood forecasting accuracy. The main part of this paper pertains to the techniques used to combine a mixed set of predictions from multiple NN models. The combination of these models can lead to improved accuracy, robustness, and generalization performance compared to using a single model. However, generating neural network ensembles also requires careful consideration of the trade-offs between model diversity, model complexity, and computational resources. The ensemble must balance these factors to achieve the best performance. The insights presented in this review paper are particularly relevant for researchers and practitioners working in coastal regions where accurate storm surge flood modeling is critical. Full article
(This article belongs to the Special Issue Coastal Disaster Assessment and Response)
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<p>(<b>a</b>) Best track positions and storm surge predictions from the empirical CHRPS model compared to water level observations from select NOAA tide gauge and storm surge predictions from operational ADCIRC simulations performed at CHL [<a href="#B39-jmse-11-02154" class="html-bibr">39</a>]. (<b>b</b>) Winds. (<b>c</b>) Hourly heights. (<b>d</b>) Barometric pressure. (<b>e</b>) Air temperature. (<b>f</b>) Sea surface temperature in Aransas Wildlife Refuge station, TX, for Hurricane Harvey (August 2017).</p> Full article ">Figure 1 Cont.
<p>(<b>a</b>) Best track positions and storm surge predictions from the empirical CHRPS model compared to water level observations from select NOAA tide gauge and storm surge predictions from operational ADCIRC simulations performed at CHL [<a href="#B39-jmse-11-02154" class="html-bibr">39</a>]. (<b>b</b>) Winds. (<b>c</b>) Hourly heights. (<b>d</b>) Barometric pressure. (<b>e</b>) Air temperature. (<b>f</b>) Sea surface temperature in Aransas Wildlife Refuge station, TX, for Hurricane Harvey (August 2017).</p> Full article ">Figure 2
<p>Flow diagram of transfer learning in NN, including the reuse of a pre-trained model on a new problem.</p> Full article ">Figure 3
<p>Flow diagram of transfer learning in NN involving the reuse of a pre-trained model on a new problem.</p> Full article ">Figure 4
<p>A general scheme of the bagging ensemble approach.</p> Full article ">Figure 5
<p>A simplified pseudo-code of an ensemble learning algorithm for bagging.</p> Full article ">Figure 6
<p>A general schematic of the boosting ensemble approach.</p> Full article ">Figure 7
<p>A simplified pseudo-code of an ensemble learning algorithm for boosting.</p> Full article ">Figure 8
<p>A general scheme of the stacking ensemble approach.</p> Full article ">Figure 9
<p>A simplified pseudo-code of ensemble learning algorithm for stacking.</p> Full article ">Figure 10
<p>Qualitative assessment of studies numbered 1 to 6 from <a href="#jmse-11-02154-t002" class="html-table">Table 2</a>.</p> Full article ">Figure 11
<p>General process of pruning and fine-tuning in a neural network ensemble.</p> Full article ">Figure A1
<p>Simplified BP algorithm in a 1-layer NN with 2D input.</p> Full article ">
27 pages, 11397 KiB  
Article
The SAVEMEDCOASTS-2 webGIS: The Online Platform for Relative Sea Level Rise and Storm Surge Scenarios up to 2100 for the Mediterranean Coasts
by Antonio Falciano, Marco Anzidei, Michele Greco, Maria Lucia Trivigno, Antonio Vecchio, Charalampos Georgiadis, Petros Patias, Michele Crosetto, Josè Navarro, Enrico Serpelloni, Cristiano Tolomei, Giovanni Martino, Giuseppe Mancino, Francesco Arbia, Christian Bignami and Fawzi Doumaz
J. Mar. Sci. Eng. 2023, 11(11), 2071; https://doi.org/10.3390/jmse11112071 - 30 Oct 2023
Cited by 4 | Viewed by 2604
Abstract
Here we show the SAVEMEDCOASTS-2 web-based geographic information system (webGIS) that supports land planners and decision makers in considering the ongoing impacts of Relative Sea Level Rise (RSLR) when formulating and prioritizing climate-resilient adaptive pathways for the Mediterranean coasts. The webGIS was developed [...] Read more.
Here we show the SAVEMEDCOASTS-2 web-based geographic information system (webGIS) that supports land planners and decision makers in considering the ongoing impacts of Relative Sea Level Rise (RSLR) when formulating and prioritizing climate-resilient adaptive pathways for the Mediterranean coasts. The webGIS was developed within the framework of the SAVEMEDCOASTS and SAVEMEDCOASTS-2 projects, funded by the European Union, which respond to the need to protect people and assets from natural disasters along the Mediterranean coasts that are vulnerable to the combined effects of Sea Level Rise (SLR) and Vertical Land Movements (VLM). The geospatial data include available or new high-resolution Digital Terrain Models (DTM), bathymetric data, rates of VLM, and multi-temporal coastal flooding scenarios for 2030, 2050, and 2100 with respect to 2021, as a consequence of RSLR. The scenarios are derived from the 5th Assessment Report (AR5) provided by the Intergovernmental Panel on Climate Change (IPCC) and encompass different Representative Concentration Pathways (RCP2.6 and RCP8.5) for climate projections. The webGIS reports RSLR scenarios that incorporate the temporary contribution of both the highest astronomical tides (HAT) and storm surges (SS), which intensify risks to the coastal infrastructure, local community, and environment. Full article
(This article belongs to the Special Issue Sea Level Rise and Related Hazards Assessment)
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<p>The SAVEMEDCOASTS-2 and SAVEMEDCOASTS case studies: (<b>a</b>) Ebro Delta (Spain); (<b>b</b>) Rhone Delta (France); (<b>c</b>) Venice Lagoon (Italy); (<b>d</b>) Metaponto Plain (Italy); (<b>e</b>) Chalastra Plain (Greece); (<b>f</b>) Cinque Terre (Italy); (<b>g</b>) Lipari Island (Italy); (<b>h</b>) Lefkada Island (Greece). Background layer: “Sentinel-2 cloudless—<a href="https://s2maps.eu" target="_blank">https://s2maps.eu</a> (accessed on 7 August 2023) by EOX IT Services GmbH (Contains modified Copernicus Sentinel data 2020)”.</p> Full article ">Figure 2
<p>Workflow of data analysis and results for the SAVEMEDCOASTS-2 webGIS geodatabase.</p> Full article ">Figure 3
<p>The City of Venice (Italy). RSLR scenarios in 2030, 2050, and 2100 for the RCP8.5 climatic projections from the regional IPCC AR5 Report, integrated with the contribution of the mean VLM rate derived from the combined InSAR-GNSS analysis. Background layer: Ortofoto 2014. Città Metropolitana di Venezia. Reference topography: LiDAR DTM ex Consorzio Venezia Nuova.</p> Full article ">Figure 4
<p>The Ebro Delta (Spain). Potential coastal flooding scenarios in 2021, 2030, 2050, and 2100 for: (<b>a</b>) RCP2.6 RSLR and 1 yr storm surge (SS); (<b>b</b>) RCP2.6 RSLR and 100 yrs SS; (<b>c</b>) RCP8.5 RSLR and 1 yr SS; (<b>d</b>) RCP8.5 RSLR and 100 yrs SS. Flooding areas are shown in pale yellow in 2021, in a blue color palette for RCP2.6, and a yellow–red color palette for RCP8.5 up to 2100, respectively. Background layer: Delta de l’Ebre Ortofoto febrer 2020, Institut Cartogràfic i Geològic de Catalunya (ICGC). Reference topography: Modelo digital del terreno 2a Cobertura (2015-Actualidad), Instituto Geográfico Nacional (IGN).</p> Full article ">Figure 5
<p>Monterosso (Italy). Results of the “Storm surge scenarios” app for the RCP8.5 climate scenario, storm surge with RT = 100 yrs, and time horizon 2100.</p> Full article ">Figure 6
<p>Metaponto (Italy). Results of the “Storm surge scenarios” app for the RCP8.5 climate scenario, storm surge with RT = 100 yrs, and time horizon 2100.</p> Full article ">Figure 7
<p>Cavallino Treporti (Italy). Results of the “Comparison between scenarios” app for RCP2.6 climate scenario, storm surge with RT = 100 yrs, and time horizon 2100.</p> Full article ">Figure 8
<p>The Ebro Delta (Spain). An example of using the “Flood risk indicators” app for the RCP2.6 IPCC scenario, a storm surge with RT = 100 yrs, and rice fields as a flood risk indicator.</p> Full article ">
20 pages, 6891 KiB  
Article
Experimental Study on Prediction for Combustion Optimal Control of Oil-Fired Boilers of Ships Using Color Space Image Feature Analysis and Support Vector Machine
by Chang-Min Lee, Byung-Gun Jung and Jae-Hyuk Choi
J. Mar. Sci. Eng. 2023, 11(10), 1993; https://doi.org/10.3390/jmse11101993 - 16 Oct 2023
Cited by 4 | Viewed by 1647
Abstract
The International Maritime Organization strives to improve the atmospheric environment in oceans and ports by regulating ship emissions of air pollutants and promoting energy efficiency. This study deals with the prediction of eco-friendly combustion in boilers to reduce air pollution emissions. Accurately measuring [...] Read more.
The International Maritime Organization strives to improve the atmospheric environment in oceans and ports by regulating ship emissions of air pollutants and promoting energy efficiency. This study deals with the prediction of eco-friendly combustion in boilers to reduce air pollution emissions. Accurately measuring air pollutants from ship boilers in real-time is crucial for optimizing boiler combustion. However, using data obtained through an exhaust gas analyzer for real-time control is challenging due to combustion process delays. Therefore, a real-time predictive modeling approach is proposed to enhance the accuracy of prediction models for NOx, SO2, CO2, and O2 by analyzing the color spectrum of flame images in a quasi-instantaneous combustion state. Experimental investigations were carried out on an oil-fired boiler installed on an actual ship, where the air damper was adjusted to create various combustion conditions. This algorithm is a saturation-based feature extraction filter (SEF) through color spectrum analysis using RGB (red, green, and blue) and HSV (hue, saturation, and value). The prediction model applying the proposed method was verified against exhaust gas analyzer data using a new data set, and real-time prediction performance and generalization were confirmed. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence and Automatic Control in Marine and Maritime Research)
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<p>Schematic of image-based regression model training. (<b>A</b>) Exhaust gas measurement probe (<b>B</b>) CMOS webcam (<b>C</b>) Exhaust gas analyzer and data acquisition device.</p> Full article ">Figure 2
<p>Combustion process of oil-fired boilers.</p> Full article ">Figure 3
<p>Real-time data acquisition process in MATLAB Simulink.</p> Full article ">Figure 4
<p>OFB emission data according to different linkage positions.</p> Full article ">Figure 5
<p>Flame image pre-processing.</p> Full article ">Figure 6
<p>Representative color spaces. (<b>A</b>) RGB, (<b>B</b>) YCbCr, and (<b>C</b>) HSV.</p> Full article ">Figure 7
<p>Histograms by color spaces for various combustion conditions.</p> Full article ">Figure 8
<p>Spectral variation of saturation for various combustion condition.</p> Full article ">Figure 9
<p>Distribution plot of saturation histogram for the entire flame image dataset.</p> Full article ">Figure 10
<p>Schematic of data formation for the proposed SEF.</p> Full article ">Figure 11
<p>Predicted results based on SVM. (<b>A</b>) RGB_origin (<b>B</b>) Histo_RGB.</p> Full article ">Figure 12
<p>Predicted results based on SVM. (<b>A</b>) Histo _HSV (<b>B</b>) SEF.</p> Full article ">Figure 13
<p>Real and predicted values of EGC with further environment.</p> Full article ">
15 pages, 13570 KiB  
Article
Assessment of Wave Power Density Using Sea State Climate Change Initiative Database in the French Façade
by Sonia Ponce de León, Marco Restano and Jérôme Benveniste
J. Mar. Sci. Eng. 2023, 11(10), 1970; https://doi.org/10.3390/jmse11101970 - 11 Oct 2023
Cited by 2 | Viewed by 2054
Abstract
This study considers assessing the wave energy potential in the French façade. The objective is to investigate the validity of satellite altimetry-based estimates of wave renewable energy potential using the homogenized multi-mission altimeter data made available by the European Space Agency Sea State [...] Read more.
This study considers assessing the wave energy potential in the French façade. The objective is to investigate the validity of satellite altimetry-based estimates of wave renewable energy potential using the homogenized multi-mission altimeter data made available by the European Space Agency Sea State Climate Change Initiative (Sea_State_cci). The empirical model of Gommenginger et al. (2003) is adopted to calculate the wave period, which is required to estimate the wave power density from both the radar altimeter’s significant wave height and backscatter coefficient. The study comprises 26 years of data, from January 1992 to December 2018. In the winter season, the wave resource is abundant and higher than in other seasons. On average, the highest value is about 99,000 W/m offshore. In the coastal zone, the wave power density is also relatively high, with values of about 60,000 W/m in the North and South regions of the French Atlantic coast. The seasonal spatial distribution of the wave power density is presented to identify potential sites of interest for the development of the marine renewable energy sector and to make renewable energy supply more resilient. The analysis reveals large inter-annual and interseasonal variability in the wave resource in the French façade in the past 26 years. The study shows the feasibility of satellite altimetry-based assessments of wave renewable energy potential as a promising and powerful tool. Full article
(This article belongs to the Section Physical Oceanography)
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<p>Study area. Bathymetry (colormap, in meters), wave buoys (red circles), and locations where the wave power density is estimated (1–9, blue stars).</p> Full article ">Figure 2
<p>Scatter plots for the significant wave height (Hs) (<b>a</b>) and the wave power density (<b>b</b>) for coastal buoy 64.</p> Full article ">Figure 3
<p>Mean wave power density map (W/m) for 1992–2018.</p> Full article ">Figure 4
<p>Along-track mean wave power density map (kW/m) for 1992–2018.</p> Full article ">Figure 5
<p>Mean wave power density map (W/m) for 1992–2018. Zoom of the coastal section.</p> Full article ">Figure 6
<p>Seasonal distribution of mean wave power density (W/m) for 1992–2018. Summer (<b>a</b>), autumn (<b>b</b>), winter (<b>c</b>), and spring (<b>d</b>).</p> Full article ">Figure 7
<p>Monthly mean wave power density (W/m) maps over 26 years (1992–2018).</p> Full article ">Figure 8
<p>Monthly wave power density charts over 26 years (1992–2018). January (<b>a</b>), February (<b>b</b>), March (<b>c</b>), April (<b>d</b>), May (<b>e</b>), June (<b>f</b>), July (<b>g</b>), August (<b>h</b>), September (<b>i</b>), October (<b>j</b>), November (<b>k</b>), December (<b>l</b>).</p> Full article ">Figure 9
<p>Local correlation between wind and waves estimated from Sea State_cci data at the nine chosen locations (<a href="#jmse-11-01970-f001" class="html-fig">Figure 1</a>) for 1992–2018.</p> Full article ">Figure 10
<p>Seasonal variability indices estimated from the Sea State_cci data at the nine chosen locations (<a href="#jmse-11-01970-f001" class="html-fig">Figure 1</a>) for 1992–2018. (<b>a</b>) wind, (<b>b</b>) waves.</p> Full article ">Figure 11
<p>Wave power variability indices: coefficient of variation (COV) (<b>a</b>), seasonal variability index (SVI) (<b>b</b>), monthly Variability index (MVI) (<b>c</b>), and annual variability index (AVI) (<b>d</b>).</p> Full article ">Scheme 1
<p>Processing steps to obtain the wave power estimates. * The estimation of Te and Pwave can also be made for a specific site by estimating Hs and σ<sup>0</sup> for the specific site and using the regression coefficients “a” and “b” from the nearest buoy (or by interpolating “a” and “b” from several buoys).</p> Full article ">
14 pages, 5305 KiB  
Article
New Insights into Sea Turtle Propulsion and Their Cost of Transport Point to a Potential New Generation of High-Efficient Underwater Drones for Ocean Exploration
by Nick van der Geest, Lorenzo Garcia, Roy Nates and Fraser Borrett
J. Mar. Sci. Eng. 2023, 11(10), 1944; https://doi.org/10.3390/jmse11101944 - 9 Oct 2023
Cited by 1 | Viewed by 2254
Abstract
Sea turtles gracefully navigate their marine environments by flapping their pectoral flippers in an elegant routine to produce the required hydrodynamic forces required for locomotion. The propulsion of sea turtles has been shown to occur for approximately 30% of the limb beat, with [...] Read more.
Sea turtles gracefully navigate their marine environments by flapping their pectoral flippers in an elegant routine to produce the required hydrodynamic forces required for locomotion. The propulsion of sea turtles has been shown to occur for approximately 30% of the limb beat, with the remaining 70% employing a drag-reducing glide. However, it is unknown how the sea turtle manipulates the flow during the propulsive stage. Answering this research question is a complicated process, especially when conducting laboratory tests on endangered animals, and the animal may not even swim with its regular routine while in a captive state. In this work, we take advantage of our robotic sea turtle, internally known as Cornelia, to offer the first insights into the flow features during the sea turtle’s propulsion cycle consisting of the downstroke and the sweep stroke. Comparing the flow features to the animal’s swim speed, flipper angle of attack, power consumption, thrust and lift production, we hypothesise how each of the flow features influences the animal’s propulsive efforts and cost of transport (COT). Our findings show that the sea turtle can produce extremely low COT values that point to the effectiveness of the sea turtle propulsive technique. Based on our findings, we extract valuable data that can potentially lead to turtle-inspired elements for high-efficiency underwater drones for long-term underwater missions. Full article
(This article belongs to the Topic Applications and Development of Underwater Robotics and Underwater Vision Technology)
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<p>The wild sea turtle (<span class="html-italic">Chelonia mydas</span>) flipper pattern during the animal’s regular swimming routine obtained from van der Geest et al. [<a href="#B6-jmse-11-01944" class="html-bibr">6</a>].</p> Full article ">Figure 2
<p>The robotic sea turtle (<span class="html-italic">Cornelia</span>) illustrating the propulsive cycle consisting of the downstroke and sweep stroke [<a href="#B8-jmse-11-01944" class="html-bibr">8</a>]. Also, see <a href="#app1-jmse-11-01944" class="html-app">Movie S2</a>.</p> Full article ">Figure 3
<p>Turtle dyno. (<b>a</b>) Complete dyno assembly showing camera lighting arrangement. (<b>b</b>) Close-up of robot attached to load cell and linear rail assembly.</p> Full article ">Figure 4
<p>Dye visualisation tools. (<b>a</b>) Streamline tool in use. (<b>b</b>) Cloud tool in use. (<b>c</b>) Streamline tool wake disturbance testing. Also, see <a href="#app1-jmse-11-01944" class="html-app">Movie S3</a>.</p> Full article ">Figure 5
<p>Plots of swimming performance of Cornelia for the Green sea turtle’s regular swimming routine. (<b>a</b>) Thrust production during the downstroke and the sweep stroke obtained from van der Geest et al. [<a href="#B8-jmse-11-01944" class="html-bibr">8</a>]. (<b>b</b>) Lift production during the downstroke and the sweep stroke obtained from van der Geest et al. [<a href="#B8-jmse-11-01944" class="html-bibr">8</a>]. (<b>c</b>) The average Angle of Attack across wingspan. See <a href="#app1-jmse-11-01944" class="html-app">Figure S3</a> for exact AOA values across the flipper span. (<b>d</b>) Power consumption during the downstroke and the sweep stroke obtained from van der Geest et al. [<a href="#B8-jmse-11-01944" class="html-bibr">8</a>]. (<b>e</b>) Turtle swim speed during the downstroke and the sweep stroke obtained from van der Geest et al. [<a href="#B8-jmse-11-01944" class="html-bibr">8</a>]. (<b>f</b>) The flipper tip velocity magnitude during the downstroke and the sweep stroke. (<b>g</b>) For the timing of the observed flow features, see <a href="#app1-jmse-11-01944" class="html-app">Movies S4–S7</a>.</p> Full article ">Figure 6
<p>Downstroke flow features. (<b>a</b>) Velocity plots from various cross-sections along the turtle’s wingspan from CFD. The numbers 1–6 in the top left of each cross-section correlate to the dashed lines shown in <a href="#jmse-11-01944-f006" class="html-fig">Figure 6</a>b. (<b>b</b>) Plan view of the turtle wing showing cross-section locations and velocity streamlines obtained from CFD. (<b>c</b>) Dye visualisation testing shows flow reattachment towards the wing trailing edge and the spanwise flow demonstrated by dye being transported towards the wing tip. (<b>d</b>) Vector plot from CFD showing flow reattachment.</p> Full article ">Figure 7
<p>Flow over the sea turtle’s rear flippers during its regular swimming routine. (<b>a</b>) A wild sea turtle producing the 5-stage locomotion cycle, demonstrating the rear flipper position. (<b>b</b>) Cornelia replicates the wild sea turtle in (<b>a</b>). (<b>c</b>) CFD results showing a velocity contour of the flow around the rear the flippers. (<b>d</b>) Dye visualisation tests showing flow separation point. Also see <a href="#app1-jmse-11-01944" class="html-app">Movie S8</a>.</p> Full article ">Figure 8
<p>Turtle swimming efficiency. (<b>a</b>) Power input data for the complete swimming cycle obtained from van der Geest et al. [<a href="#B8-jmse-11-01944" class="html-bibr">8</a>]. (<b>b</b>) Thrust production for the complete swimming cycle obtained from van der Geest et al. [<a href="#B8-jmse-11-01944" class="html-bibr">8</a>]. (<b>c</b>) Turtle swim speed for complete swim cycle obtained from van der Geest et al. [<a href="#B8-jmse-11-01944" class="html-bibr">8</a>]. (<b>d</b>) Cost of transport plotted against time. (<b>e</b>) The power-to-thrust ratio plotted for the downstroke and the sweep stroke compared to the power-to-thrust ratio of a continuous rotating turtle wing. (<b>f</b>) Turtle-inspired propulsion applied to a low-drag drag streamlined body. Diagram illustrating a tandem configuration with both sets of wings/flippers generating propulsion (also see <a href="#app1-jmse-11-01944" class="html-app">Movie S9</a>).</p> Full article ">
33 pages, 6756 KiB  
Article
On the Digital Twin of The Ocean Cleanup Systems—Part I: Calibration of the Drag Coefficients of a Netted Screen in OrcaFlex Using CFD and Full-Scale Experiments
by Martin Alejandro Gonzalez Jimenez, Andriarimina Daniel Rakotonirina, Bruno Sainte-Rose and David James Cox
J. Mar. Sci. Eng. 2023, 11(10), 1943; https://doi.org/10.3390/jmse11101943 - 8 Oct 2023
Cited by 1 | Viewed by 2238
Abstract
The Ocean Cleanup introduces a Digital Twin (DT) describing the cleanup systems made of netting to extract marine litter from our oceans. It consists of two wings forming a “U-shape” and a retention zone. During operation, the system is towed and drag-driven with [...] Read more.
