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Volume 12
Issue 7
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J. Mar. Sci. Eng., Volume 12, Issue 7 (July 2024) – 203 articles

Cover Story (view full-size image): It is widely acknowledged that pharmaceuticals present a significant threat to aquatic marine ecosystems, primarily due to their biological impact. Pharmaceuticals are employed globally for the treatment of human and animal disorders, as well as for the promotion of livestock growth. In addition to the common inefficiency of removal in wastewater treatment plants, a number of other sources contribute to the contamination of marine environments and subsequent adverse effects. These emerging contaminants interact with specific biochemical and physiological pathways in target organisms, causing alterations in marine species throughout their entire life cycle. Marine fish serve as bioindicators of pharmaceutical contamination in seawater, exhibiting the capacity to bioaccumulate these compounds and manifest their effects. View this paper
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29 pages, 9684 KiB  
Article
Decoupled Analysis of a Multi-Layer Flexible Pipeline Buried in Clay Subjected to Large Lateral Soil Displacement
by Eduardo Ribeiro Malta, Xiaoyu Dong and Hodjat Shiri
J. Mar. Sci. Eng. 2024, 12(7), 1238; https://doi.org/10.3390/jmse12071238 - 22 Jul 2024
Viewed by 577
Abstract
Multilayered flexible subsea pipelines may experience significant lateral movements due to manmade and environmental geohazards. These pipelines incorporate several structural and protective layers to resist different loads, and may require additional protection such as trenching, rock placement, or burial. In practice, simplifications are [...] Read more.
Multilayered flexible subsea pipelines may experience significant lateral movements due to manmade and environmental geohazards. These pipelines incorporate several structural and protective layers to resist different loads, and may require additional protection such as trenching, rock placement, or burial. In practice, simplifications are considered due to the complexities and uncertainties involved in the multi-layer pipe structure and the surrounding soil, compromising the pipe structure or the soil behavior. These simplifications are applied either on the pipe by assuming a rigid section or on the soil by representing it as elastic springs, which may result in inaccuracies. This study proposes a decoupled methodology combining the Coupled Eulerian–Lagrangian (CEL) model for soil displacement with a small-strain finite element analysis of the flexible pipe. This approach aims to accurately capture cross-sectional deformations and local stresses due to soil movement while maintaining reasonable computational effort. A parametric analysis was conducted to assess the impact of several variables on failure risk. The deformed cross-section was then used for a collapse analysis to determine critical loads at maximum operational depth. The study showed that modeling parameters such as soil strength and internal diameter might significantly influence pipe failure and the risk of collapse. Full article
(This article belongs to the Special Issue Advanced Research in Flexible Riser and Pipelines)
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Figure 1
<p>(<b>a</b>) Pipeline submitted to ice gouging (adapted from [<a href="#B21-jmse-12-01238" class="html-bibr">21</a>]); (<b>b</b>) a multi-layered flexible pipe and the layers that comprise the structural nucleus (adapted from [<a href="#B22-jmse-12-01238" class="html-bibr">22</a>]).</p> Full article ">Figure 2
<p>Simulation flow chart.</p> Full article ">Figure 3
<p>Configuration of the CEL model.</p> Full article ">Figure 4
<p>Verification of the developed CEL model on the bearing capacity factor [<a href="#B8-jmse-12-01238" class="html-bibr">8</a>,<a href="#B10-jmse-12-01238" class="html-bibr">10</a>,<a href="#B37-jmse-12-01238" class="html-bibr">37</a>].</p> Full article ">Figure 5
<p>General aspect of the FE model. The surface areas of each concentric ring were broken into circular sectors to facilitate the structured mesh creation, thus appearing in different colors.</p> Full article ">Figure 6
<p>Superposition between pressure armor profiles.</p> Full article ">Figure 7
<p>Boundary conditions for Configuration I.</p> Full article ">Figure 8
<p>Comparison between the two boundary conditions.</p> Full article ">Figure 9
<p>Stress vs. strain curve for the carbon steel.</p> Full article ">Figure 10
<p>Stress vs. strain curve for the HDPE [<a href="#B26-jmse-12-01238" class="html-bibr">26</a>].</p> Full article ">Figure 11
<p>Lateral force on pipe.</p> Full article ">Figure 12
<p>Bearing capacity factor.</p> Full article ">Figure 13
<p>Plastic strain in soil (at δ/D = 1.20). PEVAVG is the average plastic strain in the element, computed as a volume fraction.</p> Full article ">Figure 14
<p>Contact pressure profiles introduced into FE analysis (starting from the top of the pipe and moving in a counterclockwise direction). Figure (<b>a</b>–<b>e</b>) show different burial depths normalized by the diameter of the pipeline (<math display="inline"><semantics> <mrow> <mrow> <mrow> <mi>δ</mi> </mrow> <mo>/</mo> <mrow> <mi>D</mi> </mrow> </mrow> </mrow> </semantics></math>).</p> Full article ">Figure 15
<p>Plastic strain in study cases (at δ/D = 1.20).</p> Full article ">Figure 16
<p>Contact pressure profiles introduced into FE analysis in different study cases (at δ/D = 1.20, starting from the top of the pipe and moving in a counter-clockwise direction).</p> Full article ">Figure 17
<p>Results of the processing of the numerical results for the case CS-01.</p> Full article ">Figure 18
<p>Comparison of soil stiffness results over time.</p> Full article ">Figure 19
<p>Force reaction results for the soil stiffness cases over time.</p> Full article ">Figure 20
<p>Soil berm and shear band formation.</p> Full article ">Figure 21
<p>Comparison of flexible pipe internal diameter results over time.</p> Full article ">Figure 22
<p>Force reaction results for the internal diameter cases over time.</p> Full article ">Figure 23
<p>Comparison of burial depth results over time.</p> Full article ">Figure 24
<p>Force reaction results for the burial depth cases over time.</p> Full article ">Figure 25
<p>Extreme value comparisons for all the parametric analyses.</p> Full article ">Figure 26
<p>Wet and dry collapse of the cross-section for case CS-03. Red areas show the largest displacements.</p> Full article ">
17 pages, 5127 KiB  
Article
A Web-Based Interactive Application to Simulate and Correct Distortion in Multibeam Sonars
by Guillermo Boyra and Udane Martinez
J. Mar. Sci. Eng. 2024, 12(7), 1237; https://doi.org/10.3390/jmse12071237 - 22 Jul 2024
Viewed by 638
Abstract
Multibeam sonars are advanced scientific tools for estimating fish school volume and density, using multiple beams to provide comprehensive size measurements of detected targets. However, challenges remain in accurately estimating target dimensions due to beam geometric expansion and overlap, particularly in athwart-beam measurements, [...] Read more.
Multibeam sonars are advanced scientific tools for estimating fish school volume and density, using multiple beams to provide comprehensive size measurements of detected targets. However, challenges remain in accurately estimating target dimensions due to beam geometric expansion and overlap, particularly in athwart-beam measurements, which tend to be overestimated with increasing distance from the transducer. We present an interactive web application that simulates distortion caused by beam overlap and expansion in multibeam sonars using simple geometric equations. Users can define sonar characteristics, such as the number of beams, swath opening, or degree of overlap, as well as specify an elliptical target’s dimensions, orientation, and distance from the transducer. The application estimates and visualises the true and distorted shapes of the target, calculating the level of distortion. It can run simulations in both forward and inverse directions, either simulating the distortion of a true school or correcting the shape of a distorted school. This tool aims to enhance the interpretation of multibeam sonar signals and improve the accuracy of target dimension estimates, facilitating more effective use of these sonars in scientific research. Full article
(This article belongs to the Section Ocean Engineering)
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<p>Illustration of how beam overlap causes athwart distortion in multibeam sonars. We highlight the difference between <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>φ</mi> </mrow> <mrow> <mi>a</mi> <mi>n</mi> <mi>g</mi> </mrow> </msub> </mrow> </semantics></math>, the angular separation between adjacent beams (the boundaries between beams marked by grey dashed lines), and <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>φ</mi> </mrow> <mrow> <mi>b</mi> <mi>w</mi> </mrow> </msub> </mrow> </semantics></math>, the width of each individual beam (bounded by a solid black line, with an arrow signalling the centre of the beam). In (<b>a</b>), <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>φ</mi> </mrow> <mrow> <mi>b</mi> <mi>w</mi> </mrow> </msub> <mo>=</mo> <msub> <mrow> <mi>φ</mi> </mrow> <mrow> <mi>a</mi> <mi>n</mi> <mi>g</mi> </mrow> </msub> </mrow> </semantics></math>, there is no overlap between the beams (the degree of overlap, <math display="inline"><semantics> <mrow> <mi>D</mi> <mi>O</mi> </mrow> </semantics></math>, is zero) and the beams outside the target are unable to detect it, hence causing no distortion. The beams that can detect the target in the absence of distortion are marked in blue. As <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>φ</mi> </mrow> <mrow> <mi>b</mi> <mi>w</mi> </mrow> </msub> </mrow> </semantics></math> exceeds <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>φ</mi> </mrow> <mrow> <mi>a</mi> <mi>n</mi> <mi>g</mi> </mrow> </msub> </mrow> </semantics></math>, overlap occurs between adjacent beams, as shown for three increasing overlap values (<b>b</b>–<b>d</b>). The degree of overlap measures the number of beams outside the true edge of the target that can detect the aggregation from either side (Ndist), thus distorting the apparent shape of the target on the echogram from a blue filled circle to a wider red ellipse.</p> Full article ">Figure 2
<p>(<b>a</b>) Example of a simulated sonar swath with the following combination of parameters: <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>ϴ</mi> </mrow> <mrow> <mi>B</mi> <mi>W</mi> </mrow> </msub> </mrow> </semantics></math> = 120°, <span class="html-italic">N</span> = 32 beams, <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>R</mi> </mrow> <mrow> <mi>m</mi> <mi>a</mi> <mi>x</mi> </mrow> </msub> </mrow> </semantics></math> = 500 m, and <math display="inline"><semantics> <mrow> <mo>∆</mo> <mi>R</mi> </mrow> </semantics></math> = 25 m. (<b>b</b>) The defined swath plus an ellipse with the following parameters: <span class="html-italic">R<sub>CM</sub></span> = 260 m, <span class="html-italic">a</span> = 250 m, <span class="html-italic">b</span> = 100 m, α = 0°. (<b>c</b>) The defined swath after selecting and marking in red the samples that are inside the ellipse. (<b>d</b>) The swath samples distinguishing the samples belonging to the true school (in blue), to the distorted part of the school (in red), and to neither (in black).</p> Full article ">Figure 3
<p>Area of each sample defined as the difference between the areas of the circular sectors defined by the opening angle <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>φ</mi> </mrow> <mrow> <mi>a</mi> <mi>n</mi> <mi>g</mi> </mrow> </msub> </mrow> </semantics></math> and the ranges <span class="html-italic">R<sub>s+</sub></span><sub>1</sub> and <span class="html-italic">R<sub>s</sub></span>.</p> Full article ">Figure 4
<p>Controls for operating and entering the simulation parameters in the graphic user interface of the web-based application, where the user can define the parameters to run the simulation. Panel (<b>a</b>) displays the controls to simulate the sonar swath; panel (<b>b</b>) displays the controls to simulate the target. At the bottom part of both is the switch to access the Help information panel: if turned on, the main part of the app will display some text explaining the simulation and providing links to the literature used.</p> Full article ">Figure 5
<p>Main display of the simulation results in the graphic user interface of the web-based application to run the simulation displaying the different types of samples (SampleType) in a sonar echogram: those of the true target (School) and the distorted ones (Distortion). The graph title presents information on the true and observed areas, and the estimated percentage of distortion. The central part shows the true and apparent shapes of the simulated target. The text boxes at the bottom inform about the estimated values of distance between beams (<math display="inline"><semantics> <mrow> <msub> <mrow> <mi>φ</mi> </mrow> <mrow> <mi>a</mi> <mi>n</mi> <mi>g</mi> </mrow> </msub> </mrow> </semantics></math>) and individual beamwidth (<math display="inline"><semantics> <mrow> <msub> <mrow> <mi>φ</mi> </mrow> <mrow> <mi>b</mi> <mi>w</mi> </mrow> </msub> </mrow> </semantics></math>).</p> Full article ">Figure 6
<p>Main application panel showing different sonar geometries, with different swath opening angles (from left to right, (<b>a</b>) <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>φ</mi> </mrow> <mrow> <mi>N</mi> </mrow> </msub> </mrow> </semantics></math> = 65° and (<b>b</b>) 360°).</p> Full article ">Figure 7
<p>Evolution of the linear athwart distortion as a function of K, the number of beams detecting the target, and the degree of overlap. For large numbers of beams, the distortion tends to zero regardless of the overlap.</p> Full article ">Figure 8
<p>Comparison of the ratio of true (A0) and distorted (Aobs) cross-sectional areas of the simulated schools as a function of detection range. The sonar and target configurations of the simulation experiment conducted by [<a href="#B26-jmse-12-01237" class="html-bibr">26</a>] were reproduced and simulated with the application developed in this study. The distortion results with this simulation (Ratio) were compared with those obtained with their simulation at two different threshold values (R_36dB and R_41dB).</p> Full article ">Figure A1
<p>Main application panel showing the same target displayed by a simulated sonar with increasing degrees of overlap (from left to right and from top to bottom, DO = 0, 2, 4, 6 beams).</p> Full article ">Figure A2
<p>Main application panel showing the same target displayed by a simulated sonar with increasing number of beams (from left to right and from top to bottom, N = 16, 32, 48, 64 beams).</p> Full article ">Figure A3
<p>Main application panel showing the same target displayed by a simulated sonar with increasing target range (from left to right and from top to bottom, RCM = 100, 200, 300, 400 m).</p> Full article ">Figure A4
<p>Main application panel showing the same target displayed by a simulated sonar with increasing major axis angle (from left to right and from top to bottom, α = 0°, 30°, 60°, 90°).</p> Full article ">Figure A5
<p>Main application panel showing a target with increasing horizontal diameter displayed by the same simulated sonar (from left to right and from top to bottom, <math display="inline"><semantics> <mrow> <msub> <mrow> <mi mathvariant="sans-serif">Φ</mi> </mrow> <mrow> <mi>w</mi> </mrow> </msub> </mrow> </semantics></math> = 100, 150, 200, 250 m).</p> Full article ">
21 pages, 11469 KiB  
Article
Development of Hardware-in-the-Loop Simulation Test Bed to Verify and Validate Power Management System for LNG Carriers
by Kwangkook Lee
J. Mar. Sci. Eng. 2024, 12(7), 1236; https://doi.org/10.3390/jmse12071236 - 22 Jul 2024
Viewed by 727
Abstract
Liquefied natural gas carrier (LNGC) orders are increasing owing to marine environment regulations. The complexity of the integrated system applied to shipbuilding and software errors have increased with the high degree of automation. Direct on-site inspection methods are associated with high costs and [...] Read more.
Liquefied natural gas carrier (LNGC) orders are increasing owing to marine environment regulations. The complexity of the integrated system applied to shipbuilding and software errors have increased with the high degree of automation. Direct on-site inspection methods are associated with high costs and safety risks, whereas software-based simulations rely heavily on the accuracy of the models of power system components. Hardware-in-the-loop simulation (HILS) can be utilized for designing and testing intricate real-time embedded systems. Specifically, HILS offers a reliable means of evaluating power management system (PMS) performance for LNGCs, which are high-value vessels commonly used in offshore plants. This study proposes a PMS–HIL test bed comprising a power supply unit, consumer, simulation control console, and main switchboard. The proposed HILS test bed utilizes the real equipment data of the shipbuilding industry to replicate the conditions associated with actual LNGCs. The proposed system is verified and validated through a software acceptance test procedure. Additionally, load-sharing, load-dependent start, blackout prevention, and preferential tests are performed for the PMS function evaluation. Test results indicate that the proposed system has great potential for conventional PMS commissioning. Therefore, it exhibits the potential to replace traditional factory acceptance tests. Additional development of the system will be conducted for ship automation, utilizing PMS control and an energy management system. Full article
(This article belongs to the Topic Advances in Intelligent Construction, Operation and Maintenance)
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Figure 1
<p>Low-end and high-end market segments within ship automation and management levels [<a href="#B1-jmse-12-01236" class="html-bibr">1</a>].</p> Full article ">Figure 2
<p>Software quality assurance process with the hardware-in-the-loop test.</p> Full article ">Figure 3
<p>HIL simulator and target system concept for LNGC-PMS.</p> Full article ">Figure 4
<p>Modeling logic description of power supply.</p> Full article ">Figure 5
<p>Simulation verification of the power supply model.</p> Full article ">Figure 6
<p>Modeling logic of the heavy consumer model.</p> Full article ">Figure 7
<p>Simulation verification of heavy consumer model.</p> Full article ">Figure 8
<p>Integrated HIL simulation model of the power supply and heavy consumer.</p> Full article ">Figure 9
<p>Integrated HIL test bed for the LNGC power management system.</p> Full article ">Figure 10
<p>Network configuration of the PMS-HIL test bed.</p> Full article ">Figure 11
<p>Condition monitoring GUI of the PMS-HIL test bed. Blue circles means ‘connected or on’ and red circles means ‘disconnected or off’.</p> Full article ">Figure 12
<p>Air circuit breaker test of the main switchboard (MSBD).</p> Full article ">Figure 13
<p>Symmetric load-sharing test.</p> Full article ">Figure 14
<p>Asymmetric load-sharing test.</p> Full article ">Figure 15
<p>Load-dependent test.</p> Full article ">Figure 16
<p>Blackout prevention test.</p> Full article ">Figure 17
<p>Preferential trip test.</p> Full article ">
14 pages, 3124 KiB  
Article
Effects of Angle of Attack on Flow-Induced Vibration of a D-Section Prism
by Shiguang Fan, Zhuang Li, Jining Song, Xietian Du and Juan Wang
J. Mar. Sci. Eng. 2024, 12(7), 1235; https://doi.org/10.3390/jmse12071235 - 22 Jul 2024
Viewed by 478
Abstract
The VIVACE device, which utilizes flow-induced vibration for harvesting ocean current energy, has been a research hotspot in the field of renewable energy. In this study, the flow-induced vibration characteristics and energy conversion efficiency of a D-section prism were investigated using the k-ω [...] Read more.
The VIVACE device, which utilizes flow-induced vibration for harvesting ocean current energy, has been a research hotspot in the field of renewable energy. In this study, the flow-induced vibration characteristics and energy conversion efficiency of a D-section prism were investigated using the k-ω SST turbulence model and Newmark-β method. The vibration amplitude, frequency, equilibrium position offset, and energy conversion efficiency of the two-degree-of-freedom cylinder were systematically analyzed at seven angles of attack between 0 and 180 degrees. The Reynolds number ranged from 368 to 14,742, corresponding to equivalent speeds of 2 to 20. The results indicate that the angle of attack has a significant influence on the flow-induced vibration response of the D-section prism. As the angle of attack changes, the vibration amplitude of the cylinder continuously increases, and the cylinder sequentially enters the vortex-induced vibration, vortex-induced vibration-galloping, and fully galloping branches. The change in the angle of attack disrupts the symmetry of the cylinder’s vibration in the streamwise direction, leading to a shift in the equilibrium position of the cylinder’s vibration. When the angle of attack is 0°, the energy conversion efficiency of the column reaches a maximum of 11.75%. Additionally, at high Reynolds numbers, the vibration of the cylinder is not self-limiting, making it more advantageous for energy conversion devices compared to cylinders with circular cross-sections. Full article
(This article belongs to the Special Issue The State of the Art of Marine Risers and Pipelines)
►▼ Show Figures

Figure 1

Figure 1
<p>D-section prism: (<b>a</b>) schematic of D-section prism; (<b>b</b>) computational domain and boundary conditions.</p> Full article ">Figure 2
<p>The computational grid (Mesh3): (<b>a</b>) overall grid; (<b>b</b>) adaptive grid.</p> Full article ">Figure 3
<p>Variations in transverse amplitude versus reduced velocity.</p> Full article ">Figure 4
<p>Variations in transverse equilibrium position offset with reduced velocity.</p> Full article ">Figure 5
<p>Variations of transverse dominant frequency with reduced velocity.</p> Full article ">Figure 6
<p>Transverse displacement histories and spectra of a D-section prism.</p> Full article ">Figure 7
<p>Comparison of the energy conversion efficiency at different angles of attack.</p> Full article ">
26 pages, 22542 KiB  
Article
Numerical Study on the Anti-Sloshing Effect of Horizontal Baffles in a Cargo Hold Loaded with Liquefied Cargo
by Jianwei Zhang, Anqi Wang, Peng Chen, Jian Liu and Deqing Yang
J. Mar. Sci. Eng. 2024, 12(7), 1234; https://doi.org/10.3390/jmse12071234 - 22 Jul 2024
Viewed by 553
Abstract
Sloshing of liquefied bulk granular cargoes weakens the stability of cargo carriers when at sea. Using the horizontal rectangle baffle is a promising way to restrain its sloshing motion. But the location height and optimal baffle area rate to achieve a better anti-sloshing [...] Read more.
Sloshing of liquefied bulk granular cargoes weakens the stability of cargo carriers when at sea. Using the horizontal rectangle baffle is a promising way to restrain its sloshing motion. But the location height and optimal baffle area rate to achieve a better anti-sloshing effect should be studied first. The discrete element method was adopted to establish the simulation model, and the direct shear test was used for verification. Through the static tilt tests, the definite relationship between the effects of moisture content on cargo motion and particle friction coefficients was acquired. Then, liquefied cargo motion in a cargo hold without baffles and with one and two pairs of horizontal baffles was simulated. Based on variations in the cargo gravity center offset and the sloshing-induced force on the cargo hold, the anti-sloshing effect of different settings of the baffles was compared. Results show that the baffles have the ability to restrain cargo sloshing, and this is important for sea transportation safety. The anti-sloshing effect is better when the baffle plane is right on the cargo top surface compared to the other location heights. Further, there is an optimal length–width combination, e.g., a single baffle plane with a length of 0.26 L and a width of 0.46 B, at which a better anti-sloshing effect could be achieved with the smallest baffle area rate. This study could be useful for the practical application of horizontal baffles for bulk granular cargo carriers. Full article
(This article belongs to the Special Issue Innovative Technologies in Safety and Reliability of Marine Engineering)
►▼ Show Figures

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Figure 1
<p>The nicker ore cargo when initially loaded (<b>left</b>), after being liquefied (<b>middle</b>), and the carrier capsizing accident (<b>right</b>) [<a href="#B7-jmse-12-01234" class="html-bibr">7</a>].</p> Full article ">Figure 2
<p>Linear contact behavior of two particles [<a href="#B33-jmse-12-01234" class="html-bibr">33</a>].</p> Full article ">Figure 3
<p>Cargo hold model loaded with particles (particle radius is 0.01 m).</p> Full article ">Figure 4
<p>Time history of the sway motion.</p> Full article ">Figure 5
<p>Force in x-direction on the bulkhead for the 6 different cases.</p> Full article ">Figure 6
<p>Offset of cargo gravity center in x-direction for the 6 different cases.</p> Full article ">Figure 7
<p>Schematic diagram of direct shear test process (different colors represent different particle radii). (<b>a</b>) Initial shearing status. (<b>b</b>) Completed shearing status.</p> Full article ">Figure 8
<p>Fitting curve of shear and normal strength.</p> Full article ">Figure 9
<p>Static inclination test results by Class NK under different moisture content levels [<a href="#B11-jmse-12-01234" class="html-bibr">11</a>].</p> Full article ">Figure 9 Cont.
<p>Static inclination test results by Class NK under different moisture content levels [<a href="#B11-jmse-12-01234" class="html-bibr">11</a>].</p> Full article ">Figure 10
<p>The present simulation results of static tilt test under different moisture contents.</p> Full article ">Figure 11
<p>Numerical model of the cargo hold loaded with nickel ore particles.</p> Full article ">Figure 12
<p>Motion status of liquefied nickel ore in the cargo hold during one motion period.</p> Full article ">Figure 13
<p>Offset of cargo gravity center in the x-direction.</p> Full article ">Figure 14
<p>Sloshing-induced force on the cargo hold.</p> Full article ">Figure 15
<p>Motion of the liquefied nickel ore in the cargo hold during one motion period with one pair of horizontal baffles.</p> Full article ">Figure 16
<p>Variation in cargo gravity center in the x-direction with one pair of horizontal baffles.</p> Full article ">Figure 16 Cont.
<p>Variation in cargo gravity center in the x-direction with one pair of horizontal baffles.</p> Full article ">Figure 17
<p>Variation in sloshing-induced force on the cargo hold with one pair of horizontal baffles.</p> Full article ">Figure 18
<p>Maximum offset of the cargo gravity center in the x-direction with one pair of horizontal baffles. (<b>a</b>) h1 = 180 mm, (<b>b</b>) h2 = 160 mm, (<b>c</b>) h3 = 140 mm, (<b>d</b>) h4 = 120 mm.</p> Full article ">Figure 19
<p>Maximum force on the cargo hold with one pair of horizontal baffles. (<b>a</b>) h1 = 180 mm, (<b>b</b>) h2 = 160 mm, (<b>c</b>) h3 = 140 mm, (<b>d</b>) h4 = 120 mm.</p> Full article ">Figure 20
<p>Maximum offset of the center of gravity in the x-direction with different baffle width.</p> Full article ">Figure 21
<p>Maximum force of the cargo hold with different baffle width.</p> Full article ">Figure 22
<p>Maximum offset of the center of gravity in the x-direction with different baffle length.</p> Full article ">Figure 23
<p>Maximum force of the cargo hold with different baffle length.</p> Full article ">Figure 24
<p>Top view of arrangements of modular horizontal baffles.</p> Full article ">Figure 25
<p>Displacement of gravity center of the cargo in x-direction of modular horizontal baffles.</p> Full article ">Figure 26
<p>Force of the cargo hold under modular horizontal baffles.</p> Full article ">Figure 26 Cont.
<p>Force of the cargo hold under modular horizontal baffles.</p> Full article ">Figure 27
<p>Comparison of the maximum offset of the center of gravity of the cargo.</p> Full article ">Figure 28
<p>Comparison of the maximum force on the cargo hold.</p> Full article ">Figure 29
<p>Maximum offset of the cargo gravity center in the x-direction for different baffle lengths and widths. (<b>a</b>) when the area rate is 48%, (<b>b</b>) when the area rate is 64%.</p> Full article ">Figure 30
<p>Maximum force on the cargo hold for different baffle lengths and widths. (<b>a</b>) when the area rate is 48%, (<b>b</b>) when the area rate is 64%.</p> Full article ">
16 pages, 4745 KiB  
Article
Influence of Grid Resolution and Assimilation Window Size on Simulating Storm Surge Levels
by Xin Bi, Wenqi Shi, Junli Xu and Xianqing Lv
J. Mar. Sci. Eng. 2024, 12(7), 1233; https://doi.org/10.3390/jmse12071233 - 22 Jul 2024
Viewed by 510
Abstract
Grid resolution and assimilation window size play significant roles in storm surge models. In the Bohai Sea, Yellow Sea, and East China Sea, the influence of grid resolution and assimilation window size on simulating storm surge levels was investigated during Typhoon 7203. In [...] Read more.
Grid resolution and assimilation window size play significant roles in storm surge models. In the Bohai Sea, Yellow Sea, and East China Sea, the influence of grid resolution and assimilation window size on simulating storm surge levels was investigated during Typhoon 7203. In order to employ a more realistic wind stress drag coefficient that varies with time and space, we corrected the storm surge model using the spatial distribution of the wind stress drag coefficient, which was inverted using the data assimilation method based on the linear expression Cd = (a + b × U10) × 10−3. Initially, two grid resolutions of 5′ × 5′ and 10′ × 10′ were applied to the numerical storm surge model and adjoint assimilation model. It was found that the influence of different grid resolutions on the numerical model is almost negligible. But in the adjoint assimilation model, the root mean square (RMS) errors between the simulated and observed storm surge levels under 5′ × 5′ and 10′ × 10′ grid resolutions were 11.6 cm and 15.6 cm, and the average PCC and WSS values for 10 tidal stations changed from 89% and 92% in E3 to 93% and 96% in E4, respectively. The results indicate that the finer grid resolution can yield a closer consistency between the simulation and observations. Subsequently, the effects of assimilation window sizes of 6 h, 3 h, 2 h, and 1 h on simulated storm surge levels were evaluated in an adjoint assimilation model with a 5′ × 5′ grid resolution. The results show that the average RMS errors were 11.6 cm, 10.6 cm, 9.6 cm, and 9.3 cm under four assimilation window sizes. In particular, the RMS errors for the assimilation window sizes of 1 h and 6 h at RuShan station were 3.9 cm and 10.2 cm, a reduction of 61.76%. The PCC and WSS values from RuShan station in E4 and E7 separately showed significant increases, from 85% to 98% and from 92% to 99%. These results demonstrate that when the assimilation window size is smaller, the simulated storm surge level is closer to the observation. Further, the results show that the simulated storm surge levels are closer to the observation when using the wind stress drag coefficient with a finer grid resolution and smaller temporal resolution. Full article
(This article belongs to the Special Issue Ocean Modeling and Data Assimilation)
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<p>Typhoon path and locations of tidal stations. Star symbols represent the tidal stations’ locations. The solid line indicates the path of Typhoon 7203. Circles represent the time.</p> Full article ">Figure 2
<p>Simulated storm surge levels in E1–E4 and the observed level (<b>top</b>), and the differences between the simulation and observation (<b>bottom</b>) at YingKou station during Typhoon 7203.</p> Full article ">Figure 3
<p>Simulated storm surge levels in E1–E4 and the observed level (<b>top</b>), and the differences between the simulation and observation (<b>bottom</b>) at HuLuDao station during Typhoon 7203.</p> Full article ">Figure 4
<p>Simulated storm surge levels in E1–E4 and the observed level (<b>top</b>), and the differences between the simulation and observation (<b>bottom</b>) at YanTai station during Typhoon 7203.</p> Full article ">Figure 5
<p>Peaks of simulated and observed storm surge levels in E1-E4 at HuLuDao and YingKou stations.</p> Full article ">Figure 6
<p>Simulated storm surge levels in E4–E7 and observed level (<b>top</b>), and the differences between the simulation and observation (<b>bottom</b>) at YingKou station during Typhoon 7203.</p> Full article ">Figure 7
<p>Simulated storm surge levels in E4–E7 and observed level (<b>top</b>), and the differences between the simulation and observation (<b>bottom</b>) at LongKou station during Typhoon 7203.</p> Full article ">Figure 8
<p>Simulated storm surge levels in E4–E7 and observed level (<b>top</b>), and the differences between the simulation and observation (<b>bottom</b>) at RuShan station during Typhoon 7203.</p> Full article ">
17 pages, 9060 KiB  
Article
The Influence of Pre-Chamber Parameters on the Performance of a Two-Stroke Marine Dual-Fuel Low-Speed Engine
by Hao Guo, Zhongcheng Wang, Song Zhou, Ming Zhang and Majed Shreka
J. Mar. Sci. Eng. 2024, 12(7), 1232; https://doi.org/10.3390/jmse12071232 - 22 Jul 2024
Viewed by 525
Abstract
With increasing environmental pollution from ship exhaust emissions and increasingly stringent International Maritime Organization carbon regulations, there is a growing demand for cleaner and lower-carbon fuels and near-zero-emission marine engines worldwide. Liquefied natural gas is a low-carbon fuel, and when liquefied natural gas [...] Read more.