The Ocean Cleanup introduces a Digital Twin (DT) describing the cleanup systems made of netting to extract marine litter from our oceans. It consists of two wings forming a “U-shape” and a retention zone. During operation, the system is towed and drag-driven with a span-to-length ratio of 0.6 ≤SR*≤ 0.8. The twine Reynolds number is Ret*∈[800:1600], making it experience various local drag coefficients. The DT was built with OrcaFlex (OF) aiming at: (i) avoiding over- or under-designing the system; (ii) supporting the scale-up of the system; and (iii) estimating the costs and/or the impact of our offshore operations. Therefore, we present an attempt to build an accurate DT using data from the Great Pacific Garbage Patch (GPGP). We developed a three-cycle validation: (i) initial guess applying Naumov’s semi-empirical drag coefficient to define the OF drag coefficients without the influence of the angles of attack θ of the wings; (ii) adjustment of the OF drag coefficients using AquaSim (AS) with its twine-by-twine drag correlation for various θ; (iii) re-adjustment of the OF drag coefficients from two-dimensional CFD simulations using Direct Numerical Simulation (DNS) for a twine-by-twine establishment of a drag correlation on a 1 m plane net, highlighting the shielding effects for θ<24°. Consequently, an initial underestimation of −3% in the combined towline tension, for a nominal span (SR*=0.6), was corrected to a slight overestimation of +7% compared to the GPGP data. For a wide span (SR*=0.8), the deviation remained between +1% and +15% throughout the validation process. For a narrow span (SR*∼ 0.02), mostly exhibiting low θ, the first cycle showed a +276% deviation, whereas at the end of the third cycle, it showed a +43% deviation. Full article
(This article belongs to the Special Issue Oceanic Litter Conditions and Performance Evaluation of Marine Cleanups)
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Figure 1
<p>Above-water and underwater footage of <tt>System 002</tt>. <span class="html-fig-inline" id="jmse-11-01943-i001"><img alt="Jmse 11 01943 i001" src="/jmse/jmse-11-01943/article_deploy/html/images/jmse-11-01943-i001.png"/></span> Port-side vessel. <span class="html-fig-inline" id="jmse-11-01943-i002"><img alt="Jmse 11 01943 i002" src="/jmse/jmse-11-01943/article_deploy/html/images/jmse-11-01943-i002.png"/></span> Starboard-side vessel. <span class="html-fig-inline" id="jmse-11-01943-i003"><img alt="Jmse 11 01943 i003" src="/jmse/jmse-11-01943/article_deploy/html/images/jmse-11-01943-i003.png"/></span> Port-side tow bar and towing bridle, connected to a towing line. <span class="html-fig-inline" id="jmse-11-01943-i004"><img alt="Jmse 11 01943 i004" src="/jmse/jmse-11-01943/article_deploy/html/images/jmse-11-01943-i004.png"/></span> Starboard-side tow bar and towing bridle, connected to a towing line. <span class="html-fig-inline" id="jmse-11-01943-i005"><img alt="Jmse 11 01943 i005" src="/jmse/jmse-11-01943/article_deploy/html/images/jmse-11-01943-i005.png"/></span> Port-side wingspan. <span class="html-fig-inline" id="jmse-11-01943-i006"><img alt="Jmse 11 01943 i006" src="/jmse/jmse-11-01943/article_deploy/html/images/jmse-11-01943-i006.png"/></span> Starboard-side wingspan. <span class="html-fig-inline" id="jmse-11-01943-i007"><img alt="Jmse 11 01943 i007" src="/jmse/jmse-11-01943/article_deploy/html/images/jmse-11-01943-i007.png"/></span> Retention opening. <span class="html-fig-inline" id="jmse-11-01943-i008"><img alt="Jmse 11 01943 i008" src="/jmse/jmse-11-01943/article_deploy/html/images/jmse-11-01943-i008.png"/></span> Retention zone. <span class="html-fig-inline" id="jmse-11-01943-i009"><img alt="Jmse 11 01943 i009" src="/jmse/jmse-11-01943/article_deploy/html/images/jmse-11-01943-i009.png"/></span> Extraction pick-up line. (<b>a</b>) Aerial view of <tt>System 002</tt> towed by two vessels. (<b>b</b>) Underwater view of the retention zone made of nets.</p> Full article ">Figure 2
<p>Running Line Monitor (RLM) to measure towline tension.</p> Full article ">Figure 3
<p>Example comparison between <math display="inline"><semantics> <msub> <mi>u</mi> <mi>stw</mi> </msub> </semantics></math> from DVL and <math display="inline"><semantics> <msub> <mi>u</mi> <mi>stw</mi> </msub> </semantics></math> calculated with SOG-sea current.</p> Full article ">Figure 4
<p>Local and global coordinate systems in OrcaFlex.</p> Full article ">Figure 5
<p>Basic definition of a knot-less net.</p> Full article ">Figure 6
<p>A twine in the wake of a leading twine. Illustration with dimensionless vorticity field from 2D direct numerical simulation.</p> Full article ">Figure 7
<p>Method flowchart.</p> Full article ">Figure 8
<p>Sketch of the system with basic definitions.</p> Full article ">Figure 9
<p>Wing section model in OF.</p> Full article ">Figure 10
<p>The 3D sketch of a plane net. Vertical twines in green color. Horizontal twines in red color. These colors are corresponding to the components of Equations (<a href="#FD27-jmse-11-01943" class="html-disp-formula">27</a>) and (<a href="#FD28-jmse-11-01943" class="html-disp-formula">28</a>).</p> Full article ">Figure 11
<p>(<b>a</b>) Top view of Jenny’s wing modeled as collections of successive plane nets. (<b>b</b>) The 2D cross-sectional sketch of a plane net modeled as circular cylinders.</p> Full article ">Figure 12
<p>Illustration of a complex boundary layer separation at <math display="inline"><semantics> <mrow> <msubsup> <mrow> <mi mathvariant="script">R</mi> <mi>e</mi> </mrow> <mi>t</mi> <mo>*</mo> </msubsup> <mo>=</mo> <mn>1000</mn> </mrow> </semantics></math> as seen in the research of Bouard and Coutanceau [<a href="#B47-jmse-11-01943" class="html-bibr">47</a>], Koumoutsakos and Leonard [<a href="#B48-jmse-11-01943" class="html-bibr">48</a>], Mohaghegh and Udaykumar [<a href="#B49-jmse-11-01943" class="html-bibr">49</a>] captured by Basilisk [<a href="#B33-jmse-11-01943" class="html-bibr">33</a>]. Vorticity field (<b>top</b>) and the quadtree structure (<b>bottom</b>).</p> Full article ">Figure 13
<p>Illustration of the drag coefficient and the numerical parameters related to its computation. (<b>a</b>) Instantaneous drag coefficient at <math display="inline"><semantics> <mrow> <msubsup> <mrow> <mi mathvariant="script">R</mi> <mi>e</mi> </mrow> <mi>t</mi> <mo>*</mo> </msubsup> <mo>=</mo> <mn>1000</mn> </mrow> </semantics></math> for various smallest grid sizes. (<b>b</b>) Instantaneous drag coefficient at <math display="inline"><semantics> <mrow> <msubsup> <mrow> <mi mathvariant="script">R</mi> <mi>e</mi> </mrow> <mi>t</mi> <mo>*</mo> </msubsup> <mo>=</mo> <mn>1600</mn> </mrow> </semantics></math> for various smallest grid sizes. (<b>c</b>) Instantaneous number of cells for <math display="inline"><semantics> <mrow> <msup> <mi>δ</mi> <mrow> <mo>*</mo> <mo>−</mo> <mn>1</mn> </mrow> </msup> <mo>=</mo> <mn>128</mn> </mrow> </semantics></math> as a function of <span class="html-italic">c</span> at <math display="inline"><semantics> <mrow> <msubsup> <mrow> <mi mathvariant="script">R</mi> <mi>e</mi> </mrow> <mi>t</mi> <mo>*</mo> </msubsup> <mo>=</mo> <mn>1000</mn> </mrow> </semantics></math>. (<b>d</b>) Instantaneous number of cells for <math display="inline"><semantics> <mrow> <msup> <mi>δ</mi> <mrow> <mo>*</mo> <mo>−</mo> <mn>1</mn> </mrow> </msup> <mo>=</mo> <mn>128</mn> </mrow> </semantics></math> as a function of <span class="html-italic">c</span> at <math display="inline"><semantics> <mrow> <msubsup> <mrow> <mi mathvariant="script">R</mi> <mi>e</mi> </mrow> <mi>t</mi> <mo>*</mo> </msubsup> <mo>=</mo> <mn>1600</mn> </mrow> </semantics></math>. (<b>e</b>) Instantaneous drag coefficient for <math display="inline"><semantics> <mrow> <msup> <mi>δ</mi> <mrow> <mo>*</mo> <mo>−</mo> <mn>1</mn> </mrow> </msup> <mo>=</mo> <mn>128</mn> </mrow> </semantics></math> as a function of <math display="inline"><semantics> <msup> <mi>c</mi> <mo>*</mo> </msup> </semantics></math> at <math display="inline"><semantics> <mrow> <msubsup> <mrow> <mi mathvariant="script">R</mi> <mi>e</mi> </mrow> <mi>t</mi> <mo>*</mo> </msubsup> <mo>=</mo> <mn>1000</mn> </mrow> </semantics></math>. (<b>f</b>) Instantaneous drag coefficient for <math display="inline"><semantics> <mrow> <msup> <mi>δ</mi> <mrow> <mo>*</mo> <mo>−</mo> <mn>1</mn> </mrow> </msup> <mo>=</mo> <mn>128</mn> </mrow> </semantics></math> as a function of <math display="inline"><semantics> <msup> <mi>c</mi> <mo>*</mo> </msup> </semantics></math> at <math display="inline"><semantics> <mrow> <msubsup> <mrow> <mi mathvariant="script">R</mi> <mi>e</mi> </mrow> <mi>t</mi> <mo>*</mo> </msubsup> <mo>=</mo> <mn>1600</mn> </mrow> </semantics></math>.</p> Full article ">Figure 14
<p>Close-up illustration of the shielding effect mechanism for <math display="inline"><semantics> <mrow> <mi>θ</mi> <mo>=</mo> <msup> <mn>8</mn> <mo>°</mo> </msup> </mrow> </semantics></math> at <math display="inline"><semantics> <mrow> <msubsup> <mrow> <mi mathvariant="script">R</mi> <mi>e</mi> </mrow> <mi>t</mi> <mo>*</mo> </msubsup> <mo>=</mo> <mn>1000</mn> </mrow> </semantics></math>. Blue and red, respectively, indicate negative and positive values of the vorticity fields.</p> Full article ">Figure 15
<p>Illustration of the flow structure using the velocity field <math display="inline"><semantics> <msub> <mi>u</mi> <mi>x</mi> </msub> </semantics></math> as a function of <math display="inline"><semantics> <mi>θ</mi> </semantics></math> at <math display="inline"><semantics> <mrow> <msubsup> <mrow> <mi mathvariant="script">R</mi> <mi>e</mi> </mrow> <mi>t</mi> <mo>*</mo> </msubsup> <mo>=</mo> <mn>1000</mn> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msup> <mi>t</mi> <mo>*</mo> </msup> <mo>=</mo> <mn>90</mn> </mrow> </semantics></math>. Blue and red, respectively, indicate low and high values of <math display="inline"><semantics> <msubsup> <mi>u</mi> <mi>x</mi> <mo>*</mo> </msubsup> </semantics></math>.</p> Full article ">Figure 16
<p>Influence of the number of twines <math display="inline"><semantics> <msubsup> <mi>N</mi> <mi>t</mi> <mo>*</mo> </msubsup> </semantics></math> on the instantaneous total drag coefficient for various low angles of attack <math display="inline"><semantics> <mi>θ</mi> </semantics></math> at <math display="inline"><semantics> <mrow> <msubsup> <mrow> <mi mathvariant="script">R</mi> <mi>e</mi> </mrow> <mi>t</mi> <mo>*</mo> </msubsup> <mo>=</mo> <mn>800</mn> </mrow> </semantics></math>.</p> Full article ">Figure 17
<p>Dependence of the drag coefficient on the angle of attack. The median of the fluctuations is shown with the standard deviation.</p> Full article ">Figure 18
<p>Dependence of the lift coefficient on the angle of attack. Median of the fluctuations is shown with standard deviation.</p> Full article ">Figure 19
<p>Dependence of the combined towline tension on the <math display="inline"><semantics> <msub> <mi mathvariant="bold-italic">u</mi> <mi>stw</mi> </msub> </semantics></math>. Superscripts 16, 480, and 630 indicate the span in meters. The colored areas are the 15% increase in the mean load. GPGP data selected from August 2021 to August 2022. The error bar estimation is explained in <a href="#sec2dot1-jmse-11-01943" class="html-sec">Section 2.1</a>.</p> Full article ">Figure 20
<p>Distribution of the angles of attack.</p> Full article ">Figure 21
<p>Schematic representation of the boundaries for the application of the results.</p> Full article ">Figure 22
<p>Interdependence of the twine diameter, the mesh size, the solidity, and the drag coefficient of a net. Adapted from the work of Cheng et al. [<a href="#B13-jmse-11-01943" class="html-bibr">13</a>]. (<b>a</b>) Dependence of the solidity <math display="inline"><semantics> <mrow> <mi>S</mi> <msup> <mi>n</mi> <mo>*</mo> </msup> </mrow> </semantics></math> on the twine diameter and the mesh size. (<b>b</b>) Dependence of the drag coefficient on the solidity for nylon nets at <math display="inline"><semantics> <mrow> <mi>θ</mi> <mo>=</mo> <msup> <mn>90</mn> <mo>°</mo> </msup> </mrow> </semantics></math>. (<span class="html-italic">I</span>) Tang et al. [<a href="#B59-jmse-11-01943" class="html-bibr">59</a>], (<span class="html-italic">II</span>) Gansel et al. [<a href="#B60-jmse-11-01943" class="html-bibr">60</a>], (<span class="html-italic">III</span>) Tsukrov et al. [<a href="#B61-jmse-11-01943" class="html-bibr">61</a>].</p> Full article ">
23 pages, 4527 KiB  
Review
Optimizing Smart Energy Infrastructure in Smart Ports: A Systematic Scoping Review of Carbon Footprint Reduction
by Seyed Behbood Issa Zadeh, Maria Dolores Esteban Perez, José-Santos López-Gutiérrez and Gonzalo Fernández-Sánchez
J. Mar. Sci. Eng. 2023, 11(10), 1921; https://doi.org/10.3390/jmse11101921 - 5 Oct 2023
Cited by 5 | Viewed by 3144
Abstract
To lessen the environmental impact of the maritime industry, ports must decarbonize in conformity with various standards such as the European Green Deal and the Sustainable Development Goals (SDGs). In this regard, they must demonstrate integrated low-emission energy production, distribution, and supply, as [...] Read more.
To lessen the environmental impact of the maritime industry, ports must decarbonize in conformity with various standards such as the European Green Deal and the Sustainable Development Goals (SDGs). In this regard, they must demonstrate integrated low-emission energy production, distribution, and supply, as well as sustainable alternative infrastructure for refueling ships, cargo handling equipment, and other vehicles inside port boundaries. To address this issue, ports must progress toward smartening their operations. This requires intelligent infrastructure and components, with smart energy infrastructure being one of the most crucial ones. It is a part of port energy management systems (EMSs) and works based on modern technology to balance energy demand, distributions, and supply while transitioning to renewable energies. This study investigates the “scoping review” of “smart energy infrastructure” deployment and its efficiency in seaport EMSs to reduce the port’s carbon footprint (C.F). The “Introduction” section discusses the subject’s significance. The “Materials and Methods” section explains the process of selecting and revising references and relevant material. The “Findings” section then examines the several aspects and sections of a smart port and smart energy infrastructure, as well as how they function. The “Discussion” section explains the interpretation based on the present situation. Finally, the “Conclusion” part gives scientific thoughts and comments on the work-study debate and ideas for future research in the same field to help port authorities achieve sustainability. Full article
(This article belongs to the Special Issue Coastal Engineering: Sustainability and New Technologies, 2nd Edition)
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<p>Literature review methodology phases based on the PRISMA-SCR review.</p> Full article ">Figure 2
<p>Key component of smart seaport based on [<a href="#B5-jmse-11-01921" class="html-bibr">5</a>].</p> Full article ">Figure 3
<p>Process of developing a smart port based on [<a href="#B8-jmse-11-01921" class="html-bibr">8</a>].</p> Full article ">Figure 4
<p>Categorizing operation in a smart port based on [<a href="#B2-jmse-11-01921" class="html-bibr">2</a>].</p> Full article ">Figure 5
<p>Smart seaport management sections based on [<a href="#B15-jmse-11-01921" class="html-bibr">15</a>].</p> Full article ">Figure 6
<p>Energy management system responsibilities based on [<a href="#B22-jmse-11-01921" class="html-bibr">22</a>].</p> Full article ">Figure 7
<p>Components of the energy management system’s distribution of energy resources retrieved from [<a href="#B27-jmse-11-01921" class="html-bibr">27</a>].</p> Full article ">Figure 8
<p>The layout of the energy intelligence network in the smart seaport, based on [<a href="#B27-jmse-11-01921" class="html-bibr">27</a>].</p> Full article ">Figure 9
<p>Smart port operation by using ICT based on [<a href="#B39-jmse-11-01921" class="html-bibr">39</a>].</p> Full article ">Figure 10
<p>Schematic of the sustainable smart seaport Multi-Energy Distribution system retrieved from [<a href="#B73-jmse-11-01921" class="html-bibr">73</a>].</p> Full article ">
19 pages, 10659 KiB  
Article
CCGAN as a Tool for Satellite-Derived Chlorophyll a Concentration Gap Reconstruction
by Leon Ćatipović, Frano Matić, Hrvoje Kalinić, Shubha Sathyendranath, Tomislav Županović, James Dingle and Thomas Jackson
J. Mar. Sci. Eng. 2023, 11(9), 1814; https://doi.org/10.3390/jmse11091814 - 18 Sep 2023
Cited by 2 | Viewed by 1219
Abstract
This work represents a modification of the Context Conditional Generative Adversarial Network as a novel implementation of a non-linear gap reconstruction approach of missing satellite-derived chlorophyll a concentration data. By adjusting the loss functions of the network to focus on the structural credibility [...] Read more.
This work represents a modification of the Context Conditional Generative Adversarial Network as a novel implementation of a non-linear gap reconstruction approach of missing satellite-derived chlorophyll a concentration data. By adjusting the loss functions of the network to focus on the structural credibility of the reconstruction, high numerical and structural reconstruction accuracies have been achieved in comparison to the original network architecture. The network also draws information from proxy data, sea surface temperature, and bathymetry, in this case, to improve the reconstruction quality. The implementation of this novel concept has been tested on the Adriatic Sea. The most accurate model reports an average error of 0.06mgm−3 and a relative error of 3.87%. A non-deterministic method for the gap-free training dataset creation is also devised, further expanding the possibility of combining other various oceanographic data to possibly improve the reconstruction efforts. This method, the first of its kind, has satisfied the accuracy requirements set by scientific communities and standards, thus proving its validity in the initial stages of conceptual utilisation. Full article
(This article belongs to the Special Issue Technological Oceanography Volume II)
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<p>The Adriatic Sea. The position on the globe is enclosed by the red rectangle on the minimap.</p> Full article ">Figure 2
<p>Visualisation of the masking process of <math display="inline"><semantics> <msub> <mi>chl</mi> <mi mathvariant="normal">a</mi> </msub> </semantics></math> data (<b>a</b>) representing the full data; and (<b>b</b>) representing the manually masked data.</p> Full article ">Figure 3
<p>Iteration-dependent of the behaviours of generator and discriminator loss values based on two different loss metrics. The top graph displays the mean-squared-error-based (MSE) model. The middle graph depicts the behaviour of the MSE model that takes into account the distribution of land points. Bottom graph displays the Structural-Similarity-Index-Measure-based (SSIM) model.</p> Full article ">Figure 4
<p>Example of MSE ambiguity. The leftmost image was created by the cross multiplication of a sinusoidal vector and its transpose. The middle image was filled with the mean value of the leftmost image. The rightmost image was obtained by the linear transformation of the leftmost image.</p> Full article ">Figure 5
<p>Geospatial distribution of the three error metrics—Structural Similarity Index Measure (SSIM) in (<b>a</b>), mean squared error (MSE) in (<b>b</b>), relative error (RE) in (<b>c</b>). (<b>d</b>) displays the spatial distribution of test data sampling.</p> Full article ">Figure 6
<p>Intra-annual and interannual distributions of Structural Similarity Index Measure (SSIM, blue), mean squared error (MSE, red), and relative error (RE, green). Top part displays the monthly dependency, while the bottom displays the yearly. Boxplots display the minimum, maximum, mean, lower, and upper quartile of each metric.</p> Full article ">Figure 7
<p>Reconstruction of the masked parts of data matrices as selected by the least vector norm based on the characteristic patterns (<b>A1</b>–<b>A5</b>,<b>B1</b>–<b>B5</b>,<b>C1</b>–<b>C5</b>,<b>D1</b>–<b>D5</b>,<b>E1</b>–<b>E5</b>,<b>F1</b>–<b>F5</b>,<b>G1</b>–<b>G5</b>, and <b>H1</b>–<b>H5</b>) derived by double Growing Neural Gas. Columns 1 and 2 display the proxy variables—sea surface temperature (SST) and bathymetrical data, in that respective order. Column 3 is the masked part of the real data chlorophyll <span class="html-italic">a</span> concentration data presented to the CCGAN algorithm, column 4 displays the CCGAN’s reconstructed output and column 5 contains the respective difference between the target and reconstructed data.</p> Full article ">Figure A1
<p>Dilated land–sea mask: (<b>a</b>) geographical land–sea mask; (<b>b</b>) the dilated land–sea mask; and (<b>c</b>) the difference. Land points are purple, sea points are yellow. White represents the geographically accurate sea points that were classified as land points using the dilated land–sea mask.</p> Full article ">Figure A2
<p>Depiction of how the algorithm used for data sampling updates the counter variable to assure the representativeness of the set. Green pixels represent land, whilst blue pixels represent sea. The black number denotes sampling the counter variable for each pixel. For details, see text.</p> Full article ">
26 pages, 10176 KiB  
Article
Monitoring of a Coastal Protection Scheme through Satellite Remote Sensing: A Case Study in Ghana
by Luciana das Neves, Carolina Andrade, Maria Francisca Sarmento and Paulo Rosa-Santos
J. Mar. Sci. Eng. 2023, 11(9), 1771; https://doi.org/10.3390/jmse11091771 - 11 Sep 2023
Cited by 2 | Viewed by 1065
Abstract
Earth observation can provide managers with valuable information on ongoing coastal processes and major trends in coastline evolution, especially in data-poor regions. This paper examines the use of optical satellite images in the mapping of the changes in shoreline position before, during, and [...] Read more.
Earth observation can provide managers with valuable information on ongoing coastal processes and major trends in coastline evolution, especially in data-poor regions. This paper examines the use of optical satellite images in the mapping of the changes in shoreline position before, during, and after the implementation of a protection scheme. The aim of this paper is twofold: (i) to demonstrate the potential of satellite imagery as an effective, robust, and low-cost tool to remotely monitor the effectiveness of protective structures based on a large-scale case study in West Africa; and (ii) to compile lessons learned from this case study that can be used in the design of future interventions. The analysis shows that before the implementation of the protection scheme, the coastal sector was retreating at a rate of −1.6 m/year, which is in line with the average retreat rates reported in other studies for the region. After project implementation, this trend reversed into shoreline accretion at a rate of +1.0 m/year, locally experiencing positive and negative oscillations in the short term. Furthermore, the shoreline-extracted positions proved useful in assessing the impact of differences in the groynes’ permeability with respect to temporary leeside erosion. Finally, it is recommended to continue this monitoring to assess long-term trends. Full article
(This article belongs to the Section Coastal Engineering)
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<p>Ada Coastal Protection Works, project location, Phase 1 and Phase 2.</p> Full article ">Figure 2
<p>Division of the case study area in beach compartments (identified by letters from A in the east to U in the west) between two adjacent groynes (identified with numbers from 1 in the east to 22 in the west).</p> Full article ">Figure 3
<p>Methodology workflow.</p> Full article ">Figure 4
<p>Examples of observed anomalies in satellite imagery of the case study area in 1985 (<b>left panel</b>), and 2010 (<b>right panel</b>) [source: Google Earth].</p> Full article ">Figure 5
<p>Distances along transects of the extracted shorelines to the reference (March 2013), after artificial nourishment during the study period.</p> Full article ">Figure 6
<p>Distances along transects of the extracted shorelines to the reference (March 2013) during and following construction.</p> Full article ">Figure 7
<p>Groyne compartments A and B (including groynes 1, 2, and 3) and distance to the reference shoreline (2013) per considered transect.</p> Full article ">Figure 8
<p>Groyne compartments C and D (including groynes 3, 4, and 5) and distance to the reference shoreline (2013) per considered transect.</p> Full article ">Figure 9
<p>Groyne compartments E and F (including groynes 5, 6, and 7) and distance to the reference shoreline (2013) per considered transect.</p> Full article ">Figure 10
<p>Groyne compartments G and H (including groynes 7, 8, and 9) and distance to the reference shoreline (2013) per considered transect.</p> Full article ">Figure 11
<p>Groyne compartments I and J (including groynes 9, 10, and 11) and distance to the reference shoreline (2013) per considered transect.</p> Full article ">Figure 12
<p>Groyne compartments K and L (including groynes 11, 12, and 13) and distance to the reference shoreline (2013) per considered transect.</p> Full article ">Figure 13
<p>Groyne compartments M and N (including groynes 13, 14, and 15) and distance to the reference shoreline (2013) per considered transect.</p> Full article ">Figure 14
<p>Groyne compartments O and P (including groynes 15, 16, and 17) and distance to the reference shoreline (2013) per considered transect.</p> Full article ">Figure 15
<p>Groyne compartments Q and R (including groynes 17, 18, and 19) and distance to the reference shoreline (2013) per considered transect.</p> Full article ">Figure 16
<p>Groyne compartments S and T (including groynes 19, 20, and 21) and distance to the reference shoreline (2013) per considered transect.</p> Full article ">Figure 17
<p>Groyne compartment U (including groynes 21 and 22) and distance to the reference shoreline (2013) per considered transect.</p> Full article ">Figure 18
<p>Coastline evolution updrift and downdrift the groynes of Phase 1 with respect to the reference shoreline of 2013 (i.e., baseline). The groyne number is indicated on the top left corner.</p> Full article ">Figure 19
<p>Coastline evolution updrift and downdrift the groynes of Phase 2, against reference shoreline in 2013 (i.e., baseline). The groyne number is indicated on the top left corner.</p> Full article ">
25 pages, 17202 KiB  
Article
A Numerical Study on the Hydrodynamic Performance of a Tanker in Bow Sea Conditions Depending on Restraint Conditions
by Soon-Hyun Lee, Seunghyun Hwang, Hwi-Su Kim, Yeo-Jin Hyun, Sun-Kyu Lee and Kwang-Jun Paik
J. Mar. Sci. Eng. 2023, 11(9), 1726; https://doi.org/10.3390/jmse11091726 - 1 Sep 2023
Cited by 3 | Viewed by 1355
Abstract
The importance of accurate ship performance estimation is increasing for efficient ship operation. Ship performance has been evaluated through model tests in the past, but there are limitations in terms of facilities and costs. With the spread of high-performance computers, the method of [...] Read more.
The importance of accurate ship performance estimation is increasing for efficient ship operation. Ship performance has been evaluated through model tests in the past, but there are limitations in terms of facilities and costs. With the spread of high-performance computers, the method of evaluating the performance of a ship by numerical analysis, especially computational fluid dynamics (CFD), has become common. There have been many numerical studies on added resistance under various wave conditions for many years, showing a high reliability. Meanwhile, most of the studies were conducted under conditions where the degree of freedom (DOF) of the ship was limited due to computational complexity. In this study, we tried to compare the added resistance performance and fluid dynamics of S-VLCC with 6 DOFs in the regular wave conditions. One of the methods for utilizing the 6 DOFs is the soft-mooring system, which allows springs to be attached to the bow and stern to recover the non-restoring force of the hull. The second method considers the free-running condition. The virtual disk is used for the self-propulsion of the ship, and the rudder can be rotated to maintain its course. The propeller rotation speed and rudder angle are controlled through PID control. The bow wave (ψ = 180°) and oblique wave (ψ = 150°, 120°) conditions were considered, and various regular wave conditions from short to long wavelengths were regarded. The effects of restraint conditions on the added resistance and motion response amplitude operator (RAO), according to each wave condition, were compared. As a result, there was a difference in the roll motion for each restraint condition, and the y-direction force and yaw moment generated on the hull were compared to analyze the cause. In addition, we observed the change in flow characteristics by comparing the streamlines around the hull and the nominal wake on the propeller plane. Full article
(This article belongs to the Special Issue CFD Applications in Ship and Offshore Hydrodynamics)
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<p>Geometry of S-VLCC.</p> Full article ">Figure 2
<p>Computational domain and boundary conditions according to heading angles.</p> Full article ">Figure 2 Cont.
<p>Computational domain and boundary conditions according to heading angles.</p> Full article ">Figure 3
<p>Free-running simulation grid system; left: side view; right: around the rudder.</p> Full article ">Figure 4
<p>Diagram of soft spring system.</p> Full article ">Figure 5
<p>(<b>a</b>) POW simulation grid distribution, (<b>b</b>) POW performance validation (KP458).</p> Full article ">Figure 6
<p>(<b>a</b>) Geometry of horn-type rudder of KVLCC2; (<b>b</b>) mesh distribution around rudder.</p> Full article ">Figure 7
<p>(<b>a</b>) Yaw angle and (<b>b</b>) rudder angle according to PID gain value.</p> Full article ">Figure 8
<p>Convergence of variables over time.</p> Full article ">Figure 9
<p>Comparison of added resistance performance [<a href="#B28-jmse-11-01726" class="html-bibr">28</a>,<a href="#B29-jmse-11-01726" class="html-bibr">29</a>,<a href="#B30-jmse-11-01726" class="html-bibr">30</a>].</p> Full article ">Figure 10
<p>Comparison of thrust deduction factors according to wave conditions.</p> Full article ">Figure 11
<p>The 3D domain mesh distribution, boundary conditions, and wave-forcing zone.</p> Full article ">Figure 12
<p>Fluid performance: (<b>a</b>) pressure distribution; and (<b>b</b>) nominal wake distribution.</p> Full article ">Figure 13
<p>Comparison of added resistance coefficients according to conditions.</p> Full article ">Figure 14
<p>Comparison of sway and yaw motion at different wavelength ratios in bow quartering sea conditions.</p> Full article ">Figure 15
<p>(<b>a</b>) Y−force and (<b>b</b>) z−moment according to the time series at <math display="inline"><semantics> <mrow> <mi>λ</mi> <mo>/</mo> <msub> <mi>L</mi> <mrow> <mi>P</mi> <mi>P</mi> </mrow> </msub> <mo>=</mo> <mn>0.5</mn> </mrow> </semantics></math>.</p> Full article ">Figure 16
<p>Comparison of (<b>a,b</b>) y−force coefficients and (<b>c,d</b>) Yaw moment coefficients in oblique waves.</p> Full article ">Figure 17
<p>Comparison of motion RAOs by wavelength ratio according to heading angle.</p> Full article ">Figure 18
<p>Comparison of time-averaged streamlines and wall shear stress distributions passing <math display="inline"><semantics> <mrow> <mi>X</mi> <mo>/</mo> <msub> <mi>L</mi> <mrow> <mi>P</mi> <mi>P</mi> </mrow> </msub> <mo>=</mo> <mn>0.03</mn> </mrow> </semantics></math>.</p> Full article ">Figure 19
<p>Comparison of time-averaged streamlines and wall shear stress distributions passing <math display="inline"><semantics> <mrow> <mi>X</mi> <mo>/</mo> <msub> <mi>L</mi> <mrow> <mi>P</mi> <mi>P</mi> </mrow> </msub> <mo>=</mo> <mn>0.1</mn> </mrow> </semantics></math>.</p> Full article ">Figure 20
<p>Comparison of time-averaged nominal wake distribution.</p> Full article ">Figure 21
<p>Comparison of nominal wake per wave direction according to wave encounter period.</p> Full article ">
22 pages, 1802 KiB  
Article
A Possible Synergistic Approach: Case Study of Saccharina latissima Extract and Nitrifying Bacteria in Lettuce
by Diana Pacheco, João Cotas, Leonel Pereira and Kiril Bahcevandziev
J. Mar. Sci. Eng. 2023, 11(9), 1645; https://doi.org/10.3390/jmse11091645 - 23 Aug 2023
Cited by 2 | Viewed by 2759
Abstract
Nowadays, the exponential expansion in human population has resulted in the massification of intensive agricultural practices, with crop yield and sustainability being one of the most pressing challenges. Therefore, there was a need for new and natural fertilizers and pesticides, which has become [...] Read more.
Nowadays, the exponential expansion in human population has resulted in the massification of intensive agricultural practices, with crop yield and sustainability being one of the most pressing challenges. Therefore, there was a need for new and natural fertilizers and pesticides, which has become a popular agricultural trend nowadays. Therefore, there was an increased interest to apply seaweed and bacterial extracts in agriculture to promote new means of sustainability and soil usage. This work aims to test seaweed inclusion in the agricultural field, as a simple or complex foliar biofertilizer solution applied together with a nitrifying bacteria, to verify if there is a potential synergistic effect of these two different types of biofertilizers on economically important vegetables. As a result, experiments were conducted in a greenhouse using an aqueous extract of the brown seaweed Saccharina latissima (1.2% v/v) and a biofertilizer based on BlueN bacteria (0.03% m/v), both simple or in combination, on lettuce (Lactuca sativa L. var. crispa) plants. The seaweed extract (simple or in combination), presented favorable effect on lettuce growth and nutritional properties. The aqueous algal extract, and it in combination with BlueN, produced heavier lettuce leaves (74.25 ± 6.86 and 74.13 ± 3.07 g, respectively) than the controls and enriched leaf micronutrient contents (zinc and manganese). Also, this study demonstrated that a combined seaweed-bacteria fertilizer did not show synergistic behavior, being a non-profitable solution when compared to a simple seaweed extract. In summary, this study demonstrated that simple (crude) seaweed extracts can be considered as an important key for natural plant biofertilizers and growth stimulators concerned with the blue circular economy. Full article
(This article belongs to the Section Marine Biology)
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<p>Photographic record of <span class="html-italic">Saccharina latissima</span> (Phaeophyceae) (central part of the photo) on Viana do Castelo harbor (Lima River mouth) (41°41′17.7″ N 8°50′11.4″ W).</p> Full article ">Figure 2
<p>Register of minimum and maximum temperatures (°C), and relative air humidity (%) recorded in the greenhouse during the bioassay.</p> Full article ">Figure 3
<p>Photographic record of the lettuce plants in each treatment at the end of the growth experiment: (<b>a</b>) CP—positive control; (<b>b</b>) CN—negative control; (<b>c</b>) E—algal extract; (<b>d</b>) B—BlueN; (<b>e</b>) EB—algal extract + BlueN.</p> Full article ">Figure 4
<p>(<b>a</b>) Root length and (<b>b</b>) aerial-part diameter; (<b>c</b>) root and (<b>d</b>) aerial-part fresh weight of the lettuces on each treatment. The graphs are shown as average values and SE (n = 12). CP—positive control; CN—negative control; E—algal extract; B—BlueN; EB—algal extract + BlueN. Statistically significant differences (<span class="html-italic">p</span> &lt; 0.05) are expressed by different letters <sup>a,b</sup>.</p> Full article ">
16 pages, 8734 KiB  
Article
Real-Time Instance Segmentation for Detection of Underwater Litter as a Plastic Source
by Brendan Chongzhi Corrigan, Zhi Yung Tay and Dimitrios Konovessis
J. Mar. Sci. Eng. 2023, 11(8), 1532; https://doi.org/10.3390/jmse11081532 - 31 Jul 2023
Cited by 13 | Viewed by 2445
Abstract
Thousands of tonnes of litter enter the ocean every day, posing a significant threat to marine life and ecosystems. While floating and beach litter are often in the spotlight, about 70% of marine litter eventually sinks to the seafloor, making underwater litter the [...] Read more.
Thousands of tonnes of litter enter the ocean every day, posing a significant threat to marine life and ecosystems. While floating and beach litter are often in the spotlight, about 70% of marine litter eventually sinks to the seafloor, making underwater litter the largest accumulation of marine litter that often goes undetected. Plastic debris makes up the majority of ocean litter and is a known source of microplastics in the ocean. This paper focuses on the detection of ocean plastic using neural network models. Two neural network models will be trained, i.e., YOLACT and the Mask R-CNN, for the instance segmentation of underwater litter in images. The models are trained on the TrashCAN dataset, using pre-trained model weights trained using COCO. The trained neural network could achieve a mean average precision (mAP) of 0.377 and 0.365 for the Mask R-CNN and YOLACT, respectively. The lightweight nature of YOLACT allows it to detect images at up to six times the speed of the Mask R-CNN, while only making a comparatively smaller trade-off in terms of performance. This allows for two separate applications: YOLACT for the collection of litter using autonomous underwater vehicles (AUVs) and the Mask R-CNN for surveying litter distribution. Full article
(This article belongs to the Special Issue Marine Litter and Sustainability of Ocean Ecosystems)
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<p>Instance segmentation example using YOLACT with the IoU metric.</p> Full article ">Figure 2
<p>Example of TrashCAN dataset showing bounding boxes and mask annotations.</p> Full article ">Figure 3
<p>Sample of images from TrashCAN [<a href="#B26-jmse-11-01532" class="html-bibr">26</a>].</p> Full article ">Figure 4
<p>Images with visual noise. (<b>a</b>) The same object in a clear and noisy observation. (<b>b</b>) Example of the strong light source aboard the ROV creating noise from suspended particles.</p> Full article ">Figure 5
<p>Objects in different situations. (<b>a</b>) Three different variations of a can. (<b>b</b>) Different shapes of plastic bags. (<b>c</b>) The same object is from two angles with a vastly different apparent shape.</p> Full article ">Figure 6
<p>The Mask R-CNN framework showing the parallel second stage [<a href="#B27-jmse-11-01532" class="html-bibr">27</a>].</p> Full article ">Figure 7
<p>YOLACT architecture [<a href="#B20-jmse-11-01532" class="html-bibr">20</a>].</p> Full article ">Figure 8
<p>Transfer learning.</p> Full article ">Figure 9
<p>Graphic depicting IoU.</p> Full article ">Figure 10
<p>Precision–recall curve [<a href="#B31-jmse-11-01532" class="html-bibr">31</a>].</p> Full article ">Figure 11
<p>Sampled results comparing both models displaying the class and IoU of the predicted mask (Mask R-CNN labels have been overlayed for better viewing (<b>a</b>–<b>c</b>), YOLACT models: (<b>d</b>–<b>f</b>)).</p> Full article ">Figure 12
<p>Comparison between the annotated mask and the detected mask for selected images. (<b>a</b>) Example image of a singular plastic bag in the image. (<b>b</b>) Example image with multiple objects.</p> Full article ">
24 pages, 4096 KiB  
Article
Sensitivity Analysis of Modal Parameters of a Jacket Offshore Wind Turbine to Operational Conditions
by Nasim Partovi-Mehr, Emmanuel Branlard, Mingming Song, Babak Moaveni, Eric M. Hines and Amy Robertson
J. Mar. Sci. Eng. 2023, 11(8), 1524; https://doi.org/10.3390/jmse11081524 - 30 Jul 2023
Cited by 6 | Viewed by 2259
Abstract
Accurate estimation of offshore wind turbine (OWT) modal parameters has a prominent effect on the design loads, lifetime prediction, and dynamic response of the system. Modal parameters can vary during the operation of OWTs. This paper studies the variation and sensitivity analysis of [...] Read more.
Accurate estimation of offshore wind turbine (OWT) modal parameters has a prominent effect on the design loads, lifetime prediction, and dynamic response of the system. Modal parameters can vary during the operation of OWTs. This paper studies the variation and sensitivity analysis of an OWT’s modal parameters with respect to operational and environmental conditions. Three finite element models of a jacket-supported OWT at the Block Island Wind Farm are created within the OpenSees, SAP2000, and OpenFAST platforms and validated using experimental measurements. The OpenFAST model is used to simulate the modal parameters of the turbine under various wind speed, rotor speed, power, yaw angle, mean sea level, blade pitch angle, and soil spring values. The model-predicted modal parameters of the first fore–aft (FA) and side–side (SS) modes are compared to those identified from experimental measurements. Results from the simulations show that the first FA natural frequency and damping ratio mostly depend on the rotor speed and wind speed, respectively, while yaw angle and mean sea level do not have a visible effect. It is observed that there is about 8% stiffening in the first FA frequency and an aerodynamic damping of 7.5% during the operation of the OWT. Full article
(This article belongs to the Special Issue Tenth Anniversary of JMSE – Recent Advances and Future Perspectives)
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<p>Normalized power spectral density for typical wind and wave loads, and the range of rotor-induced excitation ranges (1P and 3P ranges) to avoid resonance of the Block Island OWT.</p> Full article ">Figure 2
<p>Wind turbines B1–B3 (from <b>left</b> to <b>right</b>) standing in the Block Island Wind Farm, RI, USA (Photo: Michael Dwyer).</p> Full article ">Figure 3
<p>Instrumentation layout of sensors along with the BIWF-B2 turbine, reprinted from [<a href="#B43-jmse-11-01524" class="html-bibr">43</a>], with permission from Elsevier, 2023.</p> Full article ">Figure 4
<p>Identified first FA/SS natural frequencies and damping ratios of the B2 turbine using experimental data of one week of operation (21 April 2021 to 27 April 2021) and one day of idling (14 June 2021). Red stars and blue triangles are related to the idling and operating turbine, respectively.</p> Full article ">Figure 5
<p>Model of B2 OWT.</p> Full article ">Figure 6
<p>Natural frequencies and mode shapes from the SAP2000 and OpenSees models.</p> Full article ">Figure 7
<p>OpenFAST main coordinate system [<a href="#B57-jmse-11-01524" class="html-bibr">57</a>].</p> Full article ">Figure 8
<p>Power curve for the B2 OWT showing Regions 1, 2, and 3 of the power generation.</p> Full article ">Figure 9
<p>Campbell diagram of the B2 OWT turbine for the first FA and SS bending modes according to the OpenFAST model, in the cases of flexible blades (1st Tower FA and 1st Tower SS) and rigid blades (1st Tower FA-Rigid and 1st Tower SS-Rigid), and the analytical damping ratio for the first FA mode.</p> Full article ">Figure 10
<p>Campbell diagrams for the first FA and SS tower bending modes of the OWT in the parked condition (rotor rpm = 0).</p> Full article ">Figure 11
<p>Campbell diagrams for the first FA and SS tower bending modes of the B2 OWT operating in Region 3*.</p> Full article ">Figure 12
<p>Campbell diagrams for the first FA and SS tower bending modes of the B2 OWT operating at a constant wind speed of 7 m/s and varying rotor speed.</p> Full article ">Figure 13
<p>Yaw angle effect on the first FA and SS tower bending modes of the B2 OWT, operating at the rated wind speed of 11 m/s and rotor speed of 11.5 rpm.</p> Full article ">Figure 14
<p>Effect of vertical soil spring on the first FA and SS tower bending modes of the B2 OWT in a parked condition.</p> Full article ">
28 pages, 871 KiB  
Review
Measuring Resilience to Sea-Level Rise for Critical Infrastructure Systems: Leveraging Leading Indicators
by Lamis Amer, Murat Erkoc, Rusty A. Feagin, Sabarethinam Kameshwar, Katharine J. Mach and Diana Mitsova
J. Mar. Sci. Eng. 2023, 11(7), 1421; https://doi.org/10.3390/jmse11071421 - 15 Jul 2023
Cited by 5 | Viewed by 2049
Abstract
There has been a growing interest in research on how to define and build indicators of resilience to address challenges associated with sea-level rise. Most of the proposed methods rely on lagging indicators constructed based on the historical performance of an infrastructure sub-system. [...] Read more.