With increasing environmental pollution from ship exhaust emissions and increasingly stringent International Maritime Organization carbon regulations, there is a growing demand for cleaner and lower-carbon fuels and near-zero-emission marine engines worldwide. Liquefied natural gas is a low-carbon fuel, and when liquefied natural gas (LNG) is used on ships, dual-fuel methods are often used. The pre-chamber plays a key role in the working process of dual-fuel engines. In this paper, an effective three-dimensional simulation model based on the actual operating conditions and structural characteristics of a marine low-pressure dual-fuel engine is established. In addition, the effects of changing the Precombustion chamber (PCC) volume ratio and the PCC orifice diameter ratio on the mixture composition, engine combustion performance, and pollutant generation were thoroughly investigated. It was found that a small PPC volume ratio resulted in a higher flame jet velocity, a shorter stagnation period, and an acceleration of the combustion process in the main combustion chamber. When the PCC volume was large, the Nitrogen oxygen (NOx) ratio emission was elevated. Moreover, the angle of the PCC orifice affected the flame propagation direction of the pilot fuel. Optimizing the angle of the PCC orifice can improve combustion efficiency and reduce the generation of NOx. Furthermore, reasonable arrangement of the PCC structure can improve the stability of ignition performance and accelerate the flame jet velocity. Full article
(This article belongs to the Topic Safety, Reliability and Effectiveness of Internal Combustion Engines)
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<p>Three-dimensional model of an LP-DF engine.</p> Full article ">Figure 2
<p>A 1D model of a 2-stroke LP-DF engine.</p> Full article ">Figure 3
<p>Comparison of simulated and experimental data of the pressure and HRR under 75% load.</p> Full article ">Figure 4
<p>Scheme of different PCC volume ratios.</p> Full article ">Figure 5
<p>Effect of different PCC volume ratios on O<sub>2</sub> mass in PCC.</p> Full article ">Figure 6
<p>Effect of different PCC volume ratios on CH<sub>4</sub> mass in PCC.</p> Full article ">Figure 7
<p>MCC indicated pressure under different PCC volume ratios.</p> Full article ">Figure 8
<p>MCC temperature under different PCC volume ratios.</p> Full article ">Figure 9
<p>Mean pressure in PCC under different PCC volume ratios.</p> Full article ">Figure 10
<p>HRR in PCC under different PCC volume ratios.</p> Full article ">Figure 11
<p>Mean temperature in MCC and PCC under different PCC volume ratios.</p> Full article ">Figure 12
<p>Temperature iso-surface at 1800 K in the MCC with different PCC volume ratios.</p> Full article ">Figure 13
<p>Combustion characteristic parameters under different PCC volume ratios.</p> Full article ">Figure 14
<p>The influence of different PCC volume ratios on the MCC HRR.</p> Full article ">Figure 15
<p>Effect of different PCC volume ratios on NOx emissions.</p> Full article ">Figure 16
<p>Effect of different PCC volume ratios on engine efficiency and NOx emissions in MCC.</p> Full article ">Figure 17
<p>Three different PCC ωr in simulation cases. (<b>a</b>) ωr = 2%. (<b>b</b>) ωr = 3.2%. (<b>c</b>) ωr = 4.8%.</p> Full article ">Figure 18
<p>Mean air–fuel ratio and in-cylinder temperature under different PCC orifice diameter ratios (ωr). (<b>a</b>) Mean air–fuel ratio in PCC. (<b>b</b>) Minimum air–fuel ratio and temperature at SOI moment in PCC.</p> Full article ">Figure 19
<p>Temperature iso-surface at 1800 K in the MCC with under different PCC orifice diameter ratios (ωr).</p> Full article ">Figure 20
<p>HRR in PCC and combustion characteristic parameters under different PCC ωr. (<b>a</b>) HRR in PCC. (<b>b</b>) Combustion characteristic parameters of MCC.</p> Full article ">Figure 21
<p>Mean in-cylinder pressure and HRR under different PCC ωr.</p> Full article ">Figure 22
<p>Mean in-cylinder temperature under different PCC ωr.</p> Full article ">Figure 23
<p>Engine efficiency and emissions under different PCC ωr. (<b>a</b>) Engine indicated thermal efficiency. (<b>b</b>) NOx and HC emissions.</p> Full article ">
29 pages, 11126 KiB  
Article
Design of Inner Ribs with Unequal Stiffness for Deep-Sea Highly Pressure-Resistant Cylindrical Shells and Utilizing NSGA-2 for Lightweight Optimization
by Yizhe Huang, Xiao Wang, Zhiqiang Liu, Ying You and Haoxiang Ma
J. Mar. Sci. Eng. 2024, 12(7), 1231; https://doi.org/10.3390/jmse12071231 - 21 Jul 2024
Viewed by 758
Abstract
For conducting scientific research at depths in the ocean, deep-sea probes are essential pieces of equipment. The cylindrical shell is the most sensible and rational packaging structure for these detectors. New technical challenges for enhancing the pressure resistance and lightweight design of the [...] Read more.
For conducting scientific research at depths in the ocean, deep-sea probes are essential pieces of equipment. The cylindrical shell is the most sensible and rational packaging structure for these detectors. New technical challenges for enhancing the pressure resistance and lightweight design of the pressure-resistant cylindrical shell arise from the need to ensure that the detector packaging structure can withstand the immense water pressure at tens of thousands of meters in the underwater environment, while simultaneously reducing the detector packaging structure’s self-weight. This article examines the detection system’s deep-sea pressure-resistant cylindrical shell. To address the issue of the pressure-resistant shell’s insufficient ability to counteract the overall instability caused by the inability to form unstable half-waves in the radial direction when the ring rib pressure-resistant shell experiences it, a design method for the ribs inside the unequal-stiffness pressure-resistant cylindrical shell is suggested. The shell’s instability pressure increases by 9.65 MPa following the stiffness adjustment. Simultaneously, in order to attain even more lightweight optimization, the optimal inner rib section was obtained by applying the orthogonal topology optimization method, which also reduced the weight by 106.8 g and effectively improved the compression stability of the high-pressure cylindrical shell structure. Based on this, key optimization variables were found by performing sensitivity analysis on the cylindrical shell structure’s parameters. Then, with lightweighting as the primary objective, the high-pressure-resistant cylindrical shell’s optimal structural parameters were found using a multi-objective optimization process using the second-generation fast non-dominated genetic algorithm (NSGA-2). This resulted in a weight reduction of 1.2492 kg, or 17.26% of the original pressure-resistant shell. This has led to the development of a lightweight, highly pressure-resistant method for packaging marine exploration equipment structures. Full article
(This article belongs to the Section Ocean Engineering)
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<p>Schematic diagram of internal sensor size parameters in the detection system.</p> Full article ">Figure 2
<p>Schematic diagram of cylindrical shell’s cylindrical coordinate system.</p> Full article ">Figure 3
<p>Eigenvalue buckling analysis deformation diagram. (<b>a</b>) Instability mode of shell with equal stiffness and inner ribs; (<b>b</b>) instability mode of inner rib stiffness adjustment shell characteristics.</p> Full article ">Figure 4
<p>Mises shell stress cloud maps: (<b>a</b>) Mises stress cloud map of the inner rib cage with the same degree of rigidity; (<b>b</b>) the inner rib stiffness adjustment shell’s Mises stress cloud map.</p> Full article ">Figure 5
<p>Mises stress cloud maps of ribs: (<b>a</b>) Mises stress cloud map of ribs with equal stiffness; (<b>b</b>) Mises stress cloud map for rib stiffness adjustment.</p> Full article ">Figure 6
<p>Diagrams of relationships between assessment indicators and various levels. (<b>a</b>) The relationship between different levels and maximum stress; (<b>b</b>) the relationship between different levels and iteration steps.</p> Full article ">Figure 7
<p>Maximum stress cloud and section diagram after optimizing A<sub>3</sub>B<sub>1</sub>C<sub>3</sub> combination. (<b>a</b>) Maximum stress cloud map; (<b>b</b>) cross-section view.</p> Full article ">Figure 8
<p>Maximum stress cloud and section diagram after optimizing A<sub>1</sub>B<sub>2</sub>C<sub>3</sub> combination. (<b>a</b>) Maximum stress cloud map; (<b>b</b>) cross-section view.</p> Full article ">Figure 9
<p>Outline drawings of reinforced rib section after processing: (<b>a</b>) 3D schematic diagram of strengthening ribs; (<b>b</b>) 2D schematic diagram of strengthening ribs.</p> Full article ">Figure 10
<p>Definition diagram of arched rib parameters.</p> Full article ">Figure 11
<p>Cross-section diagram of pressure-resistant shell.</p> Full article ">Figure 12
<p>Sensitivity relationship curve between design variables and stress.</p> Full article ">Figure 13
<p>Sensitivity relationship curve between design variables and buckling characteristic values.</p> Full article ">Figure 14
<p>Sensitivity relationship curve between design variables and quality.</p> Full article ">Figure 15
<p>Bar chart showing the sensitivity relationship between design variables and various output responses.</p> Full article ">Figure 16
<p>NSGA-2 process diagram.</p> Full article ">Figure 17
<p>Comparison between predicted and simulated stress values for pressure-resistant shells.</p> Full article ">Figure 18
<p>Comparison between predicted and simulated buckling characteristic values of pressure-resistant shells.</p> Full article ">Figure 19
<p>Comparison between predicted and simulated values of pressure shell quality.</p> Full article ">Figure 20
<p>The trends of changes between different response values: (<b>a</b>) stress–buckling characteristic value; (<b>b</b>) quality–buckling characteristic value; (<b>c</b>) stress–quality.</p> Full article ">Figure 21
<p>Schematic diagram of pressure-resistant housing structure.</p> Full article ">
14 pages, 3002 KiB  
Article
Uncertainty Analysis and Maneuver Simulation of Standard Ship Model
by Hui Li, Nan Zhao, Jian Zhou, Xiangyu Chen and Chenxu Wang
J. Mar. Sci. Eng. 2024, 12(7), 1230; https://doi.org/10.3390/jmse12071230 - 21 Jul 2024
Viewed by 586
Abstract
Maneuver simulation of a standard ship model gives indication of numerical accuracy. In the numerical calculation of ship maneuvering, uncertainty analysis is a necessary step to ensure the accuracy of the calculation. In this study, uncertainty pair analysis is carried out in the [...] Read more.
Maneuver simulation of a standard ship model gives indication of numerical accuracy. In the numerical calculation of ship maneuvering, uncertainty analysis is a necessary step to ensure the accuracy of the calculation. In this study, uncertainty pair analysis is carried out in the simulation of the turning circle motion of the standard ship model ONRT in waves. According to the uncertainty analysis procedure recommended by the International Towing Tank Conference (ITTC), the change of ship resistance caused by the number of grids is studied to determine the influence of grid density on the numerical prediction. The simulation of turning motion in waves is carried out based on the uncertainty analysis. It is found that the minimum number of overset grids for this simulation is 1.4 million. The numerical results are fairly accurate compared to experimental results, and this technique provides a method with low calculated cost for this simulation. Full article
(This article belongs to the Special Issue Application of Advanced Technologies in Maritime Safety)
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<p>The model of ONRT. (<b>a</b>) Side of the model; (<b>b</b>) Planform of the model; (<b>c</b>) Bow of the model; (<b>d</b>) Stern of the model.</p> Full article ">Figure 2
<p>Surface grid of the ship with different sizes. (<b>a</b>) Fine grid; (<b>b</b>) Medium grid; (<b>c</b>) Coarse grid.</p> Full article ">Figure 3
<p>Distribution of overset grid in the longitudinal section of the computational domain.</p> Full article ">Figure 4
<p>Comparison of the turning motion trajectory in waves between numerical predictions and experimental data.</p> Full article ">Figure 5
<p>Heave versus time during Free-Running Test.</p> Full article ">Figure 6
<p>Pitch versus time during the Free-Running Test.</p> Full article ">Figure 7
<p>Roll versus time during the Free-Running Test.</p> Full article ">Figure 8
<p>Free Surface Wave Profile during Steady-State Turning Phase.</p> Full article ">
31 pages, 7679 KiB  
Article
Numerical Study of the Ultra-High-Speed Aerodynamically Alleviated Marine Vehicle Motion Stability in Winds and Waves
by Yani Song, Xiaoxu Du and Yuli Hu
J. Mar. Sci. Eng. 2024, 12(7), 1229; https://doi.org/10.3390/jmse12071229 - 21 Jul 2024
Viewed by 558
Abstract
The ultra-high-speed aerodynamically alleviated marine vehicle (AAMV) is a high-performance vessel that combines a hydrodynamic configuration and an aerodynamic wing to reduce wave-making resistance during the high-speed planing phase. The forces of the AAMV exhibit strong nonlinear and water–air coupling characteristics, resulting in [...] Read more.
The ultra-high-speed aerodynamically alleviated marine vehicle (AAMV) is a high-performance vessel that combines a hydrodynamic configuration and an aerodynamic wing to reduce wave-making resistance during the high-speed planing phase. The forces of the AAMV exhibit strong nonlinear and water–air coupling characteristics, resulting in particularly complex motion characteristics. This paper presents a longitudinal and lateral stability model of the AAMV, which considers the effects of aerodynamic alleviation. Additionally, a numerical model of wind and wave turbulence forces is established, which considers viscous correction based on the potential theory. Finally, the effect of wind and wave turbulence forces on the motion stability of the AAMV under regular and irregular waves is analyzed by numerical solution. The simulation results demonstrate the influence of these disturbance forces on the stability of the AAMV under different sea states. The motion parameters of the AAMV exhibit a pronounced response to changes in sea state level. The aerodynamically alleviated effect is enhanced as speed increases, and the influence of winds and waves on the AAMV is greatly weakened, reducing the possibility of instability. During the cruising phase under class V sea state, the pitch, roll, and heave response are 0.210°, 0.0229°, and 0.0734 m, respectively. This effect can effectively improve the motion stability of the AAMV in winds and waves. Full article
(This article belongs to the Section Ocean Engineering)
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<p>The motion process of the AAMV.</p> Full article ">Figure 2
<p>The motion of the AAMV under the influence of winds and waves.</p> Full article ">Figure 3
<p>Overall shape of the AAMV.</p> Full article ">Figure 4
<p>Definitions of the main parameters.</p> Full article ">Figure 5
<p>Coordinate system of the AAMV.</p> Full article ">Figure 6
<p>Force analysis of the AAMV.</p> Full article ">Figure 7
<p>The forces of the AAMV in wind and waves.</p> Full article ">Figure 8
<p>Computational domain and mesh arrangement.</p> Full article ">Figure 9
<p>The incidence angle of the AAMV.</p> Full article ">Figure 10
<p>Comparison of the results of steady second-order force between the near-field method and the far-field method.</p> Full article ">Figure 11
<p>Comparison curve between the results obtained from the RANS method and the potential theory method. (<b>a</b>) surge force <span class="html-italic">F<sub>x</sub></span>; (<b>b</b>) heave force <span class="html-italic">F<sub>z</sub></span>; (<b>c</b>) pitch moment <span class="html-italic">M<sub>y</sub></span>.</p> Full article ">Figure 12
<p>First-order force transfer function. (<b>a</b>) surge; (<b>b</b>) sway; (<b>c</b>) heave; (<b>d</b>) roll; (<b>e</b>) pitch; (<b>f</b>) yaw.</p> Full article ">Figure 13
<p>Second-order force transfer function. (<b>a</b>) surge; (<b>b</b>) sway; (<b>c</b>) yaw.</p> Full article ">Figure 14
<p>Response curves for longitudinal motion in regular waves. (<b>a</b>) heave response curve; (<b>b</b>) heave velocity response curve; (<b>c</b>) heave acceleration response curve; (<b>d</b>) angle of attack response curve; (<b>e</b>) pitch angle response curve; (<b>f</b>) pitch angular velocity response curve.</p> Full article ">Figure 15
<p>Response curves for longitudinal motion in irregular waves. (<b>a</b>) heave response curve; (<b>b</b>) heave velocity response curve; (<b>c</b>) heave acceleration response curve; (<b>d</b>) angle of attack response curve; (<b>e</b>) pitch angle response curve; (<b>f</b>) pitch angular velocity response curve.</p> Full article ">Figure 16
<p>The response amplitude to longitudinal motion in the presence of different sea states and <span class="html-italic">Fr<sub>B</sub></span>. (<b>a</b>) heave; (<b>b</b>) heave velocity; (<b>c</b>) heave acceleration; (<b>d</b>) angle of attack; (<b>e</b>) pitch angle; (<b>f</b>) pitch angular velocity.</p> Full article ">Figure 17
<p>Response curves for lateral motion in regular waves. (<b>a</b>) roll angle response curve; (<b>b</b>) roll angular velocity response curve; (<b>c</b>) sideslip angle response curve; (<b>d</b>) yaw angle response curve; (<b>e</b>) yaw angular velocity response curve.</p> Full article ">Figure 18
<p>Response curves for lateral motion in irregular waves. (<b>a</b>) roll angle response curve; (<b>b</b>) roll angular velocity response curve; (<b>c</b>) sideslip angle response curve; (<b>d</b>) yaw angle response curve; (<b>e</b>) yaw angular velocity response curve.</p> Full article ">Figure 19
<p>The response amplitude to lateral motion in the presence of different sea states and <span class="html-italic">Fr<sub>B</sub></span>. (<b>a</b>) roll angle; (<b>b</b>) roll angular velocity; (<b>c</b>) sideslip angle; (<b>d</b>) yaw angle; (<b>e</b>) yaw angular velocity; (<b>f</b>) directional angle.</p> Full article ">
16 pages, 1946 KiB  
Article
Polyhydroxybutyrate Production from the Macroalga Rugulopteryx okamurae: Effect of Hydrothermal Acid Pretreatment
by Agustín Romero-Vargas, Luis Alberto Fdez-Güelfo, Ana Blandino and Ana Belén Díaz
J. Mar. Sci. Eng. 2024, 12(7), 1228; https://doi.org/10.3390/jmse12071228 - 21 Jul 2024
Viewed by 884
Abstract
This study focuses on mitigating the socio-economic and environmental damage of the invasive macroalga Rugulopteryx okamurae and counteracting the pollution from petroleum-based plastics by using the alga as a feedstock for polyhydroxybutyrate (PHB) production. The enzymatic hydrolysis of R. okamurae, non-pretreated and hydrothermally [...] Read more.
This study focuses on mitigating the socio-economic and environmental damage of the invasive macroalga Rugulopteryx okamurae and counteracting the pollution from petroleum-based plastics by using the alga as a feedstock for polyhydroxybutyrate (PHB) production. The enzymatic hydrolysis of R. okamurae, non-pretreated and hydrothermally acid-pretreated (0.2 N HCl, 15 min), was carried out, reaching reducing sugar (RS) concentrations of 10.7 g/L and 21.7 g/L, respectively. The hydrolysates obtained were used as a culture medium for PHB production with Cupriavidus necator, a Gram-negative soil bacterium, without supplementation with any external carbon and nitrogen sources. The highest yield (0.774 g PHB/g RS) and biopolymer accumulation percentage (89.8% cell dry weight, CDW) were achieved with hydrolysates from pretreated macroalga, reaching values comparable to the highest reported in the literature. Hence, it can be concluded that hydrolysates obtained from algal biomass hydrothermally pretreated with acid have a concentration of sugars and a C/N ratio that favour PHB production. Full article
(This article belongs to the Special Issue Marine Microorganisms and Their Biomolecules: Biodiversity, Physiological Adaptation and Biotechnological Applications)
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<p>(<b>a</b>) substrate (RS), (<b>b</b>) biomass (CDW) and (<b>c</b>) product (PHB) profiles of the fermentations with 1, 0.1, and 0 g/L of (NH<sub>4</sub>)<sub>2</sub>SO<sub>4</sub> (N1, N0.1, and N0, respectively) with 10 g/L glucose and an inoculum size of 1% <span class="html-italic">v</span>/<span class="html-italic">v</span>.</p> Full article ">Figure 2
<p>(<b>a</b>) substrate (RS), (<b>b</b>) biomass (CDW), and (<b>c</b>) product (PHB) profiles of the fermentations with an inoculum size of 1% <span class="html-italic">v</span>/<span class="html-italic">v</span>, 5%, and 10% with 10 g/L glucose and 1 g/L (NH<sub>4</sub>)<sub>2</sub>SO<sub>4</sub>.</p> Full article ">Figure 3
<p>(<b>a</b>) substrate (RS), (<b>b</b>) biomass (CDW), and (<b>c</b>) product (PHB) profiles of the fermentations with 10, 20, and 30 g/L of glucose (C10, C20, and C30, respectively) with 1 g/L (NH<sub>4</sub>)<sub>2</sub>SO<sub>4</sub> and an inoculum size of 5% <span class="html-italic">v</span>/<span class="html-italic">v</span>.</p> Full article ">Figure 4
<p>(<b>a</b>) substrate (RS), (<b>b</b>) biomass (CDW), and (<b>c</b>) product (PHB) profiles of the fermentations with hydrolysate media from non-pretreated macroalga (NPM) and from pretreated macroalga (PM) and with synthetic medium containing 20 g/L glucose and 1 g/L (NH<sub>4</sub>)<sub>2</sub>SO<sub>4</sub> (C20), with inoculum size of 5% <span class="html-italic">v</span>/<span class="html-italic">v</span>. The vertical dotted line in each graph indicates the time at which the fermentations in NPM and PM medium were extended.</p> Full article ">Figure 5
<p>Yields achieved in the fermentations carried out with the natural media (hydrolysate of non-pretreated algae, NPM, and pretreated algae, PM) and synthetic media (with 10 g/L, 20 g/L, and 30 g/L glucose, C10, C20, and C30, respectively, and 1 g/L (NH<sub>4</sub>)<sub>2</sub>SO<sub>4</sub>, N1), with inoculum size of 5% <span class="html-italic">v</span>/<span class="html-italic">v</span>. Average of the yields in each case (avg).</p> Full article ">
14 pages, 1889 KiB  
Article
Fishing Eco-Efficiency of Ports in Northwest Spain
by Luis T. Antelo and Amaya Franco-Uría
J. Mar. Sci. Eng. 2024, 12(7), 1227; https://doi.org/10.3390/jmse12071227 - 21 Jul 2024
Viewed by 769
Abstract
Fishing is an essential economic activity and source of livelihood for millions of people worldwide. However, overfishing and unsustainable practices have led to a decline in fish populations and the degradation of marine ecosystems. Moreover, fishing activities can contribute to climate change through [...] Read more.
Fishing is an essential economic activity and source of livelihood for millions of people worldwide. However, overfishing and unsustainable practices have led to a decline in fish populations and the degradation of marine ecosystems. Moreover, fishing activities can contribute to climate change through the emission of greenhouse gases (e.g., carbon dioxide and methane) from fishing vessels and seafood transportation. To mitigate the environmental impacts of fishing activities, sustainable fishing practices must be implemented to minimize the negative impacts of fishing on the environment while maintaining the productivity and diversity of fish populations and ecosystems. These practices include using selective fishing gear, avoiding fishing in vulnerable habitats, implementing fishery management plans, and reducing the carbon footprint of the fishing industry. To this end, and as a first step in defining efficient and effective measures towards the sustainability of capture fishing activity, an analysis of the environmental sustainability of the Galician fishing sector, one of the main European regions in this field, is presented in this work. An ecosystem-based indicator (ecological footprint, calculated by adding the so-called fishing ground footprint and the carbon footprint) was employed to quantify the main impacts of capture fishing during extractive activity. The catch composition and fuel consumption of the fleet based on the vessels’ power, and economic benefits, were the parameters used in this analysis. The results showed that ports with larger vessels and fleets seem to be more eco-efficient than those concentrating smaller vessels in targeting lower trophic level species. Full article
(This article belongs to the Section Marine Environmental Science)
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<p>Location of the different ports studied in this work: Burela (43°39′42″ N/7°21′24″ W); Celeiro (43°40′43″ N/7°35′40″ W); A Coruña (43°21′52″ N/8°23′38″ W); Ribeira (2°33′39″ N/8°59′24″ W); and Vigo (42°14′13″ N/8°43′59″ W).</p> Full article ">Figure 2
<p>Total ecological footprint (EF) of the analyzed ports by species (hake <span style="color:#002060">■</span>, black-bellied angler <span style="color:#0070C0">■</span>, megrim <span style="color:#00B0F0">■</span>, angler <span style="color:#00B050">■</span>, blue whiting <span style="color:#92D050">■</span>, Atlantic horse mackerel <span style="color:#FFC000">■</span>, Atlantic mackerel <span style="color:red">■</span>).</p> Full article ">Figure 3
<p>Carbon footprint (CF) of the analyzed ports by employed fishing gear.</p> Full article ">Figure 4
<p>Catch of main species as a function of their EF intensity (gha/t of fish) and economic benefit (millions of euros) in the analyzed ports: (<b>a</b>) Celeiro; (<b>b</b>) Burela; (<b>c</b>) Vigo; (<b>d</b>) A Coruña; (<b>e</b>) Ribeira. Graph (<b>f</b>) shows a comparison of the calculated <span class="html-italic">eco-efficiency</span> of the different ports.</p> Full article ">
18 pages, 6110 KiB  
Article
An Algorithm for Ship Detection in Complex Observation Scenarios Based on Mooring Buoys
by Wenbo Li, Chunlin Ning, Yue Fang, Guozheng Yuan, Peng Zhou and Chao Li
J. Mar. Sci. Eng. 2024, 12(7), 1226; https://doi.org/10.3390/jmse12071226 - 20 Jul 2024
Viewed by 767
Abstract
Marine anchor buoys, as fixed-point profile observation platforms, are highly susceptible to the threat of ship collisions. Installing cameras on buoys can effectively monitor and collect evidence from ships. However, when using a camera to capture images, it is often affected by the [...] Read more.
Marine anchor buoys, as fixed-point profile observation platforms, are highly susceptible to the threat of ship collisions. Installing cameras on buoys can effectively monitor and collect evidence from ships. However, when using a camera to capture images, it is often affected by the continuous shaking of buoys and rainy and foggy weather, resulting in problems such as blurred images and rain and fog occlusion. To address these problems, this paper proposes an improved YOLOv8 algorithm. Firstly, the polarized self-attention (PSA) mechanism is introduced to preserve the high-resolution features of the original deep convolutional neural network and solve the problem of image spatial resolution degradation caused by shaking. Secondly, by introducing the multi-head self-attention (MHSA) mechanism in the neck network, the interference of rain and fog background is weakened, and the feature fusion ability of the network is improved. Finally, in the head network, this model combines additional small object detection heads to improve the accuracy of small object detection. Additionally, to enhance the algorithm’s adaptability to camera detection scenarios, this paper simulates scenarios, including shaking blur, rain, and foggy conditions. In the end, numerous comparative experiments on a self-made dataset show that the algorithm proposed in this study achieved 94.2% mAP50 and 73.2% mAP50:95 in various complex environments, which is superior to other advanced object detection algorithms. Full article
(This article belongs to the Special Issue Modeling and Monitoring of Coastal Ocean, Nearshore and Estuarine Environments)
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<p>(<b>a</b>) HIKVISION camera; (<b>b</b>) capture bracket.</p> Full article ">Figure 2
<p>Example of image acquisition of various types of ships.</p> Full article ">Figure 3
<p>Preprocessing image simulation under different sea conditions. (<b>a</b>) Visual blur processing; (<b>b</b>) simulation of rain impacts; (<b>c</b>) center point synthetic fog treatment.</p> Full article ">Figure 4
<p>YOLOv8 network architecture diagram [<a href="#B19-jmse-12-01226" class="html-bibr">19</a>].</p> Full article ">Figure 5
<p>YOLOv8-PMH network architecture diagram.</p> Full article ">Figure 6
<p>PSA module [<a href="#B35-jmse-12-01226" class="html-bibr">35</a>].</p> Full article ">Figure 7
<p>C2f_PSA module.</p> Full article ">Figure 8
<p>Single-layer structure of MHSA module [<a href="#B40-jmse-12-01226" class="html-bibr">40</a>].</p> Full article ">Figure 9
<p>Comparison of detection results in different situations. (<b>a</b>) Fuzzy detection results; (<b>b</b>) rainy day detection results; (<b>c</b>) fog detection results.</p> Full article ">Figure 10
<p>PR Curve comparison chart. (<b>a</b>) YOLOv8; (<b>b</b>) YOLOv8-PMH.</p> Full article ">Figure 11
<p>Comparison of experimental results of YOLOv8-PMH with other algorithms. (<b>a</b>) Fuzzy detection results; (<b>b</b>) rainy day detection results; (<b>c</b>) fog detection results.</p> Full article ">
17 pages, 2694 KiB  
Article
A Ternary Diagram Approach to Investigate the Competition within the Bohai Sea Rim Multi-Port Group
by Qin Lin, Manel Grifoll, Peijun Zhuang and Hongxiang Feng
J. Mar. Sci. Eng. 2024, 12(7), 1225; https://doi.org/10.3390/jmse12071225 - 20 Jul 2024
Viewed by 657
Abstract
The Bohai Rim region constitutes the third prominent “growth pole” in China’s economic landscape, wherein the Bohai Rim multi-port system, encompassing Tianjin Port, Dalian Port, and Qingdao Port, engages in intense competition to establish itself as the foremost shipping hub in northern China. [...] Read more.
The Bohai Rim region constitutes the third prominent “growth pole” in China’s economic landscape, wherein the Bohai Rim multi-port system, encompassing Tianjin Port, Dalian Port, and Qingdao Port, engages in intense competition to establish itself as the foremost shipping hub in northern China. This study compares the ternary diagram method and employs the comprehensive concentration index (CCI), Lerner index (LI), and spatial shift-share analysis (SSSA) methods to delve into the intricacies of concentration, inequality, and evolving competitive dynamics within the Bohai Rim multi-port system over the four decades spanning from 1981 to 2023. The aim is to analyze the evolutionary trajectory and underlying dynamic mechanisms of this multipartite port system. The analysis delineates the development trajectory of the system into three stages: the dominant stage of Tianjin Port from 1981 to 1990, the efficiency competition stage from 1991 to 1996, and the ascendancy of Qingdao Port from 1997 to 2023. The results indicate that: (i) the Bohai Rim multi-port system exhibits a relatively low level of concentration, ensuring balanced growth within a non-monopolistic competitive environment; (ii) the internal competitiveness of the Bohai Rim multi-port system has gradually shifted from Tianjin Port to Qingdao Port, with Dalian Port experiencing steady development in its container transport capabilities. (iii) Dalian Port has witnessed a decline in container throughput since 2015, indicating a weakening competitive posture. These revelations suggest that Qingdao Port is a viable candidate for development into the northern China shipping center, leveraging its increasing competitiveness and strategic location. The method applied in this study may also prove beneficial for similar multi-port systems elsewhere. Full article
(This article belongs to the Special Issue 10th International Conference on Maritime Transport (MT’24))
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<p>Location of the Bohai Rim multi-port system and map of gateway ports in Northeast Asia.</p> Full article ">Figure 2
<p>Container throughput and traffic share of Dalian Port, Tianjin Port, and Qingdao Port from 1981 to 2023.</p> Full article ">Figure 3
<p>Framework and workflow for the method that compares the ternary diagram with three widely used indexes.</p> Full article ">Figure 4
<p>Definition of corners, sides, and the barycenter.</p> Full article ">Figure 5
<p><span class="html-italic">CCI</span> (<b>left</b>) and <span class="html-italic">MVC</span> (<b>right</b>) of the Bohai Rim multi-port system during 1981–2023.</p> Full article ">Figure 6
<p>The <span class="html-italic">LI</span>, <span class="html-italic">DPB</span>, and <span class="html-italic">NDPB</span> (<b>left</b>) and the value of the <span class="html-italic">DPB</span> and <span class="html-italic">NDPB</span> in the ternary diagram (<b>right</b>) of the Bohai Rim multi-port system, 1981–2023.</p> Full article ">Figure 7
<p><span class="html-italic">CTC</span> of the Bohai Rim multi-port system, 1981–2023.</p> Full article ">
27 pages, 4121 KiB  
Article
An Improved NSGA-II Algorithm for MASS Autonomous Collision Avoidance under COLREGs
by Zuopeng Liang, Fusheng Li and Shibo Zhou
J. Mar. Sci. Eng. 2024, 12(7), 1224; https://doi.org/10.3390/jmse12071224 - 20 Jul 2024
Viewed by 628
Abstract
Autonomous collision avoidance decision making for maritime autonomous surface ships (MASS), as one of the key technologies for MASS autonomous navigation, is a research hotspot focused on by relevant scholars in the field of navigation. In order to guarantee the rationality, efficacy, and [...] Read more.
Autonomous collision avoidance decision making for maritime autonomous surface ships (MASS), as one of the key technologies for MASS autonomous navigation, is a research hotspot focused on by relevant scholars in the field of navigation. In order to guarantee the rationality, efficacy, and credibility of the MASS autonomous collision avoidance scheme, it is essential to design the MASS autonomous collision avoidance algorithm under the stipulations of the Convention on the International Regulations for Preventing Collisions at Sea (COLREGs). In order to enhance the autonomous collision avoidance decision-making capability of MASS in accordance with the relevant provisions of COLREGs, an improved NSGA-II autonomous collision avoidance decision-making algorithm based on the good point set method (GPS-NSGA-II) is proposed, which incorporates the collision hazard and the path cost of collision avoidance actions. The experimental results in the four simulation scenarios of head-on situation, overtaking situation, crossing situation, and multi-ship encounter situation demonstrate that the MASS autonomous collision avoidance decision making based on the GPS-NSGA-II algorithm under the constraints of COLREGs is capable of providing an effective collision avoidance scheme that meets the requirements of COLREGs in common encounter situations and multi-ship avoidance scenarios promptly, with a promising future application. Full article
(This article belongs to the Special Issue Autonomous Marine Vehicle Operations—2nd Edition)
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<p>Schematic diagram of DCPA.</p> Full article ">Figure 2
<p>Collision hazards at different stages of navigation.</p> Full article ">Figure 3
<p>Ship encounter dynamics.</p> Full article ">Figure 4
<p>Collision avoidance obligations of MASS in head-on, overtaking, and crossing situations. (<b>a</b>) Head-on situation, (<b>b</b>) small-angle crossing situation, (<b>c</b>) large-angle crossing situations, and (<b>d</b>) overtaking situation.</p> Full article ">Figure 4 Cont.
<p>Collision avoidance obligations of MASS in head-on, overtaking, and crossing situations. (<b>a</b>) Head-on situation, (<b>b</b>) small-angle crossing situation, (<b>c</b>) large-angle crossing situations, and (<b>d</b>) overtaking situation.</p> Full article ">Figure 5
<p>Presentation of a schematic diagram of non-dominated sorting.</p> Full article ">Figure 6
<p>Initial population generated by the good point set and randomly. (<b>a</b>) Good points set, (<b>b</b>) good points set density, (<b>c</b>) randomly, and (<b>d</b>) randomly density.</p> Full article ">Figure 7
<p>GPS-NSGA-II algorithm flow.</p> Full article ">Figure 8
<p>A schematic representation of the parameters of the fitness function.</p> Full article ">Figure 9
<p>Flowchart of MASS autonomous collision avoidance process.</p> Full article ">Figure 10
<p>Key scenario diagrams of the collision avoidance process for a head-on situation. (<b>a</b>) The initial situation, (<b>b</b>) thresholds for action, (<b>c</b>) avoiding collision successfully, and (<b>d</b>) resume to original route.</p> Full article ">Figure 11
<p>Variation curves during the collision avoidance process for head−on situation.</p> Full article ">Figure 12
<p>Key scenario diagrams of the collision avoidance process for crossing situation. (<b>a</b>) The initial situation, (<b>b</b>) thresholds for action, (<b>c</b>) avoiding collision successfully, and (<b>d</b>) resume to original route.</p> Full article ">Figure 12 Cont.