There has been a growing interest in research on how to define and build indicators of resilience to address challenges associated with sea-level rise. Most of the proposed methods rely on lagging indicators constructed based on the historical performance of an infrastructure sub-system. These indicators are traditionally utilized to build curves that describe the past response of the sub-system to stressors; these curves are then used to predict the future resilience of the sub-system to hypothesized events. However, there is now a growing concern that this approach cannot provide the best insights for adaptive decision-making across the broader context of multiple sub-systems and stakeholders. As an alternative, leading indicators that are built on the structural characteristics that embody system resilience have been gaining in popularity. This structure-based approach can reveal problems and gaps in resilience planning and shed light on the effectiveness of potential adaptation activities. Here, we survey the relevant literature for these leading indicators within the context of sea-level rise and then synthesize the gained insights into a broader examination of the current research challenges. We propose research directions on leveraging leading indicators as effective instruments for incorporating resilience into integrated decision-making on the adaptation of infrastructure systems. Full article
(This article belongs to the Special Issue Sea Level Rise: Drivers, Variability and Impacts)
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<p>Distribution of articles proposing resilience-leading indicators to model a single (shown on the diagonal sub-graphs) or multiple system response capacities for different infrastructure systems.</p> Full article ">Figure 2
<p>Bruun Rule of shoreline retreat.</p> Full article ">Figure 3
<p>A diagram of a coastal aquifer showing the main seawater intrusion (SWI) metrics.</p> Full article ">Figure 4
<p>Hierarchical structure of resilience indicators, sub-indicators, and resilience-critical variables.</p> Full article ">Figure 5
<p>A diagrammatic representation of interdependencies across infrastructure systems (inclusive of other systems such as harbor, residential, communications, etc.) in the context of SLR impact.</p> Full article ">
19 pages, 7231 KiB  
Article
Fatigue Analysis of Inter-Array Power Cables between Two Floating Offshore Wind Turbines Including a Simplified Method to Estimate Stress Factors
by Dennis Beier, Anja Schnepf, Sean Van Steel, Naiquan Ye and Muk Chen Ong
J. Mar. Sci. Eng. 2023, 11(6), 1254; https://doi.org/10.3390/jmse11061254 - 20 Jun 2023
Cited by 7 | Viewed by 3961
Abstract
The use of floating offshore wind farms for electrical energy supply is expected to rise significantly over the coming years. Suspended inter-array power cables are a new design to connect floating offshore wind turbines (FOWTs) with shorter cable lengths than conventional setups. The [...] Read more.
The use of floating offshore wind farms for electrical energy supply is expected to rise significantly over the coming years. Suspended inter-array power cables are a new design to connect floating offshore wind turbines (FOWTs) with shorter cable lengths than conventional setups. The present study investigates the fatigue life of a suspended power cable with attached buoys connecting two spar-type FOWTs. Typical environmental conditions for the North Sea are applied. The nonlinear bending behavior of the power cable is considered in the analysis. Fatigue assessment is performed using the numerical software OrcaFlex based on stress factors obtained from cross-section analysis. An effective method for obtaining the stress factors is proposed for early engineering design stages and compared with the finite element software UFLEX simulation results. The simplified method delivers similar results for axial tension loads and conservative results for bending loads compared with results obtained from the finite element software. Stress components resulting from curvature variation are identified as the main contributors to fatigue damage. The most critical locations along the power cable for fatigue life are close to the hang-off points. Full article
(This article belongs to the Special Issue Innovative Development of Offshore Wind Technology)
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<p>OC3-Hywind FOWT geometry. Dimensions in m.</p> Full article ">Figure 2
<p>Power cable cross-section (reproduced from Nexans [<a href="#B33-jmse-11-01254" class="html-bibr">33</a>]).</p> Full article ">Figure 3
<p>S–N curve for the power cable components (Nexans [<a href="#B33-jmse-11-01254" class="html-bibr">33</a>]).</p> Full article ">Figure 4
<p>Concept of two FOWTs with a suspended power cable.</p> Full article ">Figure 5
<p>Power cable hang-off detail.</p> Full article ">Figure 6
<p>Loading directions on the suspended power cable system.</p> Full article ">Figure 7
<p>Nonlinear bending behavior of the power cable for different axial tension levels.</p> Full article ">Figure 8
<p>Nonlinear bending behavior of the power cable for different rotations.</p> Full article ">Figure 9
<p>Finite element results for axial tension and curvature loads for 50 kN axial and 0.1 m<sup>−1</sup> curvature loading.</p> Full article ">Figure 10
<p>Armor wire stresses are obtained from different axial loads.</p> Full article ">Figure 11
<p>Copper wire stresses are obtained from different axial loads.</p> Full article ">Figure 12
<p>Armor wire stresses are obtained from different curvatures with 5 kN axial tension.</p> Full article ">Figure 13
<p>Copper wire stresses are obtained from different curvatures with 5 kN axial tension.</p> Full article ">Figure 14
<p>Fatigue life of the armor wires in the SOL state.</p> Full article ">Figure 15
<p>Fatigue life of the copper wires in the SOL state.</p> Full article ">Figure 16
<p>Fatigue life of the armor wires in the EOL state.</p> Full article ">Figure 17
<p>Fatigue life of the copper wires in the EOL state.</p> Full article ">Figure 18
<p>Locations of the lowest fatigue life along the suspended power cable.</p> Full article ">
26 pages, 6532 KiB  
Article
Methodological Design Optimization of a Marine LNG Internal Combustion Gas Engine to Burn Alternative Fuels
by Ander Ruiz Zardoya, Iñigo Oregui Bengoetxea, Angel Lopez Martinez, Iñaki Loroño Lucena and José A. Orosa
J. Mar. Sci. Eng. 2023, 11(6), 1194; https://doi.org/10.3390/jmse11061194 - 8 Jun 2023
Cited by 6 | Viewed by 2159
Abstract
Marine emission policies are becoming more demanding; thus, ship propulsion and power generation technologies need to be adapted to current scenarios. LNG is already considered to be a transition fuel, and new alternative marine fuels are emerging. The aim of this study was [...] Read more.
Marine emission policies are becoming more demanding; thus, ship propulsion and power generation technologies need to be adapted to current scenarios. LNG is already considered to be a transition fuel, and new alternative marine fuels are emerging. The aim of this study was to develop an innovative methodology to optimize and adapt the combustion system of an LNG internal combustion marine engine to burn alternative marine fuels. The present study was based on LBG, but the methodology could be replicated with other fuels. A total of six tests were carried out, with three prechamber designs and three spark plug designs. Each test was carried out in a single-cylinder engine with two types of high-methane-number fuel. The influence on thermal efficiency parameters such as the prechamber volume, the orientation of the flame holes, and the existence of a central hole was studied. In the case of the spark plug, the influence of the amount of precious metal in the electrode, its shape and its insertion into the prechamber were analysed. Experiments showed that by modifying both the prechamber and the spark plug, maximum improvements in thermal efficiency of 1.9% can be achieved. Those improvements allowed the LBG engine to suffer only a 4.3% thermal efficiency reduction, as opposed to its LNG counterpart. By applying the proposed methodology, the thermal efficiency of commercially available internal combustion gas engines could be improved. Full article
(This article belongs to the Special Issue Marine Engines Performance and Emissions Ⅲ)
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<p>Most common combustion systems in lean-burn engines [<a href="#B9-jmse-11-01194" class="html-bibr">9</a>].</p> Full article ">Figure 2
<p>Accumulation of combustion waste in the check valves.</p> Full article ">Figure 3
<p>Diagram of design parameters of a combustion prechamber.</p> Full article ">Figure 4
<p>SCE test bench.</p> Full article ">Figure 5
<p>Misfire (<b>left</b>) and knocking (<b>right</b>) detection through chamber pressure transducers.</p> Full article ">Figure 6
<p>AFR as a function of exhaust NOx in configurations 2, 3 and 4.</p> Full article ">Figure 7
<p>Thermal efficiency as a function of exhaust NOx in configurations 2, 3 and 4.</p> Full article ">Figure 8
<p>Maximum pressure as a function of exhaust NOx in configurations 2, 3 and 4.</p> Full article ">Figure 9
<p>Pressure covariance as a function of exhaust NOx in configurations 2, 3 and 4.</p> Full article ">Figure 10
<p>Total hydrocarbons as a function of exhaust NOx in configurations 2, 3 and 4.</p> Full article ">Figure 11
<p>AFR as a function of exhaust NOx in configurations 3, 5, 6 and 7.</p> Full article ">Figure 12
<p>Thermal efficiency as a function of exhaust NOx in configurations 3, 5, 6 and 7.</p> Full article ">Figure 13
<p>Maximum pressure as a function of exhaust NOx in configurations 3, 5, 6 and 7.</p> Full article ">Figure 14
<p>Maximum pressure covariance as a function of exhaust NOx in configurations 3, 5, 6 and 7.</p> Full article ">Figure 15
<p>Total hydrocarbons as a function of exhaust NOx in configurations 3, 5, 6 and 7.</p> Full article ">Figure 16
<p>Heat release (<b>left</b>) and main chamber pressure (<b>right</b>) curves in configurations 1 and 3.</p> Full article ">
19 pages, 758 KiB  
Review
Maritime Anomaly Detection for Vessel Traffic Services: A Survey
by Thomas Stach, Yann Kinkel, Manfred Constapel and Hans-Christoph Burmeister
J. Mar. Sci. Eng. 2023, 11(6), 1174; https://doi.org/10.3390/jmse11061174 - 3 Jun 2023
Cited by 9 | Viewed by 3376
Abstract
A Vessel Traffic Service (VTS) plays a central role in maritime traffic safety. Regulations are given by the International Maritime Organization (IMO) and Guidelines by the International Association of Marine Aids to Navigation and Lighthouse Authorities (IALA). Accordingly, VTS facilities utilize communication and [...] Read more.
A Vessel Traffic Service (VTS) plays a central role in maritime traffic safety. Regulations are given by the International Maritime Organization (IMO) and Guidelines by the International Association of Marine Aids to Navigation and Lighthouse Authorities (IALA). Accordingly, VTS facilities utilize communication and sensor technologies such as an Automatic Identification System (AIS), radar, radio communication and others. Furthermore, VTS operators are motivated to apply Decision Support Tools (DST), since these can reduce workloads and increase safety. A promising type of DST is anomaly detection. This survey presents an overview of state-of-the-art approaches of anomaly detection for the surveillance of maritime traffic. The approaches are characterized in the context of VTS and, thus, most notably, sorted according to utilized communication and sensor technologies, addressed anomaly types and underlying detection techniques. On this basis, current trends as well as open research questions are deduced. Full article
(This article belongs to the Special Issue Maritime Security and Risk Assessments)
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<p>Literature-review process comprising collection, selection and classification steps.</p> Full article ">Figure 2
<p>Counts of publications retrievable with the defined search phrase via Google Scholar. The deep black bars are the years that are within the scope of this survey.</p> Full article ">Figure 3
<p>Counts of utilized data sources, i.e., communication and sensor types, as data sources in reviewed approaches. Data sources are abbreviated as follows: AIS (A), camera (C), radar (R) and other (O). Abbreviations are concatenated when data sources are combined.</p> Full article ">Figure 4
<p>Counts of addressed anomaly types in reviewed approaches.</p> Full article ">Figure 5
<p>Counts of underlying techniques of reviewed approaches.</p> Full article ">Figure 6
<p>Counts of detection techniques combined with anomaly types.</p> Full article ">
24 pages, 3404 KiB  
Article
Measuring Detection Efficiency of High-Residency Acoustic Signals for Estimating Probability of Fish–Turbine Encounter in a Fast-Flowing Tidal Passage
by Brian Gavin Sanderson, Charles William Bangley, Louise Patricia McGarry and Daniel James Hasselman
J. Mar. Sci. Eng. 2023, 11(6), 1172; https://doi.org/10.3390/jmse11061172 - 2 Jun 2023
Cited by 7 | Viewed by 1441
Abstract
Semidiurnal tidal currents can exceed 5 ms−1 in Minas Passage, Bay of Fundy, where a tidal energy demonstration area has been designated to generate electricity using marine hydrokinetic turbines. The risk of harmful fish–turbine interaction cannot be dismissed for either migratory [...] Read more.
Semidiurnal tidal currents can exceed 5 ms−1 in Minas Passage, Bay of Fundy, where a tidal energy demonstration area has been designated to generate electricity using marine hydrokinetic turbines. The risk of harmful fish–turbine interaction cannot be dismissed for either migratory or local fish populations. Individuals belonging to several fish populations were acoustically tagged and monitored by using acoustic receivers moored within the Minas Passage. Detection efficiency ρ is required as the first step to estimate the probability of fish–turbine encounter. Moored Innovasea HR2 receivers and high-residency (HR) tags were used to obtain detection efficiency ρ as a function of range and current speed, for near-seafloor signal paths within the tidal energy development area. Strong tidal currents moved moorings, so HR tag signals and their reflections from the sea surface were used to measure ranges from tags to receivers. HR2 self-signals that reflected off the sea surface showed which moorings were displaced to lower and higher levels on the seafloor. Some of the range testing paths had anomalously low ρ, which might be attributed to variable bathymetry blocking the line-of-sight signal path. Clear and blocked signal paths accord with mooring levels. The application of ρ is demonstrated for the calculation of abundance, effective detection range, and detection-positive intervals. High-residency signals were better detected than pulse position modulation (PPM) signals. Providing that the presently obtained ρ applies to tagged fish that swim higher in the water column, there is a reasonable prospect that probability of fish–turbine encounter can be estimated by monitoring fish that carry HR tags. Full article
(This article belongs to the Special Issue Interface between Offshore Renewable Energy and the Environment)
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<p>Location of the mooring array on a flat volcanic plateau within the TED area on the northern side of Minas Passage, Bay of Fundy, Nova Scotia. The present study used seven moorings that are numbered from south to north. Depth contours are labeled in 5 m intervals.</p> Full article ">Figure 2
<p>Subsurface depth of the HR2 obtained from HR<math display="inline"><semantics> <msub> <mrow/> <mi>SELF</mi> </msub> </semantics></math> signals and their reflection from the sea surface. (<b>a</b>) Subsurface depth at site 2. (<b>b</b>) Subsurface depth at site 4.</p> Full article ">Figure 3
<p>Daily-averaged depths of the HR2 receivers are color coded according to mooring site. Vertical whiskers indicate 95% confidence intervals, which are often too small for the plot to resolve.</p> Full article ">Figure 4
<p>Separations between sites. Left insets show separation changing by about 1 m over the semidiurnal tidal time scale. The lower left inset also plots normalized tidal current (gray). Right inset shows depth at site 5 and also distance to a nearby reflective object.</p> Full article ">Figure 5
<p>Tilt measured by the HR2 at site 6 (<b>top</b>) and current along drifter tracks that passed over the volcanic plateau (<b>bottom</b>).</p> Full article ">Figure 6
<p>Comparison of detection efficiency for signal propagation from site 1 to 4 (202 m range) and from site 7 to 4 (193 m range).</p> Full article ">Figure 7
<p>Contours of detection efficiency as a function of current speed (positive flood, negative ebb) and range. Detection efficiency was obtained from HR signals transmitted by the tags and detected by HR2 units. Measurements were obtained at ranges between sites indicated by labeling beside magenta line ticks.</p> Full article ">Figure 8
<p>Probability <math display="inline"><semantics> <msub> <mi>ρ</mi> <mn>10</mn> </msub> </semantics></math> that HR transmissions between sites 4 and 5 will be detected during each 10 min interval (blue). Green shading shows flood tide normalized by 5 ms<math display="inline"><semantics> <msup> <mrow/> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> </semantics></math>. Red shows range between the sites.</p> Full article ">Figure 9
<p>Contours of detection efficiency for HR signals transmitted by one HR2 and detected by another HR2. Ranges measured are indicated in magenta and sites associated with each range are labeled to the right.</p> Full article ">Figure 10
<p>Distribution of current when sampled uniformly (blue), sampled when the HR<math display="inline"><semantics> <msub> <mrow/> <mi>FAKE</mi> </msub> </semantics></math> signal was detected at 1.25 m range (green), and sampled when HR<math display="inline"><semantics> <msub> <mrow/> <mi>FAKE</mi> </msub> </semantics></math> was detected at more distant sites (orange). Whiskers on the probability density function indicate ±two standard errors.</p> Full article ">Figure 11
<p>Detection efficiency underestimates the probability of a detection-positive 120 s interval.</p> Full article ">Figure 12
<p>Contours of the detection efficiency that best applies to detecting tagged fish that swim well clear of the seafloor. This detection efficiency is obtained by selecting those HR2-HR2 propagation paths that do not appear to be blocked by variations in seafloor topography (solid magenta lines). Tag–HR2 transmissions were used to add probabilities at the greatest range (green line, top-right corner).</p> Full article ">Figure 13
<p>Effective detection range obtained by integrating the probability that a HR signal is detected. Using HR2-to-HR2 transmission paths that do not exhibit obvious blocking (blue), all of the HR2-to-HR2 transmission paths that were measured (red), and tag–HR2 transmission paths (orange).</p> Full article ">Figure 14
<p>Contours of the ratio of HR detection efficiency to PPM detection efficiency. Contours are on a geometric scale.</p> Full article ">Figure A1
<p>(<b>a</b>) Time series of a 69 kHz pulse position modulation signal (PPM) that was measured in the Minas Passage. (<b>b</b>) Detail of the fifth pulse showing a reflection from the sea surface and reverberation.</p> Full article ">Figure A2
<p>(<b>a</b>) Time series of a high-residency (HR) signal measured at close range. Carrier wave frequency is 170 kHz, and signal duration is approximately 5.8 ms. Information is encoded by abrupt phase changes from one segment to the next. (<b>b</b>) Detail showing the first phase change in the HR signal.</p> Full article ">
28 pages, 836 KiB  
Review
Modes of Operation and Forcing in Oil Spill Modeling: State-of-Art, Deficiencies and Challenges
by Panagiota Keramea, Nikolaos Kokkos, George Zodiatis and Georgios Sylaios
J. Mar. Sci. Eng. 2023, 11(6), 1165; https://doi.org/10.3390/jmse11061165 - 1 Jun 2023
Cited by 5 | Viewed by 2493
Abstract
Oil spills may have devastating effects on marine ecosystems, public health, the economy, and coastal communities. As a consequence, scientific literature contains various up-to-date, advanced oil spill predictive models, capable of simulating the trajectory and evolution of an oil slick generated by the [...] Read more.
Oil spills may have devastating effects on marine ecosystems, public health, the economy, and coastal communities. As a consequence, scientific literature contains various up-to-date, advanced oil spill predictive models, capable of simulating the trajectory and evolution of an oil slick generated by the accidental release from ships, hydrocarbon production, or other activities. To predict in near real time oil spill transport and fate with increased reliability, these models are usually coupled operationally to synoptic meteorological, hydrodynamic, and wave models. The present study reviews the available different met-ocean forcings that have been used in oil-spill modeling, simulating hypothetical or real oil spill scenarios, worldwide. Seven state-of-the-art oil-spill models are critically examined in terms of the met-ocean data used as forcing inputs in the simulation of twenty-three case studies. The results illustrate that most oil spill models are coupled to different resolution, forecasting meteorological and hydrodynamic models, posing, however, limited consideration in the forecasted wave field (expressed as the significant wave height, the wave period, and the Stokes drift) that may affect oil transport, especially at the coastal areas. Moreover, the majority of oil spill models lack any linkage to the background biogeochemical conditions; hence, limited consideration is given to processes such as oil biodegradation, photo-oxidation, and sedimentation. Future advancements in oil-spill modeling should be directed towards the full operational coupling with high-resolution atmospheric, hydrodynamic, wave, and biogeochemical models, improving our understanding of the relative impact of each physical and oil weathering process. Full article
(This article belongs to the Special Issue Reviews in Physical Oceanography)
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<p>General concept of Operational oil-spill modeling.</p> Full article ">
23 pages, 7520 KiB  
Article
Evaluation of Coastal Protection Strategies at Costa da Caparica (Portugal): Nourishments and Structural Interventions
by Francisco Sancho
J. Mar. Sci. Eng. 2023, 11(6), 1159; https://doi.org/10.3390/jmse11061159 - 31 May 2023
Cited by 3 | Viewed by 2264
Abstract
Costa da Caparica beach, in Portugal, has suffered from chronic erosion for the last 50 years, a phenomenon that has been countered by various management interventions. This study aims at comparing sixteen possible interventions, thus identifying the most effective one(s) in terms of [...] Read more.