<p>Key scenario diagrams of the collision avoidance process for crossing situation. (<b>a</b>) The initial situation, (<b>b</b>) thresholds for action, (<b>c</b>) avoiding collision successfully, and (<b>d</b>) resume to original route.</p> Full article ">Figure 13
<p>Variation curves during the collision avoidance process for the crossing situation.</p> Full article ">Figure 14
<p>Key scenario of collision avoidance process for overtaking situation. (<b>a</b>) The initial situation, (<b>b</b>) thresholds for action, (<b>c</b>) avoiding collision successfully, and (<b>d</b>) resume to original route.</p> Full article ">Figure 15
<p>Variation curves during the collision avoidance process for the overtaking situation.</p> Full article ">Figure 16
<p>Key scenario diagrams of the collision avoidance process for the muti-ship situation. (<b>a</b>) Initial situation, (<b>b</b>) successful avoidance of target_ship1, (<b>b</b>) successful avoidance of target_ship2, and (<b>d</b>) resume to original route.</p> Full article ">Figure 17
<p>Variation curves during the collision avoidance process for the muti-ship situation.</p> Full article ">
19 pages, 18332 KiB  
Article
Regional Difference in Distribution Pattern and Morphological Characteristics of Embayed Sandy Beaches in Zhejiang Province, Eastern China
by Junli Guo, Lianqiang Shi, Min Zhang, Zhaohui Gong, Wei Chen and Xiaoming Xia
J. Mar. Sci. Eng. 2024, 12(7), 1223; https://doi.org/10.3390/jmse12071223 - 20 Jul 2024
Viewed by 618
Abstract
The distribution pattern and the morphology of sandy beaches have been extensively studied, while those in turbid coastal environments near large river estuaries are still unclear. This study analyzes the distribution pattern, morphological characteristics, and influencing factors of Zhejiang sandy beaches using statistical [...] Read more.
The distribution pattern and the morphology of sandy beaches have been extensively studied, while those in turbid coastal environments near large river estuaries are still unclear. This study analyzes the distribution pattern, morphological characteristics, and influencing factors of Zhejiang sandy beaches using statistical analysis, based on field data and historical records. Results show that the mean grain size distribution of Zhejiang sandy beaches ranges from fine sand to very coarse sand, and the beach slope and sediment grain size correspond well with the wave heights in the three regions of Zhejiang. The extent of beach headlands in central Zhejiang appeared the largest, suggesting an increased susceptibility to wave erosion due to the less sheltered headlands. Most sandy beaches in Zhejiang formed on the islands and the areas far from the estuaries, showing quantity difference in beach distribution. The comparison of the regional difference in Zhejiang sandy beaches shows that embayment is the main factor affecting the beach distribution pattern and morphological characteristics. The different embayment characteristics provide the space for beach formation and the interaction with the coastal process, the sediment supply, the nearshore hydrodynamic environment, and human intervention also have influence on the morphological characteristics of Zhejiang beaches. Full article
(This article belongs to the Special Issue Advance in Sedimentology and Coastal and Marine Geology—2nd Edition)
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<p>Geographical location of study area and the coastal current system (<b>a</b>) and the locations of the 75 accessible embayed sandy beaches in Zhejiang province (<b>b</b>). YSWC, JCC, ZFCC, and TWC in (<b>a</b>) represent the Yellow Sea Warm Current, Jiangsu Coastal Current, Zhejiang–Fujian Coastal current, and Taiwan Warm Current, respectively. Numbers in this figure show the ID of the studied beaches (refer to <a href="#app1-jmse-12-01223" class="html-app">Table S1</a>; the ID numbers of the beaches are arranged from the north according to latitude). The coastal current system and the surficial sediment type distribution were modified after [<a href="#B36-jmse-12-01223" class="html-bibr">36</a>].</p> Full article ">Figure 2
<p>Embayment parameters used in this study according to [<a href="#B52-jmse-12-01223" class="html-bibr">52</a>].</p> Full article ">Figure 3
<p>Averaged slope of the beaches in NZ (<b>a</b>), CZ (<b>b</b>), and SZ (<b>c</b>), respectively.</p> Full article ">Figure 4
<p>Grain size characteristics of sandy beaches in Zhejiang from north to south: 33 beaches in NZ (<b>a</b>,<b>d</b>,<b>g</b>,<b>j</b>), 25 beaches in CZ (<b>b</b>,<b>e</b>,<b>h</b>,<b>k</b>), and 17 beaches in SZ (<b>c</b>,<b>f</b>,<b>i</b>,<b>l</b>).</p> Full article ">Figure 5
<p>Beach length and averaged dry beach width of sandy beaches in Zhejiang: 33 beaches in NZ (<b>a</b>,<b>d</b>), 25 beaches in NZ (<b>b</b>,<b>e</b>), and 17 beaches in SZ (<b>c</b>,<b>f</b>).</p> Full article ">Figure 6
<p>Correlation between dry beach width and beach length.</p> Full article ">Figure 7
<p>Headland lengths (<span class="html-italic">S</span>), bay mouth direction angles (<span class="html-italic">γ</span>), and spiral tangent angles (<span class="html-italic">β</span>) of sandy beaches in Zhejiang: 33 beaches in NZ (<b>a</b>,<b>d</b>,<b>g</b>), 25 beaches in CZ (<b>b</b>,<b>e</b>,<b>h</b>), and 17 beaches in SZ (<b>c</b>,<b>f</b>,<b>i</b>).</p> Full article ">Figure 8
<p>Maximum indentation (<span class="html-italic">a</span>), bay mouth arc length (<span class="html-italic">b</span>), ratio of <span class="html-italic">a</span> and <span class="html-italic">b</span>, and tangential section length (<span class="html-italic">L</span>) of sandy beaches in Zhejiang: 33 beaches in NZ (<b>a</b>,<b>d</b>,<b>g</b>,<b>j</b>), 25 beaches in CZ (<b>b</b>,<b>e</b>,<b>h</b>,<b>k</b>), and 17 beaches in SZ (<b>c</b>,<b>f</b>,<b>i</b>,<b>l</b>). The dash lines in each panel show the mean value.</p> Full article ">Figure 9
<p>Conceptual model of difference in the distribution (<b>a</b>–<b>c</b>) and morphological characteristics (<b>d</b>–<b>f</b>) of NZ beaches (<b>a</b>,<b>d</b>), CZ beaches (<b>b</b>,<b>e</b>), and SZ beaches (<b>c</b>,<b>f</b>). The black dots in (<b>d</b>–<b>f</b>) show the conceptual sediment grain size.</p> Full article ">Figure 10
<p>Multiyear averaged surficial suspended sediment concentration (mg/L) distribution in the coastal regions of Zhejiang and the adjacent sea areas during summer (<b>a</b>) and winter (<b>b</b>) (modified from [<a href="#B71-jmse-12-01223" class="html-bibr">71</a>]), in which the green circles show the sandy beaches in this study.</p> Full article ">Figure 11
<p>Example of human activities on the Zhejiang beaches: seawall construction (<b>a</b>) and beach nourishment (<b>b</b>).</p> Full article ">
19 pages, 12105 KiB  
Article
Underwater Mapping and Optimization Based on Multibeam Echo Sounders
by Feihu Zhang, Tingfeng Tan, Xujia Hou, Liang Zhao, Chun Cao and Zewen Wang
J. Mar. Sci. Eng. 2024, 12(7), 1222; https://doi.org/10.3390/jmse12071222 - 20 Jul 2024
Viewed by 589
Abstract
Multibeam echo sounders (MBESs) enable extensive underwater environment exploration. However, due to weak correlation between adjacent multibeam sonar data and difficulties in inter-frame feature matching, the resulting underwater mapping accuracy frequently falls short of the desired level. To address this issue, this study [...] Read more.
Multibeam echo sounders (MBESs) enable extensive underwater environment exploration. However, due to weak correlation between adjacent multibeam sonar data and difficulties in inter-frame feature matching, the resulting underwater mapping accuracy frequently falls short of the desired level. To address this issue, this study presents the development of a multibeam data processing system, which includes functionalities for sonar parameter configuration, data storage, and point cloud conversion. Subsequently, an Iterative Extended Kalman Filter (iEKF) algorithm is employed for odometry estimation, facilitating the initial construction of the point cloud map. To further enhance mapping accuracy, we utilize the Generalized Iterative Closest Point (GICP) algorithm for point cloud registration, effectively merging point cloud data collected at different times from the same location. Finally, real-world lake experiments demonstrate that our method achieves an Absolute Trajectory Error (ATE) of 15.10 m and an average local point cloud registration error of 0.97 m. Furthermore, we conduct measurements on various types of artificial targets. The experimental results indicate that the average location error of the targets calculated by our method is 4.62 m, which meets the accuracy requirements for underwater target exploration. Full article
(This article belongs to the Special Issue Applications of Underwater Acoustics in Ocean Engineering)
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<p>Data collection system. Red lines denote the types of interfaces utilized, while blue lines represent the data types and their respective directions of transmission.</p> Full article ">Figure 2
<p>Based on the physical model built according to the experimental vessel. (<b>a</b>) Mechanical structure model for the collection; (<b>b</b>) sonar head.</p> Full article ">Figure 3
<p>The relative positions of Inertial Navigation System (INS) and Multibeam echo sounder (MBES), and their respective reference coordinate systems.</p> Full article ">Figure 4
<p>Appropriately selected sonar parameters yield high-quality sonar data, characterized by a stable line with minimal clutter.</p> Full article ">Figure 5
<p>The ROS tf tree.</p> Full article ">Figure 6
<p>The single-ping point cloud recovered from sonar data, where color represents different echo intensities.</p> Full article ">Figure 7
<p>The process of constructing a MBES point cloud map. The red lines represent the measurements from a single ping. Time 1, Time 2, and Time 3 indicate the updates on the sonar’s position and attitude over the progression of time.</p> Full article ">Figure 8
<p>Generalized Iterative Closest Point (GICP) algorithm for point-to-plane registration. Blue represents the target surface, and yellow represents the source point cloud.</p> Full article ">Figure 9
<p>Environment and satellite map of experiments performed. (<b>a</b>) Aerial view; (<b>b</b>) satellite map.</p> Full article ">Figure 10
<p>Underwater target objects. (<b>a</b>) Human-shaped model; (<b>b</b>) cube and cylinder.</p> Full article ">Figure 11
<p>The scanning results displayed within the RVIZ interface. The left image presents the scan outcomes of a flat terrain, whereas the right image depicts a sloping terrain with obstructions caused by mountainous features. (<b>a</b>) Flat terrain; (<b>b</b>) sloping terrain.</p> Full article ">Figure 12
<p>The scanning results of the lakebed terrain.</p> Full article ">Figure 13
<p>The scanning results of underwater targets and shipwreck.</p> Full article ">Figure 14
<p>The result of registering two MBES point clouds. The first registration aligns the noisy and randomly transformed point cloud with the original point cloud using an estimated rigid transformation, aiming to eliminate noise and align the two point clouds. The second registration further improves the registration accuracy using the GICP algorithm, which is performed after the first registration. The arrow represents the continuous registration process, and the effect contrast of registration can be clearly seen at the red circle.</p> Full article ">Figure 15
<p>The result of the registration of a clearly defined underwater target with regular lines and edges. (<b>a</b>) Before registration; (<b>b</b>) after registration.</p> Full article ">Figure 16
<p>The algorithm proposed in this study (<b>left</b>), and the other produced by the HydroMaster (<b>right</b>). Both images are the result of scanning the same terrain at different times. The advantage of our method lies in its ability to export point cloud files with greater information content, preserving more detailed features. In contrast, the point cloud data exported by the HydroMaster software may lose some details due to the sampling process. The data can be accessed at <a href="http://www.hydroshare.org/resource/3592c1d35fdb4f29a1416e2c6099e13b" target="_blank">http://www.hydroshare.org/resource/3592c1d35fdb4f29a1416e2c6099e13b</a> (accessed on 6 July 2024). (<b>a</b>) Our method; (<b>b</b>) HydroMaster.</p> Full article ">Figure 17
<p>Estimated trajectory of the vessel.</p> Full article ">
20 pages, 2669 KiB  
Article
Maneuver Planning for Multiple Pursuit Intelligent Surface Vehicles in a Sequence of Zero-Sum Pursuit–Evasion Games
by Le Hong, Weicheng Cui, Hao Chen, Changhui Song and Weikun Li
J. Mar. Sci. Eng. 2024, 12(7), 1221; https://doi.org/10.3390/jmse12071221 - 20 Jul 2024
Viewed by 515
Abstract
Unmanned surface pursuit is a complex and challenging engineering problem, especially when conducted by multiple intelligent surface vehicles (ISVs). To enhance the pursuit performance and facilitate strategic interaction during the target pursuit, this paper proposes a novel game theory-based maneuver planning method for [...] Read more.
Unmanned surface pursuit is a complex and challenging engineering problem, especially when conducted by multiple intelligent surface vehicles (ISVs). To enhance the pursuit performance and facilitate strategic interaction during the target pursuit, this paper proposes a novel game theory-based maneuver planning method for pursuit ISVs. Firstly, a specific two-player zero-sum pursuit–evasion game (ZSPEG)-based target-pursuit model is formed. To ensure the vehicles reach a quick consensus, a target-guided relay-pursuit mechanism and the corresponding pursuit payoffs are designed. Meanwhile, under the fictitious play framework, the behavioral pattern and the strategies of the target could be fictitiously learned. Furthermore, mixed-strategy Nash equilibrium (MNE) is employed to determine the motions for the vehicles, the value of which is the best response in the proposed ZSPEG model. Finally, simulations verify the effectiveness of the above methods in multi-ISV surface pursuit. Full article
(This article belongs to the Section Ocean Engineering)
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<p>Schematic of the surface pursuit.</p> Full article ">Figure 2
<p>Top-view of the ISV motion.</p> Full article ">Figure 3
<p>The overall step-by-step process of the proposed algorithm.</p> Full article ">Figure 4
<p>Parameter assumption for the monomer pursuit.</p> Full article ">Figure 5
<p>Performance of the pursuit team with different N under (<b>a</b>,<b>b</b>) varying survival times and (<b>c</b>,<b>d</b>) varying pursuit velocities.</p> Full article ">Figure 6
<p>The enriched motions for the target.</p> Full article ">Figure 7
<p>Performance for the three strategy sets for the target under (<b>a</b>,<b>b</b>) varying survival times.</p> Full article ">
25 pages, 4075 KiB  
Article
A Robust Sparse Sensor Placement Strategy Based on Indicators of Noise for Ocean Monitoring
by Qiannan Zhang, Huafeng Wu, Li’nian Liang, Xiaojun Mei, Jiangfeng Xian and Yuanyuan Zhang
J. Mar. Sci. Eng. 2024, 12(7), 1220; https://doi.org/10.3390/jmse12071220 - 19 Jul 2024
Viewed by 486
Abstract
A well-performing data-driven sparse sensor deployment strategy is critical for marine monitoring systems, as it enables the optimal reconstruction of marine physical quantities with fewer sensors. However, ocean data typically contain substantial amounts of noise, including outliers (incomplete data) and inherent measurement noise, [...] Read more.
A well-performing data-driven sparse sensor deployment strategy is critical for marine monitoring systems, as it enables the optimal reconstruction of marine physical quantities with fewer sensors. However, ocean data typically contain substantial amounts of noise, including outliers (incomplete data) and inherent measurement noise, which heightens the complexity of sensor deployment. Therefore, this study optimizes the sparse sensor placement model by establishing noise indicators, including small noise weight and large noise weight, which are measured by entropy to minimize the prediction bias. Building on this, a robust sparse sensor placement algorithm is proposed, which utilizes the block coordinate update (BCU) iteration method to solve the function. During the iterative updating process, the proposed algorithm simultaneously updates the selection matrix, reconstruction matrix, and noise matrix. This allows for effective identification and mitigation of noise in the data through evaluation. Consequently, the deployed sensors achieve superior reconstruction performance compared to other deployment methods that do not incorporate noise evaluation. Experiments are also conducted on datasets of sea surface temperature (SST) and global ocean salinity, which demonstrate that our strategy significantly outperforms several other considered methods in terms of reconstruction accuracy while enabling autonomous sensor deployment under noisy conditions. Full article
(This article belongs to the Special Issue Advances in Underwater Acoustic Communication and Ocean Sensor Networks)
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<p>Reconstruction for low-rank data matrix of different parameters in RSSPIN. (<b>a</b>) Reconstruction error of different <span class="html-italic">α</span> and <span class="html-italic">β</span>; (<b>b</b>) Execution time of different <span class="html-italic">α</span> and <span class="html-italic">β</span>.</p> Full article ">Figure 2
<p>Convergence for different numbers of samples in RSSPIN, in which normalized data are used. (<b>a</b>) Convergence rate of total objective results without outliers in iteration; (<b>b</b>) convergence rate of reconstruction errors without outliers in iteration.</p> Full article ">Figure 3
<p>Convergence rate for different outlier ratios in RSSPIN. (<b>a</b>) Convergence rate of total objective results with different outlier ratios in iteration; (<b>b</b>) convergence rate of reconstruction errors with different outlier ratios in iteration.</p> Full article ">Figure 4
<p>Reconstruction error of different methods for SST. (<b>a</b>) Reconstruction errors of different outlier rates using Equation (36); (<b>b</b>) reconstruction errors of different samples using Equation (36); (<b>c</b>) reconstruction errors of different outlier rates using Equation (37); (<b>d</b>) reconstruction errors of different samples using Equation (37).</p> Full article ">Figure 5
<p>Reconstructed SST of different samples by RSSPIN (Ra = 0; Sr = 0). (<b>a</b>) Snapshot of test data; (<b>b</b>) reconstructed SST of 50 samples using RSSPIN; (<b>c</b>) reconstructed SST of 500 samples using RSSPIN.</p> Full article ">Figure 5 Cont.
<p>Reconstructed SST of different samples by RSSPIN (Ra = 0; Sr = 0). (<b>a</b>) Snapshot of test data; (<b>b</b>) reconstructed SST of 50 samples using RSSPIN; (<b>c</b>) reconstructed SST of 500 samples using RSSPIN.</p> Full article ">Figure 6
<p>Reconstruction error of different methods for global ocean salinity. (<b>a</b>) Reconstruction errors of different outlier rates; (<b>b</b>) reconstruction errors of different samples.</p> Full article ">Figure 7
<p>Reconstruction salinity field by RSSPIN. (<b>a</b>) Test salinity; (<b>b</b>) reconstruction salinity with Ra = 0.2; (<b>c</b>) reconstruction salinity with Ra = 0.4.</p> Full article ">Figure 7 Cont.
<p>Reconstruction salinity field by RSSPIN. (<b>a</b>) Test salinity; (<b>b</b>) reconstruction salinity with Ra = 0.2; (<b>c</b>) reconstruction salinity with Ra = 0.4.</p> Full article ">
19 pages, 8549 KiB  
Article
Short-Term Photovoltaic Power Prediction Based on a Digital Twin Model
by Chuan Xiang, Bohan Li, Pengfei Shi, Tiankai Yang and Bing Han
J. Mar. Sci. Eng. 2024, 12(7), 1219; https://doi.org/10.3390/jmse12071219 - 19 Jul 2024
Viewed by 515
Abstract
Due to the influence of meteorological conditions, shipboard photovoltaic (PV) systems have problems such as large fluctuation and inaccurate prediction of the output power. In this paper, a short-term PV power prediction method based on a novel digital twin (DT) model and BiLSTM [...] Read more.
Due to the influence of meteorological conditions, shipboard photovoltaic (PV) systems have problems such as large fluctuation and inaccurate prediction of the output power. In this paper, a short-term PV power prediction method based on a novel digital twin (DT) model and BiLSTM is proposed. Firstly, a PV mechanism model and a data-driven model were established, in which the data-driven model was updated iteratively in real time using the sliding time window update method; then, these two models were converged to construct a PV DT model according to the DS evidence theory. Secondly, a BiLSTM model was built to make short-term predictions of the PV power using the augmented dataset of the DT model as an input. Finally, the method was tested and verified by experiments and further compared with main PV prediction methods. The research results indicate the following: firstly, the absolute error of the DT model was smaller than that of the mechanism model and the data-driven model, being as low as 5.62 W after the data update of the data-driven model; thus, the DT model realized data augmentation and high fidelity. Secondly, compared to several main PV prediction models, the PV DT model combined with BiLSTM had the lowest RMSE, MAE, and MAPE; the best followability; and the smallest absolute error under different weather conditions, which was especially obvious under cloudy weather conditions. In summary, the method can accurately predict the shipboard PV power, which has great theoretical significance and application value for improving the economy and reliability of solar ship operation. Full article
(This article belongs to the Special Issue Sustainable Utilization of Marine Renewable Energy)
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<p>Block diagram of the research idea.</p> Full article ">Figure 2
<p>Equivalent circuit of a PV cell.</p> Full article ">Figure 3
<p>Structural diagram of CNN.</p> Full article ">Figure 4
<p>Sliding time window update method.</p> Full article ">Figure 5
<p>Structural diagram of LSTM.</p> Full article ">Figure 6
<p>Structural diagram of BiLSTM network.</p> Full article ">Figure 7
<p>Flowchart of short-term PV power prediction based on the DT model.</p> Full article ">Figure 8
<p>Experimental data collection platform.</p> Full article ">Figure 9
<p>Experimental data during October 2022.</p> Full article ">Figure 10
<p>Comparison of the output power of the DT model, mechanism model, and data-driven model with actual PV power before the data update. (<b>a</b>) Power. (<b>b</b>) Absolute error.</p> Full article ">Figure 11
<p>Comparison of the output power of the DT model, mechanism model, and data-driven model with actual PV power after the data update. (<b>a</b>) Power. (<b>b</b>) Absolute error.</p> Full article ">Figure 12
<p>RMSEs of different methods.</p> Full article ">Figure 13
<p>MAEs of different methods.</p> Full article ">Figure 14
<p>MAPEs of different methods.</p> Full article ">Figure 15
<p>Comparison of the predicted and actual PV power under sunny conditions. (<b>a</b>) Predicted output power. (<b>b</b>) Absolute error.</p> Full article ">Figure 16
<p>Comparison of the predicted and actual PV power under rainy conditions. (<b>a</b>) Predicted output power. (<b>b</b>) Absolute error.</p> Full article ">Figure 17
<p>Comparison of the predicted and actual PV power under cloudy conditions. (<b>a</b>) Predicted output power. (<b>b</b>) Absolute error.</p> Full article ">
16 pages, 2212 KiB  
Article
Simulation Model of Hydraulic System States for Ship Cranes
by Mate Jurjević
J. Mar. Sci. Eng. 2024, 12(7), 1218; https://doi.org/10.3390/jmse12071218 - 19 Jul 2024
Viewed by 446
Abstract
The aim of this research is to devise a continuous simulation model for predicting ship crane failures to increase their reliability and reduce unplanned downtime during cargo loading and unloading operations. To predict the condition of the hydraulic system, a database from the [...] Read more.
The aim of this research is to devise a continuous simulation model for predicting ship crane failures to increase their reliability and reduce unplanned downtime during cargo loading and unloading operations. To predict the condition of the hydraulic system, a database from the GALIOT software package was used for carrying out maintenance on cranes at m/v “O” over a period of 120,000 working hours. In the research, fault tree analysis (FTA) was used to identify causal relationships between system failures and basic events, while the Markov mathematical model was used to model the system state and predict transitions between different failure states. A system dynamics simulation model was developed to simulate the behavior of a system using POWERSIM PowerSim Constructor 2.5.d (4002), and regression analysis was performed to analyze the simulation results and understand the relationships between dependent and independent variables. The results show that a model for predicting failures in the hydraulic motors and pumps of ship cranes was developed, and the Markov model makes it possible to estimate the frequency of transitions between states under the condition that the sum of reliability equals one. The simulation model shows high reliability of the cranes and a constant frequency of failures throughout the 120,000 operating hours, while the regression analysis confirms the validity of the simulation model and shows a strong correlation between the analyzed variables. These models are used to improve the planning of ship crane maintenance, reduce unplanned downtime, and predict and promptly detect failures, which overall minimizes maintenance costs and failures. Full article
(This article belongs to the Section Ocean Engineering)
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<p>The sequence of methods and analyses used in the paper.</p> Full article ">Figure 2
<p>Fault tree for the ship crane.</p> Full article ">Figure 3
<p>Fault tree for the ship crane hydraulic motor.</p> Full article ">Figure 4
<p>Fault tree for the ship crane hydraulic pump.</p> Full article ">Figure 5
<p>The fault tree with total faults that put the crane out of order.</p> Full article ">Figure 6
<p>Markov model for ship cranes on <span class="html-italic">m</span>/<span class="html-italic">v</span> “O”.</p> Full article ">Figure 7
<p>(<b>a</b>) System dynamics qualitative structural model of ship crane reliability on <span class="html-italic">m</span>/<span class="html-italic">v</span> “O”; (<b>b</b>) structural system dynamics ship crane reliability model on <span class="html-italic">m</span>/<span class="html-italic">v</span> “O”.</p> Full article ">Figure 8
<p>(<b>a</b>) Calculated values using the program; (<b>b</b>) numerical values for reliability.</p> Full article ">Figure 9
<p>(<b>a</b>) Failure intensity functions over a time period of 120.000 working hours; (<b>b</b>) numerical values for failure intensity.</p> Full article ">Figure 10
<p>(<b>a</b>) The mean time in operation functions until reaching state 1, 2, 3, 4, and 5; (<b>b</b>) numerical values for mean time in operation.</p> Full article ">Figure 11
<p>Regression analysis of reliability and failure frequency on ship cranes on board the <span class="html-italic">m</span>/<span class="html-italic">v</span> “O”, obtained by simulation; (<b>a</b>) change of system reliability R_<span class="html-italic">n</span> (<span class="html-italic">n</span> = 1, 2, 3, 4, 5) as a function of time <span class="html-italic">t</span>; (<b>b</b>) change of transition frequency Lam_<span class="html-italic">n</span> (<span class="html-italic">n</span> = 1, 2, 3, 4, 5) as a function of time <span class="html-italic">t</span>; (<b>c</b>) mean time in operation for “n” states Tsr_<span class="html-italic">n</span> (<span class="html-italic">n</span> = 1, 2, 3, 4, 5) as a function of time <span class="html-italic">t</span>.</p> Full article ">Figure 12
<p>Correlation between reliability R_<span class="html-italic">n</span> and the mean time in operation Tsr_<span class="html-italic">n</span> (<span class="html-italic">n</span> = 1, 2, 3, 4, 5) shown on the <span class="html-italic">y</span>-axis in relation to the failure frequency shown on the <span class="html-italic">x</span>-axis.</p> Full article ">
12 pages, 2690 KiB  
Article
Underwater Image Enhancement Based on Light Field-Guided Rendering Network
by Chia-Hung Yeh, Yu-Wei Lai, Yu-Yang Lin, Mei-Juan Chen and Chua-Chin Wang
J. Mar. Sci. Eng. 2024, 12(7), 1217; https://doi.org/10.3390/jmse12071217 - 19 Jul 2024
Cited by 1 | Viewed by 607
Abstract
Underwater images often encounter challenges such as attenuation, color distortion, and noise caused by artificial lighting sources. These imperfections not only degrade image quality but also impose constraints on related application tasks. Improving underwater image quality is crucial for underwater activities. However, obtaining [...] Read more.
Underwater images often encounter challenges such as attenuation, color distortion, and noise caused by artificial lighting sources. These imperfections not only degrade image quality but also impose constraints on related application tasks. Improving underwater image quality is crucial for underwater activities. However, obtaining clear underwater images has been a challenge, because scattering and blur hinder the rendering of true underwater colors, affecting the accuracy of underwater exploration. Therefore, this paper proposes a new deep network model for single underwater image enhancement. More specifically, our framework includes a light field module (LFM) and sketch module, aiming at the generation of a light field map of the target image for improving the color representation and preserving the details of the original image by providing contour information. The restored underwater image is gradually enhanced, guided by the light field map. The experimental results show the better image restoration effectiveness, both quantitatively and qualitatively, of the proposed method with a lower (or comparable) computing cost, compared with the state-of-the-art approaches. Full article
(This article belongs to the Special Issue Earth System Modeling, Data Assimilation, Artificial Intelligence, Deep Learning and Ocean Information Engineering II)
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<p>Illustration of the proposed framework.</p> Full article ">Figure 2
<p>Illustration of the output from the light field module.</p> Full article ">Figure 3
<p>Illustration of the proposed rendering network.</p> Full article ">Figure 4
<p>Illustration of channel attention and spatial attention modules.</p> Full article ">Figure 5
<p>Qualitative evaluation results on the UFO-120 dataset.</p> Full article ">
18 pages, 5728 KiB  
Article
NUAM-Net: A Novel Underwater Image Enhancement Attention Mechanism Network
by Zhang Wen, Yikang Zhao, Feng Gao, Hao Su, Yuan Rao and Junyu Dong
J. Mar. Sci. Eng. 2024, 12(7), 1216; https://doi.org/10.3390/jmse12071216 - 19 Jul 2024
Viewed by 578
Abstract
Vision-based underwater exploration is crucial for marine research. However, the degradation of underwater images due to light attenuation and scattering poses a significant challenge. This results in the poor visual quality of underwater images and impedes the development of vision-based underwater exploration systems. [...] Read more.
Vision-based underwater exploration is crucial for marine research. However, the degradation of underwater images due to light attenuation and scattering poses a significant challenge. This results in the poor visual quality of underwater images and impedes the development of vision-based underwater exploration systems. Recent popular learning-based Underwater Image Enhancement (UIE) methods address this challenge by training enhancement networks with annotated image pairs, where the label image is manually selected from the reference images of existing UIE methods since the groundtruth of underwater images do not exist. Nevertheless, these methods encounter uncertainty issues stemming from ambiguous multiple-candidate references. Moreover, they often suffer from local perception and color perception limitations, which hinder the effective mitigation of wide-range underwater degradation. This paper proposes a novel NUAM-Net (Novel Underwater Image Enhancement Attention Mechanism Network) that addresses these limitations. NUAM-Net leverages a probabilistic training framework, measuring enhancement uncertainty to learn the UIE mapping from a set of ambiguous reference images. By extracting features from both the RGB and LAB color spaces, our method fully exploits the fine-grained color degradation clues of underwater images. Additionally, we enhance underwater feature extraction by incorporating a novel Adaptive Underwater Image Enhancement Module (AUEM) that incorporates both local and long-range receptive fields. Experimental results on the well-known UIEBD benchmark demonstrate that our method significantly outperforms popular UIE methods in terms of PSNR while maintaining a favorable Mean Opinion Score. The ablation study also validates the effectiveness of our proposed method. Full article
(This article belongs to the Section Ocean Engineering)
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<p>Illustration of uncertainty issue in UIE learning. We show examples of UIEBD datasets, i.e., the original image, (<b>a</b>) selected reference, (<b>b</b>) contrast adjustment result, (<b>c</b>) saturation adjustment result, and (<b>d</b>) gamma correction result. Multiple potential solutions can be ambiguous in reference selection since different people might choose different labels as the reference.</p> Full article ">Figure 2
<p>The network architecture of NUAM-Net. It consists of the feature extractor, PAdaIN, AUEM, and the output blocks. The extractor’s architecture is similar to the U-Net.</p> Full article ">Figure 3
<p>The overview of the AUEM. It consisted of a conv block and AIEM block. In the AIEM block, we try to combine and enhance the probabilistic feature. AIEM includes PConv, DWConv, LKA, SG, and IMAConv, which are five types of convolution blocks.</p> Full article ">Figure 4
<p>Structures of LKA, SG, and CA used in our AUEM module. (<b>a</b>) Large-Kernel Attention (LKA), (<b>b</b>) Simple Gate (SG), and (<b>c</b>) Channel Attention (CA).</p> Full article ">Figure 5
<p>Structures of IMAConv used in our AUEM module.</p> Full article ">Figure 6
<p>Examples of the extended UIEBD dataset, including 4 labels. Label-1 denotes the manually selected label in the original UIEBD dataset, label-2 is the contrast adjustment result, label-3 is the saturation adjustment result, and label-4 is the gamma correction result.</p> Full article ">Figure 7
<p>Qualitative results of the UIEBD test dataset. (<b>a</b>) DCP, (<b>b</b>) GC, (<b>c</b>) Retinex, (<b>d</b>) SESR, (<b>e</b>) Water-Net, (<b>f</b>) Ucolor, (<b>g</b>) PUIE-MC, (<b>h</b>) PUIE-MP, (<b>i</b>) Ours.</p> Full article ">Figure 8
<p>Enhancement examples of our ablation studies. We show the enhanced images of backbone, backbone+LAB, and backbone+LAB+AUEM on a subset of the UIEBD test data. It is evident from the image that our network demonstrates significant improvement in enhancement effectiveness.</p> Full article ">Figure 9
<p>Pictures show two extracted results of backbone and our network. (<b>a</b>) represents the feature extracted by our network and (<b>b</b>) represents the feature extracted by backbone network.</p> Full article ">
16 pages, 4252 KiB  
Article
Research on the Construction of a Digital Twin System for the Long-Term Service Monitoring of Port Terminals
by Jinqiang Bi, Peiren Wang, Wenjia Zhang, Kexin Bao and Liu Qin
J. Mar. Sci. Eng. 2024, 12(7), 1215; https://doi.org/10.3390/jmse12071215 - 19 Jul 2024
Cited by 1 | Viewed by 575
Abstract
Structural damage is a prevalent issue in long-term operations of harbor terminals. Addressing the lack of transparency in terminal infrastructure components, the limited integration of sensor monitoring data, and the insufficient support for feedback on service performance, we propose a novel digital twin [...] Read more.