Costa da Caparica beach, in Portugal, has suffered from chronic erosion for the last 50 years, a phenomenon that has been countered by various management interventions. This study aims at comparing sixteen possible interventions, thus identifying the most effective one(s) in terms of reducing beach erosion or even promoting beach accretion. This exercise is achieved using a one-line shoreline evolution model, calibrated with in situ field data, forced by local wave conditions. The target management period is 25 years. In the calibration phase, it is found that the annual mean alongshore net sediment transport along the 24 km sandy coast is variable in direction and magnitude, but it is mostly smaller than ±50 × 103 m3/year. This net transport results from the imbalance of northward/southward-directed bulk transports of circa tenfold-larger magnitudes. This affects the overall sediment balance at the urban beaches, as well as the effectiveness of the intervention strategies. The results show that the present management strategy is effective in holding the shoreline position, although deploying the same nourishment volume but over a shorter area could lead to better results. The best solutions, which are capable of promoting beach accretion, implicate the lengthening of the terminal groin at the northern extremity of the beach. The results from this study can support decision makers in identifying the most appropriate management action, not just locally but also at other coastal regions where similar problems persist and the same methodology could be applied. Full article
(This article belongs to the Special Issue Sediment Dynamics in Artificial Nourishments)
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<p>Study area with place names, reference or baseline (deep orange) and 8, 16 and 30 m isobaths. (Adapted from geomar.hidrografico.pt, accessed on 20 May 2023, using ESRI World Imagery).</p> Full article ">Figure 2
<p>Images of Costa da Caparica beaches: <span class="html-italic">Praia de São João da Caparica</span> (<b>left</b>), and central urban beach, confined by groynes and an inland revetment (<b>right</b>).</p> Full article ">Figure 3
<p>Groin’s identification at Costa da Caparica beach.</p> Full article ">Figure 4
<p>Modelled (calibrated; red) and measured (green) shoreline positions in 2004 over the entire study area. Initial shoreline configuration in 1979 (dark blue) and hard structures (groins and coastal revetment; black). The limits of the sectors referred to in <a href="#jmse-11-01159-t003" class="html-table">Table 3</a> are market in light blue, at the top.</p> Full article ">Figure 5
<p>Modelled (red) and measured (green) shoreline positions in 2018 at Costa da Caparica beaches. Initial shoreline configuration in 2004 (black thin line) and hard structures (groins and coastal revetment; black thick line). (<b>a</b>) Results from post-calibration simulation; (<b>b</b>) results from post-verification simulation.</p> Full article ">Figure 6
<p>Modelled shoreline positions 20 years past initial configuration (2018) at Costa da Caparica beaches for intervention strategies A1, A2, A3 and A4.</p> Full article ">Figure 7
<p>Time-evolution of mean shoreline position relative to initial position at Costa da Caparica beaches for intervention strategies A1, A2, A3 and A4.</p> Full article ">Figure 8
<p>Modelled shoreline positions 20 years past initial configuration (2018) at Costa da Caparica beaches for intervention strategies: (<b>a</b>) A1, B2, B2 and B6; (<b>b</b>) B1, B3.1, B3.2 and B3.3; (<b>c</b>) A1, B4.1, B4.2 and B4.3; (<b>d</b>) A3, B5.1, B5.2 and B5.3.</p> Full article ">Figure 9
<p>Time-evolution of mean shoreline position relative to initial configuration at Costa da Caparica beaches for intervention strategies: (<b>a</b>) A1, B1, B2 and B6; (<b>b</b>) B1, B3.1, B3.2 and B3.3; (<b>c</b>) A1, B4.1, B4.2 and B4.3; (<b>d</b>) A3, B5.1, B5.2 and B5.3.</p> Full article ">Figure 10
<p>Alongshore annual mean sediment flux spatial distribution, for the post-calibration model simulations (1979–2004).</p> Full article ">Figure 11
<p>Annual mean net sediment fluxes for all intervention scenarios at: (<b>a</b>) x = 24,000 m; (<b>b</b>) x = 19,500 m.</p> Full article ">Figure 12
<p>Accumulated sediment budget, artificial nourishments and average shoreline advance/retreat at Costa da Caparica cell (comprised within 19,500 ≤ x ≤ 24,000 m), for the various intervention strategies.</p> Full article ">
23 pages, 10377 KiB  
Article
Ocean Surface Gravity Wave Evolution during Three Along-Shelf Propagating Tropical Cyclones: Model’s Performance of Wind-Sea and Swell
by Chu-En Hsu, Christie A. Hegermiller, John C. Warner and Maitane Olabarrieta
J. Mar. Sci. Eng. 2023, 11(6), 1152; https://doi.org/10.3390/jmse11061152 - 31 May 2023
Cited by 3 | Viewed by 2171
Abstract
Despite recent advancements in ocean–wave observations, how a tropical cyclone’s (TC’s) track, intensity, and translation speed affect the directional wave spectra evolution is poorly understood. Given the scarcity of available wave spectral observations during TCs, there are few studies about the performance of [...] Read more.
Despite recent advancements in ocean–wave observations, how a tropical cyclone’s (TC’s) track, intensity, and translation speed affect the directional wave spectra evolution is poorly understood. Given the scarcity of available wave spectral observations during TCs, there are few studies about the performance of spectral wave models, such as Simulating Waves Nearshore (SWAN), under various TC scenarios. We combined the National Data Buoy Center observations and numerical model hindcasts to determine the linkages between wave spectrum evolution and TC characteristics during hurricanes Matthew 2016, Dorian 2019, and Isaias 2020. Five phases were identified in the wave spectrogram based on the normalized distance to the TC, the sea–swell separation frequency, and the peak wave frequency, indicating how the wave evolution relates to TC characteristics. The wave spectral structure and SWAN model’s performance for wave energy distribution within different phases were identified. The TC intensity and its normalized distance to a buoy were the dominant factors in the energy levels and peak wave frequencies. The TC heading direction and translation speed were more likely to impact the durations of the phases. TC translation speeds also influenced the model’s performance on swell energy. The knowledge gained in this work paves the way for improving model’s performance during severe weather events. Full article
(This article belongs to the Special Issue Extreme Coastal and Ocean Waves)
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<p>(<b>a</b>) Track data of the three TCs from the International Best Track Archive for Climate Stewardship (IBTrACS) dataset and the computational grids and bathymetry (red: Matthew; green: Dorian; blue: Isaias; black: computational grids; hypsometric map: water depth). Locations of the TCs every 6 h (dots position) with the colormap of circles representing the maximum sustained wind during hurricanes (<b>b</b>) Matthew 2016; (<b>c</b>) Dorian 2019, and (<b>d</b>) Isaias 2020, respectively.</p> Full article ">Figure 2
<p>The three curves represent the IBTrACS data of hurricanes Matthew, Dorian, and Isaias. W1 to W7 (yellow squares) represent NDBC wave buoys. The mean water depth (<math display="inline"><semantics> <mrow> <mi>h</mi> </mrow> </semantics></math>) at each buoy is listed in the legend.</p> Full article ">Figure 3
<p>One-dimensional wave energy spectra during three hurricanes at the timing when a bi-modal wave system was observed. Panels (<b>a</b>–<b>c</b>) show the spectrum during hurricanes Matthew, Dorian, and Isaias, respectively. WH–2001, GH–2001, and HW–2012 refer to the sea–swell separation frequencies of Wang and Hwang (2001) [<a href="#B7-jmse-11-01152" class="html-bibr">7</a>], Gilhousen and Hervey (2001) [<a href="#B10-jmse-11-01152" class="html-bibr">10</a>], and Hwang et al. (2012) [<a href="#B8-jmse-11-01152" class="html-bibr">8</a>], respectively. The black dashed lines are the wind-sea peak frequency (Equation (2)).</p> Full article ">Figure 4
<p>(<b>a</b>) Two-dimensional frequency–time logarithmic wave energy spectrograms, peak wave frequency (<math display="inline"><semantics> <mrow> <msub> <mrow> <mi>f</mi> </mrow> <mrow> <mi>P</mi> </mrow> </msub> </mrow> </semantics></math>, red dots), and sea–swell separation frequency (<math display="inline"><semantics> <mrow> <msub> <mrow> <mi>f</mi> </mrow> <mrow> <mi>S</mi> </mrow> </msub> </mrow> </semantics></math>, white curve) derived with the observed data at NDBC buoy 41008 and the formula of GH-2001 during hurricanes Matthew 2016 (<b>top</b>), Dorian 2019 (<b>middle</b>), and Isaias 2020 (<b>bottom</b>). (<b>b</b>) The normalized computational errors of 2-dimensional frequency–time spectrograms between NDBC observations and COAWST simulations at NDBC buoy 41008. The yellow dashed lines, the numbers (1–5), and the alphabet (D) indicate the identified phases. The vertical dotted red lines indicate the timing of extracted snapshot in the following section. We scaled the upper and lower boundaries of the color bar to make it symmetric (i.e., white represents complete consistency with observation) and to make the overestimated frequencies visible. We normalized the computational error by dividing with the integrated energy through the instantaneous frequency spectrum to eliminate the effects of total wave energy variation.</p> Full article ">Figure 5
<p>Two-dimensional directional wave spectra at NDBC buoy 41008 during Matthew. Panels (<b>a1</b>–<b>c1</b>) depict NDBC-observed data. Panels (<b>a2</b>–<b>c2</b>) show instantaneous normalized error (divided by the observed total energy) of SWAN, where positive values mean overestimation (red) and negative values mean underestimation (blue). The area inside the dashed line parabola is the wind sea (wave age <math display="inline"><semantics> <mrow> <mo>≤</mo> </mrow> </semantics></math> 0.83).</p> Full article ">Figure 6
<p>Two-dimensional directional wave spectra at NDBC buoy 41008 during Dorian. Panels (<b>a1</b>–<b>c1</b>) depict NDBC-observed data. Panels (<b>a2</b>–<b>c2</b>) show instantaneous normalized error (divided by the observed total energy) of SWAN, where positive values mean overestimation (red) and negative values mean underestimation (blue). The area inside the dashed line parabola is the wind sea (wave age <math display="inline"><semantics> <mrow> <mo>≤</mo> </mrow> </semantics></math> 0.83).</p> Full article ">Figure 7
<p>Two-dimensional directional wave spectra at NDBC buoy 41008 during Isaias. Panels (<b>a1</b>–<b>c1</b>) depict NDBC-observed data. Panels (<b>a2</b>–<b>c2</b>) show instantaneous normalized error (divided by the observed total energy) of SWAN, where positive values mean overestimation (red) and negative values mean underestimation (blue). The area inside the dashed line parabola is the wind sea (wave age <math display="inline"><semantics> <mrow> <mo>≤</mo> </mrow> </semantics></math> 0.83).</p> Full article ">Figure 8
<p>Map of normalized wind speed and normalized zero-moment wave height (with respect to the instantaneous maxima) within phases 3 and 4 during Matthew, Dorian, and Isaias. Top panels represent snapshots within phase 3 during Matthew, Dorian, and Isaias, respectively. Bottom panels represent snapshots within phase 4 during Matthew, Dorian, and Isaias, respectively. The colormap shows the normalized zero-moment wave height; gray arrows represent the mean wind vectors; and red arrows represent the mean surface water wave vectors. The black star denotes the position of NDBC buoy 41008.</p> Full article ">Figure A1
<p>Comparison of zero-moment wave height (<math display="inline"><semantics> <mrow> <msub> <mrow> <mi>H</mi> </mrow> <mrow> <mi>m</mi> <mn>0</mn> </mrow> </msub> </mrow> </semantics></math>) time series between NDBC observation (red stars) and ROMS–SWAN model results (blue curve) with corresponding skills and RMSEs. The time format is mmddTHH, where mm is month; dd is date; HH is time hour).</p> Full article ">Figure A2
<p>Comparison of mean wave direction (<math display="inline"><semantics> <mrow> <msub> <mrow> <mi>θ</mi> </mrow> <mrow> <mi>M</mi> </mrow> </msub> </mrow> </semantics></math>) time series between NDBC observation (red stars) and ROMS–SWAN model results (blue curve) with corresponding skills and RMSEs. The time format is mmddTHH, where mm is month; dd is date; HH is time hour).</p> Full article ">Figure A3
<p>Comparison of peak wave period (<math display="inline"><semantics> <mrow> <msub> <mrow> <mi>T</mi> </mrow> <mrow> <mi>P</mi> </mrow> </msub> </mrow> </semantics></math>) time series between NDBC observation (red stars) and ROMS–SWAN model results (blue curve) with corresponding skills and RMSEs. The time format is mmddTHH, where mm is month; dd is date; HH is time hour).</p> Full article ">
18 pages, 695 KiB  
Article
Wind-Assisted Ship Propulsion: Matching Flettner Rotors with Diesel Engines and Controllable Pitch Propellers
by Veronica Vigna and Massimo Figari
J. Mar. Sci. Eng. 2023, 11(5), 1072; https://doi.org/10.3390/jmse11051072 - 18 May 2023
Cited by 8 | Viewed by 3031
Abstract
The harvesting of wind energy and its transformation into a thrust force for ship propulsion are gaining in popularity due to the expected benefit in fuel consumption and emission reductions. To exploit these benefits, a proper matching between the conventional diesel engine-screw propeller [...] Read more.
The harvesting of wind energy and its transformation into a thrust force for ship propulsion are gaining in popularity due to the expected benefit in fuel consumption and emission reductions. To exploit these benefits, a proper matching between the conventional diesel engine-screw propeller propulsion plant and the wind-assisted plant is key. This paper aims to present a method and a code for the preliminary sizing of a ship propulsion plant based on a diesel engine, a controllable pitch propeller, and one or more Flettner rotors. A mathematical model describing the behaviour of the rotor in terms of propulsive thrust and power is proposed. The rotor model has been integrated into an existing diesel propulsion model in order to evaluate the ship’s fuel consumption. The ship’s propulsion model is written in a parametric form with respect to the following design parameters: ship dimensions and resistance-speed curve, propeller diameter, engine power, rotor geometry, and true wind conditions. The methodology helps in evaluating the engine–propeller working points and eventually the total ship propulsive power, including the power required to spin the rotor. It provides a way to compare wind-assisted propulsive solutions in terms of fuel consumption and CO2 emissions. A 3000-ton Ro-Ro/Pax ferry has been selected as a case study. Results on the parametric analysis of rotor dimensions and propeller pitch optimization are presented. Full article
(This article belongs to the Section Ocean Engineering)
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<p>Wind-assisted ship propulsion modelling flow chart.</p> Full article ">Figure 2
<p>Rotor forces.</p> Full article ">Figure 3
<p>Influence on rotor thrust–ship resistance ratio <math display="inline"><semantics> <mfrac> <msub> <mi>T</mi> <mrow> <mi>F</mi> <mi>R</mi> </mrow> </msub> <mrow> <msub> <mi>R</mi> <mi>H</mi> </msub> <mo>+</mo> <msub> <mi>R</mi> <mi>W</mi> </msub> </mrow> </mfrac> </semantics></math> of the rotor’s geometric dimensions and true wind speed. (<b>a</b>) Influence of rotor’s geometric dimensions on <math display="inline"><semantics> <mfrac> <msub> <mi>T</mi> <mrow> <mi>F</mi> <mi>R</mi> </mrow> </msub> <mrow> <msub> <mi>R</mi> <mi>H</mi> </msub> <mo>+</mo> <msub> <mi>R</mi> <mi>W</mi> </msub> </mrow> </mfrac> </semantics></math> for SR = 3 and wind 15 m/s. (<b>b</b>) Influence of true wind speed on <math display="inline"><semantics> <mfrac> <msub> <mi>T</mi> <mrow> <mi>F</mi> <mi>R</mi> </mrow> </msub> <mrow> <msub> <mi>R</mi> <mi>H</mi> </msub> <mo>+</mo> <msub> <mi>R</mi> <mi>W</mi> </msub> </mrow> </mfrac> </semantics></math>.</p> Full article ">Figure 4
<p>Influence of true wind speed and direction on FR thrust.</p> Full article ">Figure 5
<p>KPI1: power savings percentage due to the use of rotors.</p> Full article ">Figure 6
<p>KPI1 for different ship speeds and <math display="inline"><semantics> <msub> <mi>V</mi> <mrow> <mi>T</mi> <mi>r</mi> <mi>u</mi> <mi>e</mi> <mi>W</mi> <mi>i</mi> <mi>n</mi> <mi>d</mi> </mrow> </msub> </semantics></math> = 15 kn from 100°, SR = 3.</p> Full article ">Figure 7
<p>Engine–propellers matching.</p> Full article ">Figure 8
<p>KPI2 for different ship speeds and <math display="inline"><semantics> <msub> <mi>V</mi> <mrow> <mi>T</mi> <mi>r</mi> <mi>u</mi> <mi>e</mi> <mi>W</mi> <mi>i</mi> <mi>n</mi> <mi>d</mi> </mrow> </msub> </semantics></math> = 15 kn from 100°, SR = 3.</p> Full article ">Figure 9
<p>KPI2: fuel savings percentage due to the use of rotors.</p> Full article ">
31 pages, 7824 KiB  
Review
Digital Twin in the Maritime Domain: A Review and Emerging Trends
by Nuwan Sri Madusanka, Yijie Fan, Shaolong Yang and Xianbo Xiang
J. Mar. Sci. Eng. 2023, 11(5), 1021; https://doi.org/10.3390/jmse11051021 - 10 May 2023
Cited by 19 | Viewed by 8691
Abstract
This paper highlights the development of Digital Twin (DT) technology and its admittance to a variety of applications within the maritime domain in general and surface ships in particular. The conceptual theory behind the evolution of DT is highlighted along with the development [...] Read more.
This paper highlights the development of Digital Twin (DT) technology and its admittance to a variety of applications within the maritime domain in general and surface ships in particular. The conceptual theory behind the evolution of DT is highlighted along with the development of the technology and current progress in practical applications with an exploration of the key milestones in the extension from the electrification of the shipping sector towards the realization of a definitive DT-based system. Existing DT-based applications within the maritime sector are surveyed along with the comprehension of ongoing research work. The development strategy for a formidable DT architecture is discussed, culminating in a proposal of a four-layered DT framework. Considering the importance of DT, an extensive and methodical literature survey has also been carried out, along with a comprehensive scientometric analysis to unveil the methodical footprint of DT in the marine sector, thus leading the way for future work on the design, development and operation of surface vessels using DT applications. Full article
(This article belongs to the Special Issue Maritime Autonomous Surface Ships)
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<p>Reflection of the Original Conceptual Idea of DT by Dr M. Grieves in 2002.</p> Full article ">Figure 2
<p>Difference of data topologies among Digital Model, Digital Shadow, and DT.</p> Full article ">Figure 3
<p>Pathway of Digital Twin Technology within the Shipping Industry.</p> Full article ">Figure 4
<p>Realtime Satellite Data Link of Smart Ships.</p> Full article ">Figure 5
<p>General Functions of a Surface Ship.</p> Full article ">Figure 6
<p>Direct Stakeholders of a Ship DT System.</p> Full article ">Figure 7
<p>Overall Task Distribution of a DT-based Framework.</p> Full article ">Figure 8
<p>Proposed 4-Layer Ship DT Framework.</p> Full article ">Figure 9
<p>The Flowchart Demonstrating the Adopted Scientometric Analytical Process.</p> Full article ">Figure 10
<p>Publication Years-DT in Maritime Applications in WoS Analysis.</p> Full article ">Figure 11
<p>Contribution of Countries Towards DT in Maritime Applications in WoS Analysis.</p> Full article ">Figure 12
<p>Authors’ Contribution on DT in Maritime Applications.</p> Full article ">Figure 13
<p>Authors’ Collaboration on DT in Maritime Applications.</p> Full article ">Figure 14
<p>Visualization of Highest Cited Authors in Respective Clusters with a Threshold of 5.</p> Full article ">Figure 15
<p>Co-citation Reference Network in the Timespan of 2014 to 2022 with 6 Main Clusters.</p> Full article ">Figure 16
<p>CiteSpace Top-50 Keyword Visualization with Top-10 Timeline Nodes.</p> Full article ">
24 pages, 3991 KiB  
Article
Biodiversity of UV-Resistant Bacteria in Antarctic Aquatic Environments
by Daniela Coppola, Chiara Lauritano, Gianluca Zazo, Genoveffa Nuzzo, Angelo Fontana, Adrianna Ianora, Maria Costantini, Cinzia Verde and Daniela Giordano
J. Mar. Sci. Eng. 2023, 11(5), 968; https://doi.org/10.3390/jmse11050968 - 1 May 2023
Cited by 3 | Viewed by 2752
Abstract
Antarctica is an untapped reservoir of bacterial communities, which are able to adapt to a huge variety of strategies to cope with extreme conditions and, therefore, are capable of producing potentially valuable compounds for biotechnological applications. In this study, 31 UV-resistant bacteria collected [...] Read more.