Structural damage is a prevalent issue in long-term operations of harbor terminals. Addressing the lack of transparency in terminal infrastructure components, the limited integration of sensor monitoring data, and the insufficient support for feedback on service performance, we propose a novel digital twin system construction methodology tailored for the long-term monitoring of port terminals. This study elaborates on the organization and processing of foundational geospatial data, sensor monitoring information, and oceanic hydrometeorological data essential for constructing a digital twin of the terminal. By mapping relationships between physical and virtual spaces, we developed comprehensive dynamic and static models of terminal facilities. Employing a “particle model” approach, we visually represented oceanic and meteorological elements. Additionally, we developed a multi-source heterogeneous data fusion model to facilitate the rapid creation of data indexes for harbor elements under high concurrency conditions, effectively addressing performance issues related to scene-rendering visualization and real-time sensor data storage efficiency. Experimental validation demonstrates that this method enables the rapid construction of digital twin systems for port terminals and supports practical application in business scenarios. Data analysis and comparison confirm the feasibility of the proposed method, providing an effective approach for the long-term monitoring of port terminal operations. Full article
(This article belongs to the Special Issue Intelligent Ships and Waterways: Design, Operation and Advanced Technology)
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<p>Construction framework of digital twin system.</p> Full article ">Figure 2
<p>Terminal digital twin logic block diagram.</p> Full article ">Figure 3
<p>Scene construction of dynamic element model of wharf.</p> Full article ">Figure 4
<p>Bilinear interpolation diagram of flow field particles.</p> Full article ">Figure 5
<p>Optimal design of asynchronous data loading.</p> Full article ">Figure 6
<p>Optimization design of ship density clustering and pseudo code. (<b>a</b>) Optimization design process for ship density clustering. (<b>b</b>) Pseudo code of DENCLUE algorithm.</p> Full article ">Figure 7
<p>Construction of digital twin model. (<b>a</b>) Twin scene model of port area. (<b>b</b>) Refined dock model.</p> Full article ">Figure 8
<p>Digital twin system platform.</p> Full article ">Figure 9
<p>Application of digital twin platform. (<b>a</b>) Simulation of settlement of structure. (<b>b</b>) Simulation of stress of structure.</p> Full article ">
13 pages, 2070 KiB  
Article
A Parallelized Climatological Drifter-Based Model of Sargassum Biomass Dynamics in the Tropical Atlantic
by Karl Payne, Khalil Greene and Hazel A. Oxenford
J. Mar. Sci. Eng. 2024, 12(7), 1214; https://doi.org/10.3390/jmse12071214 - 19 Jul 2024
Viewed by 832
Abstract
The movement and biomass fluctuations of sargassum across the Tropical Atlantic have profound implications when influxes reach the Eastern Caribbean. These influxes have cross-cutting impacts across ecological, economic, and social systems. The objective of this work is to quantify sargassum biomass accumulation in [...] Read more.
The movement and biomass fluctuations of sargassum across the Tropical Atlantic have profound implications when influxes reach the Eastern Caribbean. These influxes have cross-cutting impacts across ecological, economic, and social systems. The objective of this work is to quantify sargassum biomass accumulation in the Eastern Caribbean, accounting for the spatial variability in sea surface temperature and morphotype diversity. A parallel implementation of a climatological drifter-based model was used to simulate advection of sargassum across the model domain. After determining the trajectory of virtual sargassum particles, Monte Carlo simulations using 1000 realizations were run to quantify biomass accumulations along these tracks. For simulations with a single morphotype, the biomass accumulation as predicted by the model effectively reproduced the seasonal distributions of sargassum for the simulated period (May 2017 to August 2017). The model closely approximated an observed increase during the period from May to July 2017, followed by a subsequent decline in sargassum abundance. A major factor that led to the discrepancy between the simulated and observed biomass accumulation is the occlusion of the optical satellite signal from cloud cover, which led to underestimates of sargassum abundance. The mean maximum growth rate required to reproduce the observed sargassum biomass was 0.05 day−1, which is consistent with other published experimental and computational studies that have reported similar growth rates for sargassum populations under comparable environmental conditions. An innovative aspect of this study was the investigation of the biomass dynamics of the three dominant morphotypes found in the study area. The results from these simulations show that the accumulation of the fastest growing morphotype, Sargassum fluitans var. fluitans, closely approximates the profiles of the overall prediction with a single morphotype. Full article
(This article belongs to the Section Marine Biology)
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<p>Map showing the Central Atlantic study area and the Eastern Caribbean prediction region (red outline).</p> Full article ">Figure 2
<p>Comparison of run time of serial and parallel codes for tracking Lagrangian particles with particles varying between 200 and 2000 (<b>A</b>) and speed-up achieved through parallelization of Lagrangian tracking algorithm (<b>B</b>).</p> Full article ">Figure 3
<p>Predicted (red line) and observed sargassum biomass (blue line) in the Eastern Caribbean for the period May–August 2017. The black line indicates the predicted biomass of 11.2 kt for 10 July 2017.</p> Full article ">Figure 4
<p>Comparison of detected cloud cover (black line) and sargassum biomass (blue line) from satellite imagery for the period May–August 2017.</p> Full article ">Figure 5
<p>Predicted (red line) and observed sargassum biomass (blue line) in the Eastern Caribbean for the period November 2021 to February 2022. The black line indicates the predicted biomass of 2.4 kt for 25 January 2022.</p> Full article ">Figure 6
<p>Comparison of detected cloud cover (black line) and sargassum biomass (blue line) from satellite imagery for the period November 2021–February 2022.</p> Full article ">Figure 7
<p>Comparison of biomass predictions for three morphotypes (<b>left</b>) and single-morphotype prediction and observation (<b>right</b>) for the period 1 May–1 August 2017 (<b>A</b>,<b>B</b>) and 3 November 2021–3 February 2022 (<b>C</b>,<b>D</b>). The black line indicates predictions for (<b>A</b>), 10 July 2017, 20.3 kt (<span class="html-italic">S. fluitans</span> var. <span class="html-italic">fluitans</span>—18.2 kt, <span class="html-italic">S. natans</span> var. <span class="html-italic">natans</span>—1.6 kt, and <span class="html-italic">S. natans</span> var. <span class="html-italic">wingei</span>—0.5 kt), compared to (<b>B</b>) 11.2 kt and (<b>C</b>) 25 January 2022, 3.9 kt (<span class="html-italic">S. fluitans</span> var. <span class="html-italic">fluitans</span>—3.6 kt, <span class="html-italic">S. natans</span> var. <span class="html-italic">natans</span>—0.2 kt, and <span class="html-italic">S. natans</span> var. <span class="html-italic">wingei</span>—0.1 kt) compared to (<b>D</b>) 2.4 kt.</p> Full article ">
8 pages, 1058 KiB  
Article
Size-Dependent Microplastic Fragmentation Model
by Vicente Pérez-Muñuzuri
J. Mar. Sci. Eng. 2024, 12(7), 1213; https://doi.org/10.3390/jmse12071213 - 19 Jul 2024
Viewed by 611
Abstract
Plastic fragmentation alters the size distribution of plastic waste in aquatic habitats, which is accelerated by mechanical stress and weathering degradation processes. Microplastic pieces constitute the vast bulk of plastic pollution in terms of quantity. Their size distribution has been shown to follow [...] Read more.
Plastic fragmentation alters the size distribution of plastic waste in aquatic habitats, which is accelerated by mechanical stress and weathering degradation processes. Microplastic pieces constitute the vast bulk of plastic pollution in terms of quantity. Their size distribution has been shown to follow a power-law for larger fragments. This work introduces a novel model inspired by raindrop formation, incorporating local oceanographic processes and fragment size, aiming to improve the understanding and prediction of plastic fragmentation in marine environments. Particles can fragment when they reach a certain size, or when shear forces become too strong. Plastic aging’s effect on size distribution is also investigated. Full article
(This article belongs to the Section Physical Oceanography)
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<p>Time evolution of particles in the model. Equal-sized particles entering the systems fragment if the shear force at the particles position exceeds a critical value (<a href="#FD2-jmse-12-01213" class="html-disp-formula">2</a>). Panels show a zoom of the model grid at constant intervals of time. Set of parameters: <math display="inline"><semantics> <mrow> <msub> <mi>V</mi> <mi>max</mi> </msub> <mo>=</mo> <mn>1.4</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>T</mi> <mo>=</mo> <mn>10</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>f</mi> <mrow> <mi>f</mi> <mi>r</mi> <mi>a</mi> <mi>g</mi> </mrow> </msub> <mo>=</mo> <mn>5</mn> <mo>·</mo> <msup> <mn>10</mn> <mrow> <mo>−</mo> <mn>4</mn> </mrow> </msup> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>f</mi> <mrow> <mi>i</mi> <mi>n</mi> <mi>p</mi> <mi>u</mi> <mi>t</mi> </mrow> </msub> <mo>=</mo> <msup> <mn>10</mn> <mrow> <mo>−</mo> <mn>2</mn> </mrow> </msup> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>N</mi> <mrow> <mi>p</mi> <mi>i</mi> <mi>e</mi> <mi>c</mi> <mi>e</mi> <mi>s</mi> </mrow> </msub> <mo>=</mo> <mn>2</mn> </mrow> </semantics></math>, and <math display="inline"><semantics> <mrow> <mi>γ</mi> <mo>=</mo> <mn>0.5</mn> </mrow> </semantics></math>.</p> Full article ">Figure 2
<p>Histogram of the particle size distribution for two values of <math display="inline"><semantics> <mi>γ</mi> </semantics></math>. Equal size particles are considered to enter periodically into the system. Dashed lines correspond to fittings to Equation (<a href="#FD1-jmse-12-01213" class="html-disp-formula">1</a>). Set of parameters as in <a href="#jmse-12-01213-f001" class="html-fig">Figure 1</a>.</p> Full article ">Figure 3
<p>Dependence of the power law slope <math display="inline"><semantics> <mi>α</mi> </semantics></math> and the mean radius <math display="inline"><semantics> <mrow> <mo>〈</mo> <mi>r</mi> <mo>〉</mo> </mrow> </semantics></math> as a function of the degradation <math display="inline"><semantics> <mi>γ</mi> </semantics></math> for two sets of primary particles entering the system; constant (blue solid line) and random (red dashed line) size. Error bars correspond to different noise realizations for the particles entering the system. Set of parameters as in <a href="#jmse-12-01213-f001" class="html-fig">Figure 1</a>.</p> Full article ">Figure 4
<p>Dependence of the power law slope <math display="inline"><semantics> <mi>α</mi> </semantics></math> and the mean radius <math display="inline"><semantics> <mrow> <mo>〈</mo> <mi>r</mi> <mo>〉</mo> </mrow> </semantics></math> as a function of <math display="inline"><semantics> <mrow> <msub> <mi>f</mi> <mrow> <mi>i</mi> <mi>n</mi> <mi>p</mi> <mi>u</mi> <mi>t</mi> </mrow> </msub> <mo>/</mo> <msub> <mi>f</mi> <mrow> <mi>f</mi> <mi>r</mi> <mi>a</mi> <mi>g</mi> </mrow> </msub> </mrow> </semantics></math>. Error bars correspond to different noise realizations for the particles entering the system. Set of parameters as in <a href="#jmse-12-01213-f001" class="html-fig">Figure 1</a>.</p> Full article ">
18 pages, 4391 KiB  
Article
Seasonal Variability of Hydrological Parameters and Estimation of Circulation Patterns: Application to a Mediterranean Coastal Lagoon
by Nikolaos Simantiris and Alexander Theocharis
J. Mar. Sci. Eng. 2024, 12(7), 1212; https://doi.org/10.3390/jmse12071212 - 18 Jul 2024
Viewed by 512
Abstract
Coastal lagoons are among the most important, but also threatened, marine systems of our planet. Rainfall, wind, seawater, and freshwater discharges control water circulation in lagoons, determining the water properties that are vital for the lagoon’s biodiversity. The present work is the first [...] Read more.
Coastal lagoons are among the most important, but also threatened, marine systems of our planet. Rainfall, wind, seawater, and freshwater discharges control water circulation in lagoons, determining the water properties that are vital for the lagoon’s biodiversity. The present work is the first study on the circulation patterns and seasonal variability of hydrological parameters in Antinioti lagoon in western Greece, building a reference level on our knowledge of the hydrodynamic functioning of this marine ecosystem. This study shows that the lagoon’s water properties’ fluctuations and circulation variability are affected by an antagonistic effect between freshwater (river discharge, underground spring, rainfall) and seawater inputs. This effect, influenced by atmospheric forcing (rainfall, atmospheric temperature), controls the heat and salt budgets of the lagoon. Nevertheless, the lagoon keeps an almost balanced annual cycle, returning from June 2020 to June 2021 to similar values for all parameters. Full article
(This article belongs to the Section Physical Oceanography)
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<p>The location of Corfu Island on the map of the Mediterranean region is shown in a red rectangle at the bottom left corner, and the location of Antinioti lagoon on the map of Corfu Island is shown in a red rectangle at the top right corner. The coastline of the lagoon is marked with white color. Images from <span class="html-italic">Google Earth version: 7.3.6.9796 (64-bit)</span>, 2017 (accessed on 12 August 2020).</p> Full article ">Figure 2
<p>Antinioti lagoon’s bathymetry.</p> Full article ">Figure 3
<p>Daily air temperature (<b>a</b>), rainfall (<b>b</b>), and wind data (<b>c</b>) from a national weather station located near Antinioti lagoon. The wind data are compared with wind measurements that took place in Antinioti lagoon. In (<b>c</b>), the sampling dates that match the in situ data are shown with red color.</p> Full article ">Figure 4
<p>Multiparameter monthly profiles in the 6 selected stations of Antinioti lagoon. Summer months are marked with red, autumn months are marked with black, winter months are marked with green, and spring months are marked with blue.</p> Full article ">Figure 5
<p>Spatial interpolation of parameters in the surface and bottom of Antinioti lagoon on November 2020.</p> Full article ">Figure 6
<p>Spatial interpolation of parameters in the surface and bottom of Antinioti lagoon in June 2021.</p> Full article ">Figure 7
<p>Monthly surface (t) and bottom (b) values of physical parameters in 6 stations of Antinioti lagoon. The bottom values are colored blue and the surface values are colored red.</p> Full article ">Figure 8
<p>Heat and salt content of Antinioti lagoon per month.</p> Full article ">Figure 9
<p>Percentage of seawater forming the lagoon’s surface and bottom water properties in October 2020.</p> Full article ">Figure 10
<p>Percentage of seawater forming the lagoon’s surface and bottom water properties in April 2021.</p> Full article ">
24 pages, 9893 KiB  
Article
Diatoms of the Macroalgae Epiphyton and Bioindication of the Protected Coastal Waters of the Kazantip Cape (Crimea, the Sea of Azov)
by Anna Bondarenko, Armine Shiroyan, Larisa Ryabushko and Sophia Barinova
J. Mar. Sci. Eng. 2024, 12(7), 1211; https://doi.org/10.3390/jmse12071211 - 18 Jul 2024
Viewed by 577
Abstract
This article is about the diversity of diatoms in the benthos of the upper sublittoral near Kazantip Cape, located on the shore of the Sea of Azov in the northeastern part of Crimea. The study was conducted in 2022 and 2023 at a [...] Read more.
This article is about the diversity of diatoms in the benthos of the upper sublittoral near Kazantip Cape, located on the shore of the Sea of Azov in the northeastern part of Crimea. The study was conducted in 2022 and 2023 at a depth of 0.1 to 1 m at temperatures from 3.7 °C to 29 °C and salinity from 13.6 to 15.6 psu on the following 11 species of macroalgae: Phaeophyta of Ericaria crinita, Gongolaria barbata, and Cladosiphon mediterraneus; Chlorophyta—Bryopsis hypnoides, Cladophora liniformis, Ulva intestinalis, and Ulva linza; and Rhodophyta—Callithamnion corymbosum, Ceramium arborescens, Polysiphonia denudata, and Pyropia leucosticta. A total of 97 taxa of Bacillariophyta belonging to 3 classes, 21 orders, 30 families, and 45 genera were found. The highest number of diatom species was found on U. linza (61 species), P. denudata (45), E. crinita (40), the lowest number was recorded on thalli P. leucosticta (9). On macroalgae were found of 80% benthic diatoms, 50% marine species, 36% brackish-marine, 9% freshwater, 5% brackish, and 36% cosmopolites. The maximum abundance of the diatom community was 243.4 × 103 cells/cm2 (P. denudata in September at 23.9 °C and 15.0 psu) with dominance by the diatom of Licmophora abbreviata, and the minimum was 3.8 × 103 cells/cm2 (P. leucosticta in January at 3.7 °C and 15.0 psu). The presence in the epiphyton of diatoms—indicators of moderate organic water pollution (32 species), which developed in masse in late summer—indicate a constant inflow of organic matter into the coastal waters of the Kazantip Cape. The bioindicator and statistical studies indicate the effectiveness of the conservation regime, especially at stations within the IUCN reserve, despite relatively high saprobity rates at stations exposed to recreational pressure and poorly treated domestic wastewater. Full article
(This article belongs to the Special Issue Challenges in Monitoring and Management of Water Quality in Coastal Areas)
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Figure 1
<p>Map of the sampling sites of the Kazantip Cape in bays 1—Russkaya, 2—Shirokaya, 3—Kunushkay, and 4—Tatarskaya.</p> Full article ">Figure 2
<p>Various views of the Kazantip Cape and its bays: (<b>a</b>,<b>d</b>,<b>e</b>)—rocky cliffs; (<b>b</b>)—sandy coast of Russkaya Bay; (<b>c</b>)—Kunushkay Bay; (<b>d</b>)—Shirokaya Bay; (<b>e</b>)—coastal ice cover.</p> Full article ">Figure 3
<p>Samples of the macroalgae of the coastal waters of the Kazantip Cape: (<b>a</b>)—<span class="html-italic">Bryopsis hypnoides</span>, (<b>b</b>)—<span class="html-italic">Callithamnion corymbosum</span>, (<b>c</b>)—<span class="html-italic">Cladosiphon mediterraneus</span>, (<b>d</b>)—<span class="html-italic">Ulva intestinalis</span>, (<b>e</b>)—<span class="html-italic">Polysiphonia denudata</span>, (<b>f</b>)—<span class="html-italic">Pyropia leucosticta</span>, (<b>g</b>)—<span class="html-italic">Ulva linza</span>, (<b>h</b>)—<span class="html-italic">Ericaria crinita</span>, (<b>i</b>)—<span class="html-italic">Cladophora liniformis</span>, (<b>j</b>)—<span class="html-italic">Gongolaria barbata</span>, and (<b>k</b>)—<span class="html-italic">Ceramium arborescens</span>.</p> Full article ">Figure 4
<p>LM. Colonies of diatoms of various forms in the fouling of macroalgae in the Kazantip Cape: <span class="html-italic">Achnanthes brevipes</span> (<b>a</b>,<b>e</b>,<b>o</b>), <span class="html-italic">Grammatophora marina</span> (<b>b</b>,<b>k</b>), <span class="html-italic">Rhoicosphenia marina</span> (<b>c</b>), <span class="html-italic">Melosira jurgensii</span> (<b>d</b>), <span class="html-italic">Tabularia tabulata</span> (<b>f</b>,<b>j</b>,<b>n</b>), single cell of <span class="html-italic">Navicula ramosissima</span> (<b>g</b>) and its tube colonies (<b>h</b>), <span class="html-italic">Melosira moniliformis</span> (<b>i</b>), and <span class="html-italic">Melosira lineata</span> (<b>m</b>). A single living species of <span class="html-italic">Cocconeis scutellum</span> is inside of the red alga <span class="html-italic">Ceramium arborescens</span> (<b>l</b>).</p> Full article ">Figure 5
<p>LM. Photographs of some diatom species frustules (<b>a</b>–<b>e</b>,<b>g</b>,<b>i</b>,<b>p</b>,<b>q</b>) and cells with chloroplasts (<b>f</b>–<b>h</b>,<b>j</b>–<b>o</b>) on macroalgae epiphyton: <span class="html-italic">Cocconeis placentula</span> var. <span class="html-italic">euglypta</span> (<b>a</b>), <span class="html-italic">Cocconeis scutellum</span> (<b>b</b>), <span class="html-italic">Mastogloia pumila</span> (<b>c</b>), <span class="html-italic">Navicula cancellata</span> (<b>d</b>), <span class="html-italic">Navicula palpebralis</span> (<b>e</b>), <span class="html-italic">Diploneis didyma</span> (<b>f</b>), <span class="html-italic">Caloneis liber</span> (<b>g</b>), <span class="html-italic">Nitzschia sigmoidea</span> (<b>h</b>), <span class="html-italic">Nitzschia lanceolata</span> var. <span class="html-italic">minor</span> (<b>i</b>), <span class="html-italic">Licmophora abbreviata</span> (<b>j</b>), <span class="html-italic">Achnanthes brevipes</span> (<b>k</b>), <span class="html-italic">Petroneis humerosa</span> (<b>l</b>), <span class="html-italic">Undatella lineolata</span> (<b>m</b>), <span class="html-italic">Gomphonemopsis pseudexigua</span> (<b>n</b>), <span class="html-italic">Achnanthes longipes</span> (<b>o</b>), <span class="html-italic">Synedrosphenia crystallina</span> (<b>p</b>), and <span class="html-italic">Tabularia tabulata</span> (<b>q</b>). Scale bar: 10 µm.</p> Full article ">Figure 6
<p>SEM. Some species of diatoms found in the macroalgae epiphyton of the Kazantip Cape coastal waters are as follows: <span class="html-italic">Cocconeis scutellum</span> (<b>a</b>), <span class="html-italic">Mastogloia pumila</span> (<b>b</b>,<b>c</b>), <span class="html-italic">Navicula dumontiae</span> (<b>d</b>), <span class="html-italic">Rhopalodia musculus</span> (<b>e</b>), <span class="html-italic">Navicula perminuta</span> (<b>f</b>), <span class="html-italic">Nitzschia inconspicua</span> (<b>g</b>), <span class="html-italic">Planothidium delicatulum</span> (<b>h</b>), <span class="html-italic">Fallacia forcipata</span> (<b>i</b>), <span class="html-italic">Achnanthes brevipes</span> (<b>j</b>), <span class="html-italic">Cocconeis placentula</span> var. <span class="html-italic">euglypta</span> (<b>k</b>), <span class="html-italic">Haslea subagnita</span> (<b>l</b>), <span class="html-italic">Tabularia parva</span> (<b>m</b>), <span class="html-italic">Rhoicosphenia marina</span> (<b>n</b>), <span class="html-italic">Nitzschia hybrida</span> f. <span class="html-italic">hyalina</span> (<b>o</b>), <span class="html-italic">Lyrella atlantica</span> (<b>p</b>), <span class="html-italic">Licmophora abbreviata</span> (<b>q</b>,<b>r</b>), <span class="html-italic">Melosira moniliformis</span> (<b>s</b>), <span class="html-italic">Achnanthes longipes</span> (<b>t</b>), and <span class="html-italic">Tabularia tabulata</span> (<b>u</b>). Scale bar: (<b>a</b>–<b>e</b>) = 5 µm; (<b>f</b>,<b>g</b>) = 3 µm; (<b>h</b>) = 4 µm; (<b>i</b>–<b>n</b>) = 10 µm; (<b>o</b>–<b>q</b>) = 20 µm; (<b>r</b>,<b>s</b>) = 30 µm; and (<b>t</b>,<b>u</b>) = 40 µm.</p> Full article ">Figure 7
<p>JASP plot of the full diatom community similarity in submerged macroalgae (<b>a</b>) and in four studied sites in the coastal waters of the Kazantip Cape of the Sea of Azov (<b>b</b>). Macrophyte names were coded as in <a href="#app1-jmse-12-01211" class="html-app">Appendix A</a> <a href="#jmse-12-01211-t0A1" class="html-table">Table A1</a>. The line thickness reflects the similarity coefficient value. The red lines are negative, and blue lines are positive correlations. Different clusters are numbered 1–3.</p> Full article ">Figure 8
<p>Dendrogram of the similarity of the diatom species composition in the epiphyton by seasons.</p> Full article ">
0 pages, 22988 KiB  
Article
MEvo-GAN: A Multi-Scale Evolutionary Generative Adversarial Network for Underwater Image Enhancement
by Feiran Fu, Peng Liu, Zhen Shao, Jing Xu and Ming Fang
J. Mar. Sci. Eng. 2024, 12(7), 1210; https://doi.org/10.3390/jmse12071210 - 18 Jul 2024
Viewed by 566
Abstract
In underwater imaging, achieving high-quality imagery is essential but challenging due to factors such as wavelength-dependent absorption and complex lighting dynamics. This paper introduces MEvo-GAN, a novel methodology designed to address these challenges by combining generative adversarial networks with genetic algorithms. The key [...] Read more.
In underwater imaging, achieving high-quality imagery is essential but challenging due to factors such as wavelength-dependent absorption and complex lighting dynamics. This paper introduces MEvo-GAN, a novel methodology designed to address these challenges by combining generative adversarial networks with genetic algorithms. The key innovation lies in the integration of genetic algorithm principles with multi-scale generator and discriminator structures in Generative Adversarial Networks (GANs). This approach enhances image details and structural integrity while significantly improving training stability. This combination enables more effective exploration and optimization of the solution space, leading to reduced oscillation, mitigated mode collapse, and smoother convergence to high-quality generative outcomes. By analyzing various public datasets in a quantitative and qualitative manner, the results confirm the effectiveness of MEvo-GAN in improving the clarity, color fidelity, and detail accuracy of underwater images. The results of the experiments on the UIEB dataset are remarkable, with MEvo-GAN attaining a Peak Signal-to-Noise Ratio (PSNR) of 21.2758, Structural Similarity Index (SSIM) of 0.8662, and Underwater Color Image Quality Evaluation (UCIQE) of 0.6597. Full article
(This article belongs to the Special Issue Application of Deep Learning in Underwater Image Processing)
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<p>General MEvo-GAN architecture.</p> Full article ">Figure 2
<p>Detailed generator and discriminator architecture.</p> Full article ">Figure 3
<p>General genetic algorithm architecture.</p> Full article ">Figure 4
<p>Results of the 9-method color-card recovery experiment (CFE indicator in the upper left of the image).</p> Full article ">Figure 5
<p>Visual comparison of enhancements of images from the EUVP, UIEB, and UFO-120 datasets.</p> Full article ">Figure 6
<p>Visualization of feature maps.</p> Full article ">Figure 7
<p>Example of gradient explosion phenomenon during training without integration of evolutionary mechanism. (<b>a</b>) Original image; (<b>b</b>) training image without incorporation of evolutionary mechanism; (<b>c</b>) MEvo-GAN.</p> Full article ">Figure 8
<p>Enhancement effect feature point matching for different ablation models and the full MEvo-GAN model. (<b>a</b>) −w/o-VGG loss; (<b>b</b>) −w/o-multiscale network; (<b>c</b>) MEvo-GAN.</p> Full article ">Figure 9
<p>Visual comparison of the enhancement effects of different ablation models and the full MEvo-GAN model. (<b>a</b>) Original image; (<b>b</b>) −w/o-VGG loss; (<b>c</b>) −w/o-multiscale network; (<b>d</b>) MEvo-GAN.</p> Full article ">
22 pages, 10384 KiB  
Article
Numerical Research on a T-Foil Control Method for Trimarans Based on Phase Lag
by Yifang Sun, Yiqun Wang, Dapeng Zhang, Zongduo Wu and Guoqing Jin
J. Mar. Sci. Eng. 2024, 12(7), 1209; https://doi.org/10.3390/jmse12071209 - 18 Jul 2024
Viewed by 516
Abstract
The lift force of a T-foil, which varies with ship motion, can counteract the wave exciting force during wave encounters. The phase difference between the periodic lift force and the wave exciting force significantly impacts the T-foil’s effectiveness. This study investigates the phase [...] Read more.
The lift force of a T-foil, which varies with ship motion, can counteract the wave exciting force during wave encounters. The phase difference between the periodic lift force and the wave exciting force significantly impacts the T-foil’s effectiveness. This study investigates the phase difference between lift force and motion to optimize the control equation for the T-foil’s angle, thereby reducing negative feedback. The T-foil’s hydrodynamic performance is first calculated using computational fluid dynamics. Time-domain calculations of the phase lag between lift force and motion under open-loop control in still water are then used to determine the dimensionless phase lag of the T-foil’s angle at various frequencies, facilitating further optimization of the control method. Finally, calculations of trimaran heave and pitch in regular waves are conducted. The results demonstrate that, under phase lag control, the T-foil’s lift force phase precedes ship motion by approximately 0.2 s, reducing hysteresis in the anti-vertical motion effect. Comparisons of vertical hull motions between different control methods reveal a 20% reduction in vertical motion with phase lag control compared to pitch control. This study concludes that phase lag is a crucial factor in T-foil control optimization. Full article
(This article belongs to the Special Issue Advances in the Performance of Ships and Offshore Structures)
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<p>Coordinate systems.</p> Full article ">Figure 2
<p>Functional block diagram for the derivation of motion equation.</p> Full article ">Figure 3
<p>Meshing of the T-foil model.</p> Full article ">Figure 4
<p>Lift coefficient dependence on the attack angle.</p> Full article ">Figure 5
<p>Boundary condition setting.</p> Full article ">Figure 6
<p>Surface mesh of the trimaran model.</p> Full article ">Figure 7
<p>Convergence verification of calculation. (<b>a</b>) Time-record curves of heave with different time steps (<span class="html-italic">λ</span> = 10 m, <span class="html-italic">U</span> = 2.115 m/s). (<b>b</b>) Time-record curves of pitch with different time steps (<span class="html-italic">λ</span> = 10 m, <span class="html-italic">U</span> = 2.115 m/s). (<b>c</b>) Time-record curves of heave with different grid quantities (<span class="html-italic">λ</span> = 10 m, <span class="html-italic">U</span> = 2.115 m/s). (<b>d</b>) Time-record curves of pitch with different grid quantities (<span class="html-italic">λ</span> = 10 m, <span class="html-italic">U</span> = 2.115 m/s).</p> Full article ">Figure 8
<p>Dimensionless heave for trimaran with T-foil.</p> Full article ">Figure 9
<p>Pitch angle for trimaran with T-foil.</p> Full article ">Figure 10
<p>Time-record curves of lift force (<span class="html-italic">Fr</span> = 0.3).</p> Full article ">Figure 11
<p>Time-record curves of lift force (<span class="html-italic">Fr</span> = 0.5).</p> Full article ">Figure 12
<p>Time-record curves of swing angle and motions. (<b>a</b>) Heave amplitude and swing angle (<span class="html-italic">Fr</span> = 0.3). (<b>b</b>) Pitch angle and swing angle (<span class="html-italic">Fr</span> = 0.3). (<b>c</b>) Heave amplitude and swing angle (<span class="html-italic">Fr</span> = 0.5). (<b>d</b>) Pitch angle and swing angle (<span class="html-italic">Fr</span> = 0.5).</p> Full article ">Figure 13
<p>Dimensionless phase lag (<span class="html-italic">Fr</span> = 0.3).</p> Full article ">Figure 14
<p>Dimensionless phase lag (<span class="html-italic">Fr</span> = 0.5).</p> Full article ">Figure 15
<p>Flow chart of trimaran’s vertical motion control system.</p> Full article ">Figure 16
<p>Wave-force module.</p> Full article ">Figure 17
<p>Force–motion module.</p> Full article ">Figure 18
<p>Swinging angle control module.</p> Full article ">Figure 19
<p>Lift force calculation module.</p> Full article ">Figure 20
<p>Time-record curves of different speeds (λ = 6 m). (<b>a</b>) Time-record curves of swing angle φ (<span class="html-italic">Fr</span> = 0.3). (<b>b</b>) Time-record curves of swing angle φ (<span class="html-italic">Fr</span> = 0.5). (<b>c</b>) Time-record curves of heave (<span class="html-italic">Fr</span> = 0.3). (<b>d</b>) Time-record curves of heave (<span class="html-italic">Fr</span> = 0.5). (<b>e</b>) Time-record curves of pitch (<span class="html-italic">Fr</span> = 0.3). (<b>f</b>) Time-record curves of pitch (<span class="html-italic">Fr</span> = 0.5).</p> Full article ">Figure 20 Cont.
<p>Time-record curves of different speeds (λ = 6 m). (<b>a</b>) Time-record curves of swing angle φ (<span class="html-italic">Fr</span> = 0.3). (<b>b</b>) Time-record curves of swing angle φ (<span class="html-italic">Fr</span> = 0.5). (<b>c</b>) Time-record curves of heave (<span class="html-italic">Fr</span> = 0.3). (<b>d</b>) Time-record curves of heave (<span class="html-italic">Fr</span> = 0.5). (<b>e</b>) Time-record curves of pitch (<span class="html-italic">Fr</span> = 0.3). (<b>f</b>) Time-record curves of pitch (<span class="html-italic">Fr</span> = 0.5).</p> Full article ">Figure 21
<p>Heave amplitude in different control methods (<span class="html-italic">Fr</span> = 0.3).</p> Full article ">Figure 22
<p>Heave amplitude in different control methods (<span class="html-italic">Fr</span> = 0.5).</p> Full article ">Figure 23
<p>Pitch amplitude in different control methods (<span class="html-italic">Fr</span> = 0.3).</p> Full article ">Figure 24
<p>Pitch amplitude in different control methods (<span class="html-italic">Fr</span> = 0.5).</p> Full article ">
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