Antarctica is an untapped reservoir of bacterial communities, which are able to adapt to a huge variety of strategies to cope with extreme conditions and, therefore, are capable of producing potentially valuable compounds for biotechnological applications. In this study, 31 UV-resistant bacteria collected from different Antarctic aquatic environments (surface sea waters/ice and shallow lake sediments) were isolated by UV-C assay and subsequently identified. A phylogenetic analysis based on 16S rRNA gene sequence similarities showed that the isolates were affiliated with Proteobacteria, Actinobacteria and Firmicutes phyla, and they were clustered into 15 bacterial genera, 5 of which were Gram negative (Brevundimonas, Qipengyuania, Sphingorhabdus, Sphingobium, and Psychrobacter) and 10 of which were Gram positive (Staphylococcus, Bacillus, Mesobacillus, Kocuria, Gordonia, Rhodococcus, Micrococcus, Arthrobacter, Agrococcus, and Salinibacterium). Strains belonging to Proteobacteria and Actinobacteria phyla were the most abundant species in all environments. The genus Psychrobacter was dominant in all collection sites, whereas bacteria belonging to Actinobacteria appeared to be the most diverse and rich in terms of species among the investigated sites. Many of these isolates (20 of 31 isolates) were pigmented. Bacterial pigments, which are generally carotenoid-type compounds, are often involved in the protection of cells against the negative effects of UV radiation. For this reason, these pigments may help bacteria to successfully tolerate Antarctic extreme conditions of low temperature and harmful levels of UV radiation. Full article
(This article belongs to the Special Issue Biodiversity, Adaptation Strategies, and Opportunities in Extreme Marine Environments)
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<p>Map of Victoria Land, Ross Sea, Antarctica. (<b>a</b>) Collection coastal areas in Tethys Bay (red site), Inexpressible Island (yellow site), and Edmonson Point (blue site), scale bar = 50 km. (<b>b</b>) Collection sites in Tethys Bay: scale bar = 1 km. In each location, collection sites were numbered as indicated in <a href="#jmse-11-00968-t001" class="html-table">Table 1</a>.</p> Full article ">Figure 2
<p>Representative Antarctic UV-resistant bacteria belonging to different genera and showing different pigmentation.</p> Full article ">Figure 3
<p>16S rRNA genes phylogenetic tree created using the Maximum Likelihood method based on the Jukes–Cantor model of Antarctic UV-resistant bacteria isolated from marine and lake environments. The percentage of trees in which the associated taxa clustered together in the bootstrap test (500 replicates) is shown next to the branches (values below 50% are not shown). The tree is drawn to scale, with branch lengths measured in the number of substitutions per site. The analysis involved 1257 positions in the final dataset. All positions containing gaps and missing data were eliminated. The tree was outgrouped with the 16S rRNA gene sequence of <span class="html-italic">Methanocaldococcus jannaschii</span> DSM2661 (NR_074233.1). All accession numbers are in parentheses.</p> Full article ">Figure 4
<p>16S rRNA genes phylogenetic tree created using the Maximum Likelihood method based on the Jukes–Cantor model of Antarctic UV-resistant bacteria isolated from marine and lake environments affiliated with Actinobacteria. The percentage of trees in which the associated taxa clustered together in the bootstrap test (500 replicates) is shown next to the branches (values below 50% are not shown). The tree is drawn to scale, with branch lengths measured in the number of substitutions per site. The analysis involved 1319 positions in the final dataset. All positions containing gaps and missing data were eliminated. The tree was outgrouped with the 16S rRNA gene sequence of <span class="html-italic">Methanocaldococcus jannaschii</span> DSM2661 (NR_074233.1). All accession numbers are in parentheses.</p> Full article ">Figure 5
<p>16S rRNA genes phylogenetic tree created using the Maximum Likelihood method based on the Jukes–Cantor model of Antarctic UV-resistant bacteria isolated from marine and lake environments affiliated with Proteobacteria. The percentage of trees in which the associated taxa clustered together in the bootstrap test (500 replicates) is shown next to the branches (values below 50% are not shown). The tree is drawn to scale, with branch lengths measured in the number of substitutions per site. The analysis involved 1274 positions in the final dataset. All positions containing gaps and missing data were eliminated. The tree was outgrouped with the 16S rRNA gene sequence of <span class="html-italic">Methanocaldococcus jannaschii</span> DSM2661 (NR_074233.1). All accession numbers are in parentheses.</p> Full article ">Figure 6
<p>16S rRNA genes phylogenetic tree created using the Maximum Likelihood method based on the Jukes–Cantor model of Antarctic UV-resistant bacteria isolated from marine and lake environments affiliated with Firmicutes. The percentage of trees in which the associated taxa clustered together in the bootstrap test (500 replicates) is shown next to the branches (values below 50% are not shown). The tree is drawn to scale, with branch lengths measured in the number of substitutions per site. The analysis involved 1321 positions in the final dataset. All positions containing gaps and missing data were eliminated. The tree was outgrouped with the 16S rRNA gene sequence of <span class="html-italic">Methanocaldococcus jannaschii</span> DSM2661 (NR_074233.1). All accession numbers are in parentheses.</p> Full article ">
15 pages, 3774 KiB  
Article
Three-Dimensional-Printed Coral-like Structures as a Habitat for Reef Fish
by Asa Oren, Ofer Berman, Reem Neri, Ezri Tarazi, Haim Parnas, Offri Lotan, Majeed Zoabi, Noam Josef and Nadav Shashar
J. Mar. Sci. Eng. 2023, 11(4), 882; https://doi.org/10.3390/jmse11040882 - 21 Apr 2023
Cited by 4 | Viewed by 3056
Abstract
Coral reefs are three-dimensional biogenic structures that provide habitat for plenty of marine organisms; yet, coral reefs are deteriorating worldwide. Hence, it is essential to identify suitable substitutes for such coral services. This study examines reef fishes’ behavior and reactions to three-dimensional-printed (3DP) [...] Read more.
Coral reefs are three-dimensional biogenic structures that provide habitat for plenty of marine organisms; yet, coral reefs are deteriorating worldwide. Hence, it is essential to identify suitable substitutes for such coral services. This study examines reef fishes’ behavior and reactions to three-dimensional-printed (3DP) corals based on scanned Stylophora pistillata, as well as modified 3DP models. In particular, fishes’ unresponsiveness to the color, shape, morphology, and material of 3DP models both in vitro and in situ experiments was investigated. Coral reef fishes responded to the 3DP corals and demonstrated their usage in a range of services. Moreover, a greater number of fish species interacted more with 3DP models than they did with live corals. Furthermore, specific reef fish species, such as Sea Goldies (Pseudanthias squamipinnis), showed a preference for specific 3DP coral color, and other species demonstrated preferences for specific 3DP model shapes. The current study results show that three-dimensional-printed coral models can substitute for live corals for certain types of reef fish services. Full article
(This article belongs to the Topic Marine Ecology, Environmental Stress and Management)
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<p>The experimental setup. A tank with a holding chamber (<b>A</b>) in its center. After acclimation, a gate was raised and the fish were allowed to choose between two shelters set on the far sides of the tank. Circles around the shelters illustrate the area in which fishes were counted.</p> Full article ">Figure 2
<p>Reflectance spectra taken from the shelters.</p> Full article ">Figure 3
<p>Tabular structure manipulation of the original model (<b>A</b>) before and (<b>B</b>) after.</p> Full article ">Figure 4
<p>Fishes’ preference of shelter structure experiment set-up. Note 5 yellow coral models, each with their own structure.</p> Full article ">Figure 5
<p>Fish interacting with artificial models. (<b>A</b>) <span class="html-italic">Bodianus anthioides</span> interacting with the brown model. (<b>B</b>) <span class="html-italic">Corythoichthys cf schultzi</span> interacting with the yellow model. (<b>C</b>) <span class="html-italic">Pseudanthias squamipinnis</span> interacting with the blue model. (<b>D</b>) <span class="html-italic">Chromis viridis</span> interacting with the brown model. (<b>E</b>) <span class="html-italic">Neopomacentrus miryae</span> interacting with the yellow model. (<b>F</b>) <span class="html-italic">Meiacanthus nigrolineatus</span> interacting with the orange model.</p> Full article ">Figure 6
<p>Time spent within each shelter. Means ± standard deviation. Percentage of time <span class="html-italic">P. squamipinnis</span> spent inside or less than 2 cm away from various models of <span class="html-italic">S. pistillata.</span> F<sub>(5,473)</sub> = 3.8544; <span class="html-italic">p</span> = 0.00196; two-way ANOVA. A Tukey post hoc test was used to show a significant difference in fish shelter preference; <span class="html-italic">p</span> &lt; 0.05.</p> Full article ">Figure 7
<p>Hierarchal chart of fishes’ preference based on the number of fishes that preferred each model in each setting. The direction of the arrows indicates the most dominant to the less dominant models, the width of the arrows is proportional to the percentage of dominance, and the numbers next to each arrow are the ratio of more dominant to the less dominant models.</p> Full article ">Figure 8
<p>Richness of fish species interacting with the various models. The natural structures observed are shown in the marked area. DM is the abbreviation for <span class="html-italic">Dascyllus marginatus</span>. For a detailed explanation of the codes of shelters and an abbreviation explanation for the rest of the species, see the <a href="#app1-jmse-11-00882" class="html-app">Supplementary Material</a>.</p> Full article ">
29 pages, 23244 KiB  
Article
Analysis of the Mooring Effects of Future Ultra-Large Container Vessels (ULCV) on Port Infrastructures
by Sara Sanz Sáenz, Gabriel Diaz-Hernandez, Lutz Schweter and Pieter Nordbeck
J. Mar. Sci. Eng. 2023, 11(4), 856; https://doi.org/10.3390/jmse11040856 - 18 Apr 2023
Cited by 7 | Viewed by 2399
Abstract
The size of container vessels is continuously growing, always exceeding expectations. Port authorities and terminals need to constantly adapt and face challenges related to maritime infrastructure, equipment, and operations, as these are the principal areas affected by the future Ultra Large Container Vessels [...] Read more.
The size of container vessels is continuously growing, always exceeding expectations. Port authorities and terminals need to constantly adapt and face challenges related to maritime infrastructure, equipment, and operations, as these are the principal areas affected by the future Ultra Large Container Vessels (ULCVs). Maneuvring areas are at their limits, and mooring equipment is at an increased risk of being overloaded. This study aims to analyze the limitations that present mooring systems may face when ULCVs are subjected to wind and passing-ship forces exerted by a future ULCV and wind forces through Dynamic Mooring Analysis (DMA). A hypothetical and massive future ULCV with a capacity of 40,000 TEU is compared to the Emma Maersk, which is a present vessel that regularly calls at container terminals. The Emma Maersk, with its current mooring arrangement, experiences higher motion than future ULCVs, which experience higher forces but are also moored with more and stronger lines. This translates into considerably higher loads in the mooring system, potentially compromising safe mooring conditions at the terminal. Mitigating measures are proposed in the study to face these limitations. In addition, the study explores the potential of new and innovative mooring technologies, such as high-strength synthetic ropes and smart mooring systems, to address the challenges posed by ULCVs. A container terminal at the Port of Rotterdam, Europe’s largest sea port, has been analyzed as a case study. The terminal is located next to a busy fairway that leads to other container terminals, justifying the need to analyze both wind and passing-ship effects on moored ships. Full article
(This article belongs to the Special Issue Advances in Ship and Marine Hydrodynamics)
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<p>DMA modeling approach.</p> Full article ">Figure 2
<p>Port of Rotterdam.</p> Full article ">Figure 3
<p>Emma Maersk selected mooring arrangement. Dimensions in m.</p> Full article ">Figure 4
<p>Future vessel selected mooring arrangement. Dimensions in m.</p> Full article ">Figure 5
<p>Selection of parameters for the Dynamic Mooring Analysis.</p> Full article ">Figure 6
<p>Passing speed AIS data projected in the port channel, between April 2018 and March 2019.</p> Full article ">Figure 7
<p>Passing speed histogram in knots for vessels exceeding 370 m between April 2018 and March 2019 at a passing distances greater than 200 m from the terminal line.</p> Full article ">Figure 8
<p>Measured wind rose at Container Terminal.</p> Full article ">Figure 9
<p>Significant sway motion results for dynamic wind conditions with and without ShoreTension<sup>®</sup>.</p> Full article ">Figure 10
<p>Significant surge motion results for dynamic wind conditions with and without ShoreTension<sup>®</sup>.</p> Full article ">Figure 11
<p>Maximum line loads results for dynamic wind conditions with and without ShoreTension<sup>®</sup>.</p> Full article ">Figure 12
<p>Maximum line loads and angle distribution in the Emma Maersk.</p> Full article ">Figure 13
<p>Maximum line loads and angle distribution in the future vessel.</p> Full article ">Figure 14
<p>Bollard load results for dynamic wind conditions with and without ShoreTension<sup>®</sup>.</p> Full article ">Figure 15
<p>Maximum surge motion results for the Emma Maersk and the future vessel subjected to passing-ship conditions.</p> Full article ">Figure 16
<p>Maximum sway motion results for the Emma Maersk and the future vessel subjected to passing-ship conditions.</p> Full article ">Figure 17
<p>Maximum line load results for the Emma Maersk and future vessel subjected to passing-ship conditions.</p> Full article ">Figure 18
<p>Maximum bollard loads results for the Emma Maersk and the future vessel subjected to passing-ship conditions.</p> Full article ">Figure 19
<p>Maximum surge motion results for the Emma Maersk and the future vessel subjected to passing-ship conditions with ShoreTension<sup>®</sup>.</p> Full article ">Figure 20
<p>Maximum sway motion results for the Emma Maersk and the future vessel subjected to passing-ship conditions with ShoreTension<sup>®</sup>.</p> Full article ">Figure 21
<p>Position of the 60 t ShoreTension<sup>®</sup> modules for the Emma Maersk (red lines).</p> Full article ">Figure 22
<p>Position of the 60 t ShoreTension<sup>®</sup> modules for the future vessel (red lines).</p> Full article ">Figure 23
<p>Future vessel position of the 100 <span class="html-italic">t</span> ShoreTension<sup>®</sup> when replacing lines in the breast lines (red lines).</p> Full article ">Figure 24
<p>Future vessel position of the 100 t ShoreTension<sup>®</sup> when replacing lines in the spring lines (red lines).</p> Full article ">Figure 25
<p>Example of the uncrossing mooring arrangement in the future vessel.</p> Full article ">
33 pages, 23865 KiB  
Article
Visualization of Underwater Radiated Noise in the Near- and Far-Field of a Propeller-Hull Configuration Using CFD Simulation Results
by Julian Kimmerl and Moustafa Abdel-Maksoud
J. Mar. Sci. Eng. 2023, 11(4), 834; https://doi.org/10.3390/jmse11040834 - 15 Apr 2023
Cited by 5 | Viewed by 2740
Abstract
Underwater radiated noise is part of the anthropogenic emissions into the environment and as such a pressing problem for the preservation of the marine ecosystem. In order to direct attention to the most relevant noise sources associated with ships it is crucial to [...] Read more.
Underwater radiated noise is part of the anthropogenic emissions into the environment and as such a pressing problem for the preservation of the marine ecosystem. In order to direct attention to the most relevant noise sources associated with ships it is crucial to precisely determine the local origins of the acoustic emissions. As acoustics are by nature perceived through a very subjective auditory perception, visual post-processing support is required in engineering applications to assess the impact on structures and to create an understanding of the overall noise field geometrically, topologically, and directionally. In the context of CFD simulations, this may be achieved by considering the pressure pulses on domain boundary surfaces or passive surfaces, or by evaluating various volumetric information, such as Proudman acoustic sources or the Lighthill stress tensor, which is the fundamental input for many acoustic analogies including the Ffowcs-Williams-Hawkings method. For a propeller-hull configuration, the acoustic emissions from modeled and scale-resolved turbulence two-phase CFD analyses are evaluated in detail with different visualization methods. It is shown that the spatial distribution information of frequency domain pressure pulses, and the corresponding complex phase angles on specific passive geometries, as well as the Lighthill stress tensor may be utilized to create a better understanding of underwater acoustics. This allows the identification of source types and their respective excitation of the hull and emission characteristics of the hydrodynamic sources into the fluid domain, as well as the effect of the CFD simulation domain geometry extent. Full article
(This article belongs to the Section Ocean Engineering)
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<p>Test case 3D-model.</p> Full article ">Figure 2
<p>Midplane of cavitation tunnel and quasi-infinite domain.</p> Full article ">Figure 3
<p>First 4 harmonic propeller blade frequencies incompressible hull pressure above propeller, comparison of domain extent, RANS without cavitation.</p> Full article ">Figure 4
<p>First 4 harmonic propeller blade frequencies incompressible hull pressure above propeller, comparison of domain extent, RANS with cavitation.</p> Full article ">Figure 5
<p>First 4 harmonic propeller blade frequencies incompressible hull pressure above propeller, comparison of domain extent, LES with cavitation.</p> Full article ">Figure 6
<p>Difference between cavitating and non-cavitating simulation.</p> Full article ">Figure 7
<p>Phase information on hull at the first 4 harmonic propeller blade harmonic frequencies, comparison of domain extent, and RANS.</p> Full article ">Figure 8
<p>Phase information on hull at the first 4 harmonic propeller blade harmonic frequencies, comparison of domain extent, RANS with cavitation.</p> Full article ">Figure 9
<p>Phase information on hull at the first 4 harmonic propeller blade harmonic frequencies, comparison of domain extent, LES with cavitation.</p> Full article ">Figure 10
<p>First 4 POD modes of pressure time data set on hull above propeller, comparison of domain extent, RANS.</p> Full article ">Figure 11
<p>First 4 POD modes of pressure time data set on hull above propeller, comparison of domain extent, RANS with cavitation.</p> Full article ">Figure 12
<p>First 4 POD modes of pressure time data set on hull above propeller, comparison of domain extent, LES with cavitation.</p> Full article ">Figure 13
<p>POD mode energies of the first 5 modes.</p> Full article ">Figure 14
<p>Comparison of Proudman acoustic power 3D isovalue around propulsor and rudder.</p> Full article ">Figure 15
<p>Second order statistical moments of pressure time history.</p> Full article ">Figure 16
<p>Near Wall Q-criterion <math display="inline"><semantics> <mrow> <mi>Q</mi> <mo>=</mo> <mn>5</mn> <mo>⋅</mo> <msup> <mrow> <mn>10</mn> </mrow> <mn>4</mn> </msup> <msup> <mrow> <mrow> <mtext> </mtext> <mi mathvariant="normal">s</mi> </mrow> </mrow> <mrow> <mo>−</mo> <mn>2</mn> </mrow> </msup> </mrow> </semantics></math> isosurface on shaft brackets, propeller, and rudder.</p> Full article ">Figure 17
<p>Comparison of isosurfaces of turbulent kinetic energy <math display="inline"><semantics> <mrow> <mi>k</mi> <mo>=</mo> <mn>1.5</mn> <msup> <mrow> <mrow> <mtext> </mtext> <mi mathvariant="normal">m</mi> </mrow> </mrow> <mn>2</mn> </msup> <mo>/</mo> <msup> <mi mathvariant="normal">s</mi> <mn>2</mn> </msup> </mrow> </semantics></math> (purple), second order statistical moments of pressure time history <math display="inline"><semantics> <mrow> <msup> <mrow> <mover accent="true"> <msup> <mi>p</mi> <mo>′</mo> </msup> <mo stretchy="true">¯</mo> </mover> </mrow> <mn>2</mn> </msup> <mo>=</mo> <mn>1</mn> <mo>⋅</mo> <msup> <mrow> <mn>10</mn> </mrow> <mn>6</mn> </msup> <msup> <mrow> <mrow> <mtext> </mtext> <mi>Pa</mi> </mrow> </mrow> <mn>2</mn> </msup> <mtext> </mtext> </mrow> </semantics></math> (orange), and Q-criterion <math display="inline"><semantics> <mrow> <mi>Q</mi> <mo>=</mo> <mn>5</mn> <mo>⋅</mo> <msup> <mrow> <mn>10</mn> </mrow> <mn>4</mn> </msup> <msup> <mrow> <mrow> <mtext> </mtext> <mi mathvariant="normal">s</mi> </mrow> </mrow> <mrow> <mo>−</mo> <mn>2</mn> </mrow> </msup> </mrow> </semantics></math> (green) around rotating mesh region and sliding mesh interface.</p> Full article ">Figure 18
<p>Instantaneous pressure pulses <math display="inline"><semantics> <mrow> <msup> <mi>p</mi> <mo>′</mo> </msup> </mrow> </semantics></math> in <math display="inline"><semantics> <mrow> <mrow> <mo>[</mo> <mrow> <mi>Pa</mi> </mrow> <mo>]</mo> </mrow> </mrow> </semantics></math> at arbitrary converged timestep on the midplane.</p> Full article ">Figure 19
<p>Instantaneous pressure <math display="inline"><semantics> <mi>p</mi> </semantics></math> in <math display="inline"><semantics> <mrow> <mrow> <mo>[</mo> <mrow> <mi>Pa</mi> </mrow> <mo>]</mo> </mrow> </mrow> </semantics></math> at arbitrary converged timestep on midplane through the propeller and corresponding Lighthill stress tensor magnitude <math display="inline"><semantics> <mrow> <mrow> <mo>|</mo> <mrow> <msub> <mi>T</mi> <mrow> <mi>i</mi> <mi>j</mi> </mrow> </msub> </mrow> <mo>|</mo> </mrow> </mrow> </semantics></math> in <math display="inline"><semantics> <mrow> <mo stretchy="false">[</mo> <mi>Kg</mi> <mo>/</mo> <msup> <mrow> <mi>ms</mi> </mrow> <mn>2</mn> </msup> </mrow> </semantics></math> ].</p> Full article ">Figure 20
<p>Fluid compressive stress tensor <math display="inline"><semantics> <mrow> <msub> <mi>P</mi> <mrow> <mi>i</mi> <mi>j</mi> </mrow> </msub> </mrow> </semantics></math> in <math display="inline"><semantics> <mrow> <mo stretchy="false">[</mo> <mi>Kg</mi> <mo>/</mo> <msup> <mrow> <mi>ms</mi> </mrow> <mn>2</mn> </msup> </mrow> </semantics></math> ].</p> Full article ">Figure 21
<p>Perturbation stress tensor <math display="inline"><semantics> <mrow> <msubsup> <mi>P</mi> <mrow> <mi>i</mi> <mi>j</mi> </mrow> <mo>′</mo> </msubsup> </mrow> </semantics></math> in <math display="inline"><semantics> <mrow> <mo stretchy="false">[</mo> <mi>Kg</mi> <mo>/</mo> <msup> <mrow> <mi>ms</mi> </mrow> <mn>2</mn> </msup> </mrow> </semantics></math> ].</p> Full article ">Figure 22
<p>Perturbation stress tensor <math display="inline"><semantics> <mrow> <msubsup> <mi>P</mi> <mrow> <mi>i</mi> <mi>j</mi> </mrow> <mo>′</mo> </msubsup> </mrow> </semantics></math> magnitude (<b>a</b>) and magnitude of off-diagonal components (<b>b</b>,<b>c</b>) on midplane in <math display="inline"><semantics> <mrow> <mo stretchy="false">[</mo> <mi>Kg</mi> <mo>/</mo> <msup> <mrow> <mi>ms</mi> </mrow> <mn>2</mn> </msup> </mrow> </semantics></math> ].</p> Full article ">Figure 23
<p>Lighthill stress tensor <math display="inline"><semantics> <mrow> <msub> <mi>T</mi> <mrow> <mi>i</mi> <mi>j</mi> </mrow> </msub> </mrow> </semantics></math> in <math display="inline"><semantics> <mrow> <mo stretchy="false">[</mo> <mi>Kg</mi> <mo>/</mo> <msup> <mrow> <mi>ms</mi> </mrow> <mn>2</mn> </msup> </mrow> </semantics></math> ].</p> Full article ">Figure 24
<p>Lighthill stress tensor <math display="inline"><semantics> <mrow> <msub> <mi>T</mi> <mrow> <mi>i</mi> <mi>j</mi> </mrow> </msub> </mrow> </semantics></math> magnitude of off-diagonal components in <math display="inline"><semantics> <mrow> <mo stretchy="false">[</mo> <mi>Kg</mi> <mo>/</mo> <msup> <mrow> <mi>ms</mi> </mrow> <mn>2</mn> </msup> </mrow> </semantics></math> ].</p> Full article ">Figure 25
<p>Definition of the spherical coordinate system for the directivity investigation.</p> Full article ">Figure 26
<p>Directivity of harmonic frequencies on a passive sphere around the propeller.</p> Full article ">Figure 27
<p>Directivity of harmonic frequencies on the passive rectangular box around propeller-hull combination.</p> Full article ">Figure 28
<p>Complex phase angle information on the passive sphere from <a href="#jmse-11-00834-f026" class="html-fig">Figure 26</a>.</p> Full article ">Figure 29
<p>Complex phase angle information at <math display="inline"><semantics> <mrow> <msub> <mi>f</mi> <mrow> <mi>Z</mi> <mo>=</mo> <mn>1</mn> </mrow> </msub> </mrow> </semantics></math> on the passive rectangular box shown in <a href="#jmse-11-00834-f027" class="html-fig">Figure 27</a>.</p> Full article ">
19 pages, 15171 KiB  
Article
Punctiform Breakup and Initial Oceanization in the Central Red Sea Rift
by Ya-Di Sang, Bakhit M. T. Adam, Chun-Feng Li, Liang Huang, Yong-Lin Wen, Jia-Ling Zhang and Yu-Tao Liu
J. Mar. Sci. Eng. 2023, 11(4), 808; https://doi.org/10.3390/jmse11040808 - 10 Apr 2023
Cited by 5 | Viewed by 2312
Abstract
The Central Red Sea Rift is a natural laboratory to study the transition from rifting to spreading. Based on new reflection seismic profiles and gravity modeling, we examined the crustal structure, tectonic evolution, breakup mechanism, and future evolution of the Central Red Sea [...] Read more.
The Central Red Sea Rift is a natural laboratory to study the transition from rifting to spreading. Based on new reflection seismic profiles and gravity modeling, we examined the crustal structure, tectonic evolution, breakup mechanism, and future evolution of the Central Red Sea Rift. Along this rift axis, the breakup of continental lithosphere is discontinuous and the oceanic crust is limited to the axial deeps. The punctiform breakup and formation of deeps is assisted by mantle upwelling and topographic uplift, but the nucleation is directly controlled by the normal-fault system. The discontinuities spaced between axial deeps within the relatively continuous central troughs are presently axial domes or highs and will evolve into new deeps with tectonic subsidence. Isolated deeps will grow and connect with each other to become a continuous central trough, before transitioning into a unified spreading center. Full article
(This article belongs to the Special Issue Recent Advances in Geological Oceanography II)
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<p>(<b>a</b>) Tectonic settings and the magnetic anomaly map of the Red Sea Rift. Bathymetry data are from the GEBCO Compilation Group 2021 (<a href="https://www.gebco.net" target="_blank">https://www.gebco.net</a>, accessed on 3 September 2021); magnetic anomaly data are from EMAG2 (<a href="https://ngdc.noaa.gov/geomag/emag2.html" target="_blank">https://ngdc.noaa.gov/geomag/emag2.html</a>, accessed on 2 March 2020). Grey arrows show the plate movement of the Arabian Plate relative to the Nubia Plate, and the direction and velocity are calculated from the model MORVEL [<a href="#B40-jmse-11-00808" class="html-bibr">40</a>]. Grey dashed box shows the area of <a href="#jmse-11-00808-f001" class="html-fig">Figure 1</a>b. (<b>b</b>) Bathymetry of the Central Red Sea. Red lines show the locations of the reflection seismic profiles used in this study, the black dots show sites 225–228 on the DSDP Leg 23 [<a href="#B24-jmse-11-00808" class="html-bibr">24</a>,<a href="#B41-jmse-11-00808" class="html-bibr">41</a>,<a href="#B42-jmse-11-00808" class="html-bibr">42</a>,<a href="#B43-jmse-11-00808" class="html-bibr">43</a>], and the white lines represent the “discontinuities” between axial deeps within the trough.</p> Full article ">Figure 2
<p>Stratigraphy of the Red Sea. Basement composition and stratigraphy refer to previous geological studies and DSDP drill results [<a href="#B17-jmse-11-00808" class="html-bibr">17</a>,<a href="#B45-jmse-11-00808" class="html-bibr">45</a>,<a href="#B46-jmse-11-00808" class="html-bibr">46</a>,<a href="#B48-jmse-11-00808" class="html-bibr">48</a>,<a href="#B54-jmse-11-00808" class="html-bibr">54</a>,<a href="#B55-jmse-11-00808" class="html-bibr">55</a>,<a href="#B56-jmse-11-00808" class="html-bibr">56</a>,<a href="#B57-jmse-11-00808" class="html-bibr">57</a>,<a href="#B58-jmse-11-00808" class="html-bibr">58</a>,<a href="#B59-jmse-11-00808" class="html-bibr">59</a>].</p> Full article ">Figure 3
<p>Calculated Moho depth and comparison with published data. (<b>a</b>) Moho depth from gravity inversion. Black lines show the isobaths of the calculated Moho depth. Grey lines along the rift axis represent the isobaths of −900 m and −1800 m bathymetries. White lines indicate reflection seismic profiles in this study. Black and white lines (AA’ [<a href="#B78-jmse-11-00808" class="html-bibr">78</a>], BB’ [<a href="#B47-jmse-11-00808" class="html-bibr">47</a>], CC’ [<a href="#B79-jmse-11-00808" class="html-bibr">79</a>]) represent locations of the published density and velocity structures. White dots on the line CC’ show the distribution of OBS stations within the Suakin Deep [<a href="#B9-jmse-11-00808" class="html-bibr">9</a>]. (<b>b</b>) Published density and velocity structures [<a href="#B47-jmse-11-00808" class="html-bibr">47</a>,<a href="#B78-jmse-11-00808" class="html-bibr">78</a>,<a href="#B79-jmse-11-00808" class="html-bibr">79</a>] and microearthquakes records [<a href="#B9-jmse-11-00808" class="html-bibr">9</a>]. Red dashed lines indicate calculated Moho depths along published profiles.</p> Full article ">Figure 4
<p>Reflection seismic profile 060 and interpretations. Dashed box shows the area of <a href="#jmse-11-00808-f007" class="html-fig">Figure 7</a>a.</p> Full article ">Figure 5
<p>Reflection seismic profile 054 and interpretations. Dashed box shows the area of <a href="#jmse-11-00808-f007" class="html-fig">Figure 7</a>b.</p> Full article ">Figure 6
<p>Reflection seismic profile 050 and interpretations. Dashed box shows the area of <a href="#jmse-11-00808-f007" class="html-fig">Figure 7</a>c.</p> Full article ">Figure 7
<p>Deformed zones. Indicators of regional uplift: deformed basement, interrupted Pliocene–Pleistocene sediments, and disturbances within the evaporite deposition. Differential crustal vertical movement between the axial deep and the inter-trough zone. (<b>a</b>) Profile 060. (<b>b</b>) Profile 054. (<b>c</b>) Profile 050.</p> Full article ">Figure 8
<p>Sediment and tectonic evolution of the Hatiba Deep based on profile 060. (<b>a</b>) As the center of the Pliocene–Pleistocene uplift. (<b>b</b>) Intense tectonic subsidence controlled the formation of the deep. Lithologic patterns and unconformities refer to <a href="#jmse-11-00808-f002" class="html-fig">Figure 2</a>.</p> Full article ">Figure 9
<p>Free-air gravity models of the profile 060 (<b>a</b>), 054 (<b>b</b>), and 050 (<b>c</b>).</p> Full article ">Figure 10
<p>Segmentation of the Central Red Sea Rift axis. (<b>a</b>) Bathymetric map of the rift axis; data from Augustin et al. [<a href="#B34-jmse-11-00808" class="html-bibr">34</a>]. Grey lines are isobaths of −900 m, −1400 m, and −1900 m, respectively; data from the GEBCO Compilation Group 2021 (<a href="https://www.gebco.net" target="_blank">https://www.gebco.net</a>, accessed on 3 September 2021). Red dash line represents the axis of the Central Red Sea Rift. (<b>b</b>) Water depth variation along the red dash line in (<b>a</b>). The red solid lines are indicators of the oceanic crust in axial deeps. Longer arrows represent the first-order segments, central troughs spaced by ITZs; shorter ones represent the second-order segments, axial deeps spaced by the discontinuities (represented by the black dots).</p> Full article ">Figure 11
<p>Conceptual model of initial oceanization. (<b>a</b>) Early rift initiation, after the first two rifting stages (30–35 Ma). (<b>b</b>) Mantle upwelling and topographic uplift concentrated in the weak zones. (<b>c</b>) Nucleated normal-fault systems controlled the final breakup and formation of the oceanic crust.</p> Full article ">Figure 12
<p>Geophysical and kinematic characteristics of the Central Red Sea Rift. (<b>a</b>) Free-air gravity anomalies of the Central Red Sea Rift [<a href="#B98-jmse-11-00808" class="html-bibr">98</a>]. Black lines along the rift axis indicate the isobaths of −900 m and −1800 m. (<b>b</b>) Plate kinematic, heat flow, and earthquakes in the Central Red Sea Rift. Black arrows represent relative motion angular velocities between the Nubia and Arabia, calculated according to the model MORVEL [<a href="#B40-jmse-11-00808" class="html-bibr">40</a>]. Grey lines indicate the isobaths of −900 m and −1800 m. Focal mechanism solutions of the earthquakes from 1976 to present are from the CMT catalog [<a href="#B99-jmse-11-00808" class="html-bibr">99</a>,<a href="#B100-jmse-11-00808" class="html-bibr">100</a>]. Dots are heat flow sites [<a href="#B101-jmse-11-00808" class="html-bibr">101</a>].</p> Full article ">
19 pages, 4400 KiB  
Article
Speed and Fuel Ratio Optimization for a Dual-Fuel Ship to Minimize Its Carbon Emissions and Cost
by You-Chen Shih, Yu-An Tzeng, Chih-Wen Cheng and Chien-Hua Huang
J. Mar. Sci. Eng. 2023, 11(4), 758; https://doi.org/10.3390/jmse11040758 - 31 Mar 2023
Cited by 7 | Viewed by 2864
Abstract
In this study, nondominated sorting genetic algorithm II (NSGA-II) was used to minimize the cost and carbon emissions of a liquefied natural gas (LNG) dual-fuel ship for a given route. This study considered the regulations of emission control areas (ECA) and the European [...] Read more.
In this study, nondominated sorting genetic algorithm II (NSGA-II) was used to minimize the cost and carbon emissions of a liquefied natural gas (LNG) dual-fuel ship for a given route. This study considered the regulations of emission control areas (ECA) and the European Union (EU) Emissions Trading System (ETS) to determine the optimal speed and LNG/oil ratio for the ship. NSGA-II used the arrival time at each port and the LNG usage ratio for each voyage leg as its genes. The time window for arrival, the fuel cost, and potential EU carbon emission regulations were used to estimate the cost of the considered voyage. Moreover, fuel consumption was determined using historical data that were divided by period, machinery, and voyage leg. The results indicated that the optimal speed and fuel ratio could be determined under any given fuel and carbon price profile by using NSGA-II. Finally, the effects of regulations and carbon price differences on the optimal speed and fuel ratio were investigated. The cost minimization solution was susceptible to being affected by the regulations of ECAs and the EU ETS. The speed profile of the cost minimization solution was found to have a tendency to travel at faster-than-average speeds outside ECAs and non-EU regions, and travel slower in ECAs and EU regions. Meanwhile, the selection of fuel type showed that 100% traditional fuel oil in all regions, but with sufficiently high EU carbon permit cost, tends to use 100% LNG in EU regions. Full article
(This article belongs to the Special Issue Energy Efficiency in Marine Vehicles)
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<p>Types of fuel predicted to be used to reach the goal of net-zero GHG emissions by 2050 [<a href="#B7-jmse-11-00758" class="html-bibr">7</a>].</p> Full article ">Figure 2
<p>Results of speed–fuel consumption regression for a dual-fuel ship.</p> Full article ">Figure 3
<p>Process for evaluating the costs and carbon emissions of a dual-fuel ship.</p> Full article ">Figure 4
<p>Flowchart of NSGA-II [<a href="#B28-jmse-11-00758" class="html-bibr">28</a>].</p> Full article ">Figure 5
<p>Map of the considered route. Orange regions were considered ECAs.</p> Full article ">Figure 6
<p>PF figures for a (<b>a</b>) general single-fuel ship and (<b>b</b>) general dual-fuel ship. (<b>a</b>) The carbon emission and the cost had no trade-off on single-fuel ship; thus, it was one optimal solution and a single point on PF figure. (<b>b</b>) For the general LNG dual-fuel ship, the two objectives had conflicts, and the optimal solutions would become a straight line.</p> Full article ">Figure 7
<p>Theoretical average speed (blue, solid line) and approximate optimal speed (orange, dash-dotted line) in all legs for both a general single-fuel ship and a general dual-fuel ship.</p> Full article ">Figure 8
<p>PFs (green solid, orange solid, red dashed) represent Scenario 3, Scenario 2, and considering 0.5% sulfur oil and LNG available in all legs, respectively. The green-frame arrow indicates the bending point of green PF.</p> Full article ">Figure 9
<p>Theoretical average speed (blue, solid line), cost minimization optimal speed (orange dashed dotted line and green dotted line) in all legs represent the Scenario 2 and Scenario 3.</p> Full article ">Figure 10
<p>PFs (orange, green, red) represent scenario 2, scenario 4 with a 100 USD carbon permit, and scenario 4 with a 200 USD carbon permit, respectively.</p> Full article ">Figure 11
<p>Theoretical average speed (blue solid line), cost minimization optimal speed (orange dashed dotted line, green dotted line, and red dashed line) in all legs represent Scenario 2 and Scenario 4 with a 100 USD carbon permit, and Scenario 4 with a 200 USD carbon permit, respectively.</p> Full article ">Figure 12
<p>Hypervolume analysis of the considered problem.</p> Full article ">
9 pages, 2105 KiB  
Article
Underwater Positioning System Based on Drifting Buoys and Acoustic Modems
by Pablo Otero, Álvaro Hernández-Romero, Miguel-Ángel Luque-Nieto and Alfonso Ariza
J. Mar. Sci. Eng. 2023, 11(4), 682; https://doi.org/10.3390/jmse11040682 - 23 Mar 2023
Cited by 6 | Viewed by 2128
Abstract
GNSS (Global Navigation Satellite System) positioning is not available underwater due to the very short range of electromagnetic waves in the sea water medium. In this article a LBL (Long Base Line) acoustic repeater system of the GNSS positioning is presented. The system [...] Read more.
GNSS (Global Navigation Satellite System) positioning is not available underwater due to the very short range of electromagnetic waves in the sea water medium. In this article a LBL (Long Base Line) acoustic repeater system of the GNSS positioning is presented. The system is hyperbolic, i.e., based on time differences and it does not need very accurate atomic clocks to synchronize repeaters. The system architecture and system calculations that demonstrate the feasibility of the solution are presented. The system uses four buoys that sequentially transmit their position and the time of the instant of transmission, for which they are equipped with GNSS receivers and acoustic modems. The buoys can be fixed or even drifting, but they are inexpensive devices, which pose no hazard to navigation and can be easily and quickly deployed for a specific underwater mission. The multilateration algorithm used in the receiver is presented. To simplify the algorithm, the depth of the receiver, measured by a depth sensor, is used. Results are presented for the position error of an underwater vehicle due to its displacement during the transmission frame of the four buoys. Full article
(This article belongs to the Special Issue Navigation and Localization for Autonomous Marine Vehicles)
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<p>System sketch.</p> Full article ">Figure 2
<p>Frame structure.</p> Full article ">Figure 3
<p>Error when the receiver is moving along a parallel.</p> Full article ">Figure 4
<p>Error when the receiver is moving along a meridian.</p> Full article ">
21 pages, 5074 KiB  
Article
Underwater Target Detection Based on Improved YOLOv7
by Kaiyue Liu, Qi Sun, Daming Sun, Lin Peng, Mengduo Yang and Nizhuan Wang
J. Mar. Sci. Eng. 2023, 11(3), 677; https://doi.org/10.3390/jmse11030677 - 22 Mar 2023
Cited by 44 | Viewed by 8183
Abstract
Underwater target detection is a crucial aspect of ocean exploration. However, conventional underwater target detection methods face several challenges such as inaccurate feature extraction, slow detection speed, and lack of robustness in complex underwater environments. To address these limitations, this study proposes an [...] Read more.
Underwater target detection is a crucial aspect of ocean exploration. However, conventional underwater target detection methods face several challenges such as inaccurate feature extraction, slow detection speed, and lack of robustness in complex underwater environments. To address these limitations, this study proposes an improved YOLOv7 network (YOLOv7-AC) for underwater target detection. The proposed network utilizes an ACmixBlock module to replace the 3 × 3 convolution block in the E-ELAN structure, and incorporates jump connections and 1 × 1 convolution architecture between ACmixBlock modules to improve feature extraction and network reasoning speed. Additionally, a ResNet-ACmix module is designed to avoid feature information loss and reduce computation, while a Global Attention Mechanism (GAM) is inserted in the backbone and head parts of the model to improve feature extraction. Furthermore, the K-means++ algorithm is used instead of K-means to obtain anchor boxes and enhance model accuracy. Experimental results show that the improved YOLOv7 network outperforms the original YOLOv7 model and other popular underwater target detection methods. The proposed network achieved a mean average precision (mAP) value of 89.6% and 97.4% on the URPC dataset and Brackish dataset, respectively, and demonstrated a higher frame per second (FPS) compared to the original YOLOv7 model. In conclusion, the improved YOLOv7 network proposed in this study represents a promising solution for underwater target detection and holds great potential for practical applications in various underwater tasks. Full article
(This article belongs to the Section Ocean Engineering)
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<p>The network structure of YOLOv7.</p> Full article ">Figure 2
<p>The structure diagram of ResNet-ACmix module (<b>left</b>: ResNet; <b>right</b>: ResNet-ACmix).</p> Full article ">Figure 3
<p>The structure diagram of AC-E-ELAN module (<b>left</b>: RepVgg; <b>right</b>: AC-E-ELAN).</p> Full article ">Figure 4
<p>Structure diagram of the YOLOv7-AC model.</p> Full article ">Figure 5
<p>Example images of the URPC dataset.</p> Full article ">Figure 6
<p>Statistical results of the URPC dataset: (<b>a</b>) bar chart of the number of targets in each class; (<b>b</b>) normalized target location map; (<b>c</b>) normalized target size map.</p> Full article ">Figure 7
<p>The precision-recall curve of the YOLOv7-AC model on the URPC dataset.</p> Full article ">Figure 8
<p>The confusion matrix of the YOLOv7-AC model on the URPC dataset.</p> Full article ">Figure 9
<p>Variation curves of loss values on the URPC dataset.</p> Full article ">Figure 10
<p>Example images of the Brackish dataset.</p> Full article ">Figure 11
<p>Statistical results of the Brackish dataset: (<b>a</b>) bar chart of the number of targets in each class; (<b>b</b>) normalized target location map; (<b>c</b>) normalized target size map.</p> Full article ">Figure 12
<p>The precision-recall curve of the YOLOv7-AC model on the Brackish dataset.</p> Full article ">Figure 13
<p>The confusion matrix of the YOLOv7-AC model (the Brackish dataset).</p> Full article ">Figure 14
<p>Variation curves of loss values on the Brackish dataset.</p> Full article ">Figure 15
<p>Detection results of YOLOv7 (<b>left</b>) and YOLOv7-AC (<b>right</b>) in harsh underwater scenes.</p> Full article ">Figure 16
<p>(<b>a</b>) Error detection of YOLOv7-AC in highly complex underwater environments (<b>left:</b> marked in black boxes); (<b>b</b>) omission detection of YOLOv7-AC in highly complex underwater environments (<b>right</b>: marked in red boxes).</p> Full article ">
22 pages, 9639 KiB  
Article
Automated Atmospheric Correction of Nanosatellites Using Coincident Ocean Color Radiometer Data
by Sean McCarthy, Summer Crawford, Christopher Wood, Mark D. Lewis, Jason K. Jolliff, Paul Martinolich, Sherwin Ladner, Adam Lawson and Marcos Montes
J. Mar. Sci. Eng. 2023, 11(3), 660; https://doi.org/10.3390/jmse11030660 - 21 Mar 2023
Cited by 5 | Viewed by 2142
Abstract
Here we present a machine-learning-based method for utilizing traditional ocean-viewing satellites to perform automated atmospheric correction of nanosatellite data. These sensor convolution techniques are required because nanosatellites do not usually possess the wavelength combinations required to atmospherically correct upwelling radiance data for oceanographic [...] Read more.
Here we present a machine-learning-based method for utilizing traditional ocean-viewing satellites to perform automated atmospheric correction of nanosatellite data. These sensor convolution techniques are required because nanosatellites do not usually possess the wavelength combinations required to atmospherically correct upwelling radiance data for oceanographic applications; however, nanosatellites do provide superior ground-viewing spatial resolution (~3 m). Coincident multispectral data from the Suomi National Polar-Orbiting Partnership Visible Infrared Imaging Radiometer Suite (Suomi NPP VIIRS; referred to herein as “VIIRS”) were used to remove atmospheric contamination at each of the nanosatellite’s visible wavelengths to yield an estimate of spectral water-leaving radiance [Lw(l)], which is the basis for surface ocean optical products. Machine learning (ML) algorithms (KNN, decision tree regressors) were applied to determine relationships between Lw and top-of-atmosphere (Lt)/Rayleigh (Lr) radiances within VIIRS training data, and then applied to test cases for (1) the Marine Optical Buoy (MOBY) in Hawaii and (2) the AErosol RObotic Network Ocean Color (AERONET-OC), Venice, Italy. For the test cases examined, ML-based methods appeared to improve statistical results when compared to alternative dark spectrum fitting (DSF) methods. The results suggest that ML-based sensor convolution techniques offer a viable path forward for the oceanographic application of nanosatellite data streams. Full article
(This article belongs to the Special Issue New Frontiers in Ocean Color Remote Sensing: Novel Applications, Sensor Fusion, and Hyperspectral Sensing)
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<p>Standard atmospheric correction process implemented within NRL’s APS, built upon NASA’s SeaDAS processing code.</p> Full article ">Figure 2
<p><b>Left</b> Image: VIIRS True Color Image, 16 March 2019. The red box is centered around the MOBY hyperspectral nLw in situ mooring, located off the Hawaiian island of Lanai. <b>Right</b> Image: VIIRS True Color Image, 28 February 2021. The red box is centered around the AERONET-OC multispectral nLw in situ platform, located off the coast of Venice, Italy. All valid pixels within the red box were used for training the atmospheric correction models to be used for atmospherically correcting the nanosatellite imagery.</p> Full article ">Figure 3
<p>Complete methodology for building and validating a linear regression nLw predictive model at wavelength 486 nm from VIIRS training datasets at the MOBY nLw in situ mooring.</p> Full article ">Figure 3 Cont.
<p>Complete methodology for building and validating a linear regression nLw predictive model at wavelength 486 nm from VIIRS training datasets at the MOBY nLw in situ mooring.</p> Full article ">Figure 4
<p>Example decision tree regressor nLw predictive model at 486 nm, built from VIIRS training datasets at the MOBY nLw in situ mooring.</p> Full article ">Figure 5
<p>Gordon–Wang automated atmospheric correction using Planet PlanetScope’s duplicated red band and NIR band. <b>Top left</b>: MOBY nLw_494. <b>Top right</b>: MOBY nLw_545. <b>Middle left</b>: MOBY nLw_635/644. <b>Middle right</b>: Venice nLw_494. <b>Bottom left</b>: Venice nLw_545. <b>Bottom right</b>: Venice nLw_635/644.</p> Full article ">Figure 6
<p>MOBY and Venice AERONET-OC in situ nLw climatologies. <b>Top left</b>: MOBY in situ nLw_494. <b>Top right</b>: MOBY in situ nLw_545. <b>Middle left</b>: MOBY in situ nLw_640. <b>Middle right</b>: Venice AERONET-OC in situ nLw_490. <b>Bottom left</b>: Venice AERONET-OC in situ nLw_555. <b>Bottom right</b>: Venice AERONET-OC in situ nLw_668.</p> Full article ">Figure 7
<p>(<b>a</b>). VIIRS top 10 nLw predictive models compared to the real (APS-processed) nLw_494 at the MOBY study area. (<b>b</b>). VIIRS top 10 nLw predictive models compared to the real (APS-processed) nLw_545 at the MOBY study area. (<b>c</b>). VIIRS top 10 nLw predictive models compared to the real (APS-processed) nLw_636/644 at the MOBY study area. (<b>d</b>). VIIRS top 10 nLw predictive models compared to the real (APS-processed) nLw_494 at the Venice study area. (<b>e</b>). VIIRS top 10 nLw predictive models compared to the real (APS-processed) nLw_545 at the Venice study area. (<b>f</b>). VIIRS top 10 nLw predictive models compared to the real (APS-processed) nLw_635/644 at the Venice study area.</p> Full article ">Figure 7 Cont.
<p>(<b>a</b>). VIIRS top 10 nLw predictive models compared to the real (APS-processed) nLw_494 at the MOBY study area. (<b>b</b>). VIIRS top 10 nLw predictive models compared to the real (APS-processed) nLw_545 at the MOBY study area. (<b>c</b>). VIIRS top 10 nLw predictive models compared to the real (APS-processed) nLw_636/644 at the MOBY study area. (<b>d</b>). VIIRS top 10 nLw predictive models compared to the real (APS-processed) nLw_494 at the Venice study area. (<b>e</b>). VIIRS top 10 nLw predictive models compared to the real (APS-processed) nLw_545 at the Venice study area. (<b>f</b>). VIIRS top 10 nLw predictive models compared to the real (APS-processed) nLw_635/644 at the Venice study area.</p> Full article ">Figure 7 Cont.
<p>(<b>a</b>). VIIRS top 10 nLw predictive models compared to the real (APS-processed) nLw_494 at the MOBY study area. (<b>b</b>). VIIRS top 10 nLw predictive models compared to the real (APS-processed) nLw_545 at the MOBY study area. (<b>c</b>). VIIRS top 10 nLw predictive models compared to the real (APS-processed) nLw_636/644 at the MOBY study area. (<b>d</b>). VIIRS top 10 nLw predictive models compared to the real (APS-processed) nLw_494 at the Venice study area. (<b>e</b>). VIIRS top 10 nLw predictive models compared to the real (APS-processed) nLw_545 at the Venice study area. (<b>f</b>). VIIRS top 10 nLw predictive models compared to the real (APS-processed) nLw_635/644 at the Venice study area.</p> Full article ">Figure 8
<p>MOBY and Venice AERONET-OC in situ nLw climatologies with Planet nanosatellite nLw predictions. <b>Top left</b>: MOBY in situ nLw_494. <b>Top right</b>: MOBY in situ nLw_545. <b>Middle left</b>: MOBY in situ nLw_635/644. <b>Middle right</b>: Venice AERONET-OC in situ nLw_490. <b>Bottom left</b>: Venice AERONET-OC in situ nLw_555. <b>Bottom right</b>: Venice AERONET-OC in situ nLw_668. The orange ‘x’ indicates a nanosatellite nLw predicted value.</p> Full article ">Figure 8 Cont.
<p>MOBY and Venice AERONET-OC in situ nLw climatologies with Planet nanosatellite nLw predictions. <b>Top left</b>: MOBY in situ nLw_494. <b>Top right</b>: MOBY in situ nLw_545. <b>Middle left</b>: MOBY in situ nLw_635/644. <b>Middle right</b>: Venice AERONET-OC in situ nLw_490. <b>Bottom left</b>: Venice AERONET-OC in situ nLw_555. <b>Bottom right</b>: Venice AERONET-OC in situ nLw_668. The orange ‘x’ indicates a nanosatellite nLw predicted value.</p> Full article ">Figure 9
<p>Planet PlanetScope predicted nLw vs. MOBY in situ nLw. <b>Top left</b>: nLw_494 unfiltered. <b>Top right</b>: nLw_494 filtered. <b>Middle left</b>: nLw_545 unfiltered. <b>Middle right</b>: nLw_545 filtered. <b>Lower left</b>: nLw_635/644 unfiltered. <b>Lower right</b>: nLw_635/644 filtered.</p> Full article ">Figure 10
<p>Planet PlanetScope predicted nLw vs. Venice AERONET-OC in situ nLw. <b>Top left</b>: nLw_494 unfiltered. <b>Top right</b>: nLw_494 filtered. <b>Middle left</b>: nLw_545 unfiltered. <b>Middle right</b>: nLw_545 filtered. <b>Lower left</b>: nLw_635/644 unfiltered. <b>Lower right</b>: nLw_635/644 filtered.</p> Full article ">Figure 11
<p>Planet model predicted nLw and ACOLITE nLw compared to MOBY and Venice AERONET-OC in situ nLw. <b>Top left</b>: MOBY blue nLw. <b>Top right</b>: MOBY green nLw. <b>Middle left</b>: MOBY red nLw. <b>Middle right</b>: Venice AERONET-OC blue nLw. <b>Lower left</b>: Venice AERONET-OC green nLw. <b>Lower right</b>: Venice AERONET-OC red nLw.</p> Full article ">Figure 11 Cont.
<p>Planet model predicted nLw and ACOLITE nLw compared to MOBY and Venice AERONET-OC in situ nLw. <b>Top left</b>: MOBY blue nLw. <b>Top right</b>: MOBY green nLw. <b>Middle left</b>: MOBY red nLw. <b>Middle right</b>: Venice AERONET-OC blue nLw. <b>Lower left</b>: Venice AERONET-OC green nLw. <b>Lower right</b>: Venice AERONET-OC red nLw.</p> Full article ">
17 pages, 16130 KiB  
Article
Application of Buoyancy Support System to Secure Residual Buoyancy of Damaged Ships
by Gyeong Joong Lee, Jang-Pyo Hong, Kwang Keun Lee and Hee Jin Kang
J. Mar. Sci. Eng. 2023, 11(3), 656; https://doi.org/10.3390/jmse11030656 - 20 Mar 2023
Cited by 3 | Viewed by 2471
Abstract
SOLAS (Safety of Life at Sea), which was first enacted in 1914 as a result of the Titanic disaster, presents mandatory requirements for ship safety, such as the adoption of watertight bulkheads. However, ship accidents continue to occur despite the development and application [...] Read more.
SOLAS (Safety of Life at Sea), which was first enacted in 1914 as a result of the Titanic disaster, presents mandatory requirements for ship safety, such as the adoption of watertight bulkheads. However, ship accidents continue to occur despite the development and application of numerous safety technologies. In the case of a marine accident, the risk of sinking or capsizing due to flooding can be reduced by subdividing the watertight area, but shipbuilding costs, the weight increase for light ships, and the intact stability of the vessel must be considered together. For this reason, in this study, a BSS (buoyancy support system) was designed in accordance with ISO 23121-1 and ISO 23121-2. The characteristics of watertight and non-watertight spaces were reviewed and the BSS was implemented for a small car ferry. By applying additional safety technologies while securing economic feasibility in terms of ship construction and operation, an alternative to reduce the loss of human lives, environmental damage, and property losses in the case of a ship accident was proposed. Full article
(This article belongs to the Special Issue Damage Stability of Ships)
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<p>Concept of the BSS [<a href="#B1-jmse-11-00656" class="html-bibr">1</a>].</p> Full article ">Figure 2
<p>Comparison of flooding characteristics between watertight (trapped air) and non-watertight (free vent) spaces; Arrows show the flow of water (blue colored) and air (white colored) and hyphen means there are no flow of water.</p> Full article ">Figure 3
<p>Analysis results of flooding charateristics in the watertight area.</p> Full article ">Figure 4
<p>Analysis result of flooding characteristics of non-watertight area.</p> Full article ">Figure 5
<p>Changes in <span class="html-italic">GM</span>, according to the depth of the flooded water.</p> Full article ">Figure 6
<p>Compartment layout of target vessel.</p> Full article ">Figure 7
<p>The results of the simulation of flooding due to damage to areas No. 1 and No. 2 void, sinking after 4.3 min (263 s).</p> Full article ">Figure 8
<p>Examples of evacuation simulation. Evacuation took 89 s in the bow direction (<b>above</b>) and took 142 s in the stern direction (<b>below</b>).</p> Full article ">Figure 9
<p>Example of the BSS installation space analysis and buoyancy chamber design.</p> Full article ">Figure 10
<p>Example of temperature drop design according to the length of the carbon dioxide transfer line; at wind speed: 10 m/s (<b>above</b>), 5 m/s (<b>below</b>).</p> Full article ">Figure 11
<p>Example of pipe line diagram for when a heat exchanger is applied.</p> Full article ">Figure 12
<p>P and I diagram of the BSS.</p> Full article ">Figure 13
<p>Prototype test (<b>above</b>) and target ship installation (<b>below</b>). Third-party verification certificate (<b>below right</b>) of the BSS.</p> Full article ">Figure 14
<p>Case study result of 3D point cloud data basis buoyancy chamber arrangement in the machinery room.</p> Full article ">Figure 15
<p>BSS to be equipped; 2600 GT class K-GTB appearance and specifications.</p> Full article ">Figure 16
<p>Conceptual design and location of BSS installation, as verified by the general arrangement of K-GTB.</p> Full article ">Figure 17
<p>BSS prototype test for installation in a K-GTB machinery room.</p> Full article ">
25 pages, 21339 KiB  
Article
Coastal Erosion Identification and Monitoring in the Patras Gulf (Greece) Using Multi-Discipline Approaches
by Nikolaos Depountis, Dionysios Apostolopoulos, Vasileios Boumpoulis, Dimitris Christodoulou, Athanassios Dimas, Elias Fakiris, Georgios Leftheriotis, Alexandros Menegatos, Konstantinos Nikolakopoulos, George Papatheodorou and Nikolaos Sabatakakis
J. Mar. Sci. Eng. 2023, 11(3), 654; https://doi.org/10.3390/jmse11030654 - 20 Mar 2023
Cited by 9 | Viewed by 2930
Abstract
The primary objective of this research is to demonstrate advanced surveying methods and techniques for coastal erosion identification and monitoring in a densely human-populated coastline, the southern coastline of the Gulf of Patras (Greece), which diachronically suffers erosion problems expected to become worse [...] Read more.
The primary objective of this research is to demonstrate advanced surveying methods and techniques for coastal erosion identification and monitoring in a densely human-populated coastline, the southern coastline of the Gulf of Patras (Greece), which diachronically suffers erosion problems expected to become worse in the forthcoming years due to climate change and human intervention. Its importance lies in the fact that it presents a robust methodology on how all modern scientific knowledge and techniques should be used in coastal erosion problems. The presented methods include the use of satellite and aerial photo imaging, shallow seabed bathymetry and morphology, sediment sampling, geotechnical investigations, as well as hydrodynamic modelling. The results are extensively analyzed in terms of their importance in coastal erosion studies and are cross-validated to define those areas most vulnerable to erosion. Towards this scope, the seabed erosion rate produced by hydrodynamic modelling is compared with the coastal vulnerability index (CVI) calculations performed in the examined area to identify which coastal zones are under a regime of intensive erosion. The results between the CVI and the seabed erosion rate appear to coincide in terms of the erosion potential, especially in zones where the vulnerability regime has been calculated as being high or very high, with the P. oceanica meadows playing an important role in reducing erosion. Full article
(This article belongs to the Special Issue Estuaries, Coasts, and Seas in a Changing Climate)
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<p>Map of the investigation area with the blue line representing the inspected south coastline of the Gulf of Patras. The geodetic coordinates of the map correspond to the Greek Geodetic Reference System—GGRS87.</p> Full article ">Figure 2
<p>Survey design plan combined with a regional observatory for coastal monitoring of the suffered shoreline.</p> Full article ">Figure 3
<p>Map of the performed geotechnical surveys (borehole, CPT, and sediment sampling positions) along the south coastline of the Gulf of Patras.</p> Full article ">Figure 4
<p>Study area (Araxos Cape–Rion Port) covered by the remote sensing datasets and test site (red frame area of Kaminia and Roitika villages) using GNSS measurements.</p> Full article ">Figure 5
<p>(<b>a</b>) The survey vessel “Milady Millord III”, (<b>b</b>) Bathyswath1 transducers mounted over the side of the vessel, (<b>c</b>) Bathyswath1 Transmit/Receive Unit and operating workstation and (<b>d</b>) Bathyswath Swath Processor and Hypack 2010 during data acquisition, (<b>e</b>) Edgetech 4200 SSS Towfish, (<b>f</b>) SSS tow cable, (<b>g</b>) Edgetech 4200P digital recording unit, and (<b>h</b>) SSS data during acquisition.</p> Full article ">Figure 6
<p>Map of the southern coastline of the Gulf of Patras showing the vessel tracklines.</p> Full article ">Figure 7
<p>The bathymetry of the computational domain of the Gulf of Patras and part of the Ionian Sea used in the numerical simulations (stage 1) of wind-induced wave generation, growth, and propagation. The red line indicates the coastline of the study area, while the black points indicate the location of the meteorological stations at Nafpaktos and Araxos.</p> Full article ">Figure 8
<p>Map of the southern shoreline of the research area divided into 8 coastal independent zones, with the bathymetry used in the numerical simulations (stages 2, 3, and 4).</p> Full article ">Figure 9
<p>Engineering geological map with geotechnical units of the study area.</p> Full article ">Figure 10
<p>Statistical NSM rates for the 1987–2018 period between Kaminia and Roitika villages.</p> Full article ">Figure 11
<p>(<b>a</b>) Map showing the statistical EPR transects and (<b>b</b>) line graph showing the EPR values (mm/y) of each transect between Kaminia and Roitika villages for the period 1987–2018.</p> Full article ">Figure 12
<p>(%) State of erosion as part (%) of the study area according to the EPR rates (1987–2018).</p> Full article ">Figure 13
<p>(<b>I</b>) Bathymetric map of the survey area. (<b>Ia</b>) Detailed bathymetry showing <span class="html-italic">P. οceanica</span> meadow morphology and (<b>Ib</b>) underwater photo of <span class="html-italic">P. οceanica</span>. (<b>II</b>) Slope gradient map of the survey area. Based on the multibeam bathymetric data and the slope gradient, the area has been separated into five (a–e) subareas.</p> Full article ">Figure 14
<p>Seafloor map of the survey area showing four distinct bottom classes, based on the interpretation of the side scan sonar high (400 kHz) and low (100 kHz) frequency mosaics. (<b>I</b>) Corresponds to a mosaic detail including all bottom types, indicating with dashed line borders the areas classified as <span class="html-italic">P. oceanica</span> meadows.</p> Full article ">Figure 15
<p>Sonograph of (<b>a</b>) 400 and (<b>b</b>) 100 kHz showing representative dense, almost continuous and uniform <span class="html-italic">P. oceanica</span> meadows. Arrows indicate sand gaps inside <span class="html-italic">P. oceanica</span> meadow.</p> Full article ">Figure 16
<p>Seabed level change in (<b>a</b>) coastal zones 1 and 2, (<b>b</b>) zones 3 and 4, (<b>c</b>) zones 5 and 6 due to NE waves, and (<b>d</b>) coastal zones 7 and 8 due to NW waves. These are the most affected sea bed level change winds in the respective areas.</p> Full article ">Figure 17
<p>Spatial distribution of high and very high vulnerability classes using CVI<sub>WF</sub> compared with the rate of bed level change (mostly affected by NE winds in the west and NW waves in the east) and the <span class="html-italic">P. oceanica</span> spatial distribution along the coastline of the study area.</p> Full article ">
15 pages, 5521 KiB  
Article
On the Fatigue Strength of Welded High-Strength Steel Joints in the As-Welded, Post-Weld-Treated and Repaired Conditions in a Typical Ship Structural Detail
by Antti Ahola, Kalle Lipiäinen, Juuso Lindroos, Matti Koskimäki, Kari Laukia and Timo Björk
J. Mar. Sci. Eng. 2023, 11(3), 644; https://doi.org/10.3390/jmse11030644 - 19 Mar 2023
Cited by 5 | Viewed by 2631
Abstract
Weld quality and life extension methods of welded details in ship structures made of high-strength and ultra-high-strength steels are of high importance to overcome the issues related to the fatigue characteristics of welded high-strength steels. The current work experimentally and numerically investigated the [...] Read more.
Weld quality and life extension methods of welded details in ship structures made of high-strength and ultra-high-strength steels are of high importance to overcome the issues related to the fatigue characteristics of welded high-strength steels. The current work experimentally and numerically investigated the fatigue strength of a longitudinal stiffener detail, typically present in the bulkhead connections of ship hull. Two high-strength steel grades, namely EQ47TM and EQ70QT steels, were studied in welded plate connections using gas metal arc welding with rutile-cored wires. Fatigue tests were carried out on both small-scale specimens under axial and large-scale beam specimens under four-point bending loading. In addition to the joints tested in the as-welded condition, the high-frequency mechanical impact (HFMI) treatment was considered as a post-weld treatment technique in the fatigue test series. Furthermore, the large-scale beam specimens were pre-fatigued until substantial fatigue cracks were observed, after which they were re-tested after weld repairing and post-weld treatments to investigate the potential to rehabilitate fatigue-cracked ship details. The joints in the as-welded condition were performed in accordance with the current design recommendations. Due to the severe transition from the base material to the weld reinforcement in the joints welded with the rutile-cored wire, a successful HFMI treatment required geometrical modification of weld toe using a rotary burr to avoid any detrimental sub-cracks at the HFMI-treated region. Alternatively, the use of solid filler wires could potentially overcome these issues related to the welding quality. Repaired and post-weld-treated welds performed well in the re-tests, and the fatigue strength was almost twice higher than that of tests in the as-welded condition. Full article
(This article belongs to the Special Issue Fatigue and Fracture Mechanics of Marine Structures)
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<p>Shape and dimensions of the test specimens: (<b>a</b>) the tensile specimens, and (<b>b</b>) beam specimens (dimensions in mm).</p> Full article ">Figure 2
<p>Schematic description of weld repair processing.</p> Full article ">Figure 3
<p>Residual stress measurements of (<b>a</b>) a tensile specimen and (<b>b</b>) beam specimen.</p> Full article ">Figure 4
<p>Fatigue testing equipment and instrumentation: fatigue tests of (<b>a</b>) the tensile specimens and (<b>b</b>) beam specimens, and (<b>c</b>) strain gage instrumentation.</p> Full article ">Figure 5
<p>FE meshes for (<b>a</b>) the tensile specimens and (<b>b</b>) beam specimens (not in scale).</p> Full article ">Figure 6
<p>Fatigue test results of tensile specimens in the nominal stress system. BG+HFMI refers to the specimens welded specimens (rutile-cored wire) prepared by burr grinding followed by the HFMI treatment.</p> Full article ">Figure 7
<p>Observed failure locations in the specimens: from (<b>a</b>) weld toe, (<b>b</b>) weld root, and (<b>c</b>) thermally cut edge.</p> Full article ">Figure 8
<p>Fatigue test results of the beam specimens in the nominal stress system (<sup>a</sup> Tensile specimens in <a href="#jmse-11-00644-f005" class="html-fig">Figure 5</a>).</p> Full article ">Figure 9
<p>Fatigue test results of the repair-welded (RW) beam specimens in the nominal stress system (<sup>a</sup> Beam specimens in <a href="#jmse-11-00644-f007" class="html-fig">Figure 7</a>; <sup>b</sup> Tensile specimens in <a href="#jmse-11-00644-f005" class="html-fig">Figure 5</a>).</p> Full article ">Figure 10
<p>Macro graphs on the failures in the tensile specimens: (<b>a</b>) an EQ70 specimen failing from the weld toe, and (<b>b</b>) a HFMI-treated EQ47 specimen failing from the edge of the HFMI groove in the vicinity of the fusion line.</p> Full article ">Figure 11
<p>Macro graphs on the failures in the repair-welded beam specimens: (<b>a</b>) failure from the HFMI groove, and (<b>b</b>) failure from the weld root at the side of flush-ground toe (the cope hole side of the gusset).</p> Full article ">Figure 12
<p>Macro graphs on the root failures in the successful HFMI treatments: (<b>a</b>) burr-ground and HFMI-treated specimen, and (<b>b</b>) HFMI-treated specimen (prepared using the solid wire).</p> Full article ">Figure 13
<p>Residual stress measurement results in the specimens in the AW condition: (<b>a</b>) tensile specimens, and (<b>b</b>) beam specimen.</p> Full article ">Figure 14
<p>Results of FE analyses: stress distributions and obtained structural SCFs and ENSs at the toe positions using the maximum principal stress criterion: (<b>a</b>) the tensile and (<b>b</b>) beam specimens.</p> Full article ">Figure 15
<p>Fatigue test results in terms of (<b>a</b>) structural HS stress and (<b>b</b>) ENS systems [<a href="#B26-jmse-11-00644" class="html-bibr">26</a>].</p> Full article ">
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