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Volume 12
Issue 9
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J. Mar. Sci. Eng., Volume 12, Issue 9 (September 2024) – 228 articles

Cover Story (view full-size image): Fatigue damage represents a key failure mode in ship structures, with such damage typically beginning at vulnerable points in the structure. Cyclic loading, particularly from waves, encountered by ships during their operational life is a major cause of fatigue damage, which is the main focus of this study. This paper aims to review the most commonly used methods to highlight their strengths and weaknesses and to provide essential background knowledge for developing reliable theoretical and numerical models for predicting the fatigue life of ship structures exposed to various sea states over their lifetime. The discussion also covers the determination of cyclic stress in specific structural details of the hull girder and welded joints to identify the relevant maximum stress range for subsequent fatigue studies conducted using finite element analysis. View this paper
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34 pages, 19538 KiB  
Article
Coupled Motion Response Analysis for Dynamic Target Salvage under Wave Action
by Gang Sun, Shengtao Chen, Hongkun Zhou and Fei Wan
J. Mar. Sci. Eng. 2024, 12(9), 1688; https://doi.org/10.3390/jmse12091688 - 23 Sep 2024
Viewed by 618
Abstract
The strategic recovery of buoys is a critical task in executing deep-sea research missions, as nations extend their exploration of marine territories. This study primarily investigates the dynamics of remotely operated vehicle (ROV)-assisted salvage operations for floating bodies during the recovery of dynamic [...] Read more.
The strategic recovery of buoys is a critical task in executing deep-sea research missions, as nations extend their exploration of marine territories. This study primarily investigates the dynamics of remotely operated vehicle (ROV)-assisted salvage operations for floating bodies during the recovery of dynamic maritime targets. It focuses on the hydrodynamic coefficients of dual floating bodies in this salvage process. The interaction dynamics of the twin floats are examined using parameters such as the kinematic response amplitude operator (RAO), added mass, damping coefficient, and mean drift force. During the “berthing stage”, when the double floats are at Fr = 0.15–0.18, their roll and yaw Response Amplitude Operators are diminished, resulting in smoother motion. Thus, the optimal berthing speed range for this stage is Fr = 0.15–0.18. During the “side-by-side phase”, the spacing between the ROV and FLOAT under wave action should be approximately 0.4 L to 0.5 L. The coupled motion of twin floating bodies under the influence of following waves can further enhance their stability. The ideal towing speed during the “towing phase” is Fr = 0.2. This research aims to analyze the mutual influence between two floating bodies under wave action. By simulating the coupled motion of dual dynamic targets, we more precisely assess the risks and challenges inherent in salvage operations, thus providing a scientific basis for the design and optimization of salvage strategies. Full article
(This article belongs to the Special Issue Advances in Marine Engineering Hydrodynamics)
►▼ Show Figures

Figure 1

Figure 1
<p>Multi-float coordinate system.</p> Full article ">Figure 2
<p>Schematic diagram of the dynamic target salvage system (brown represents water, blue represents air): (<b>a</b>) overall view; (<b>b</b>) top view. Process 1 (P1) represents the berthing phase of floating body salvage, in which the ROV berths to the FLOAT; Process 2 (P2) represents the concurrent berthing phase of floating body salvage, in which the ROV and the FLOAT are free drifting in the sea; and Process 3 (P3) represents the salvage phase of floating body salvage, in which the ROV tows the FLOAT using a flexible cable.</p> Full article ">Figure 3
<p>Computational domain and boundary conditions (below the blue interface is water, top is air).</p> Full article ">Figure 4
<p>Schematic of the computational domain and the grid at the waterline (red part is the boundary interface, blue represents the symmetrical plane).</p> Full article ">Figure 5
<p>Schematic diagram of the grid of the floating body salvage system (blue represents the border interface, brown represents the cross-section).</p> Full article ">Figure 6
<p>Time step convergence verification curve.</p> Full article ">Figure 7
<p>Floating body salvage test flow chart.</p> Full article ">Figure 8
<p>Experimental equipment and layout: (<b>a</b>) wave pool; (<b>b</b>) FLOAT; (<b>c</b>) ROV; (<b>d</b>) attitude instrument; (<b>e</b>) dynamic target salvage site.</p> Full article ">Figure 9
<p>RAOs of 4DOFs: (<b>a</b>) Heave; (<b>b</b>) Roll; (<b>c</b>) Pitch; (<b>d</b>) Yaw.</p> Full article ">Figure 10
<p>Added Mass of 4DOFs: (<b>a</b>) Heave; (<b>b</b>) Roll; (<b>c</b>) Pitch; (<b>d</b>) Yaw.</p> Full article ">Figure 11
<p>Damping of 4DOFs: (<b>a</b>) Heave; (<b>b</b>) Roll; (<b>c</b>) Pitch; (<b>d</b>) Yaw.</p> Full article ">Figure 12
<p>Summary diagrams of two assessment elements (MA, RMS) of 4DOFs in different wave directions: (<b>a</b>) heave response; (<b>b</b>) roll response; (<b>c</b>) pitch response; (<b>d</b>) yaw response.</p> Full article ">Figure 12 Cont.
<p>Summary diagrams of two assessment elements (MA, RMS) of 4DOFs in different wave directions: (<b>a</b>) heave response; (<b>b</b>) roll response; (<b>c</b>) pitch response; (<b>d</b>) yaw response.</p> Full article ">Figure 13
<p>Comparison of Motion Responses for Single and Double Floating Bodies: (<b>a</b>) RAOs of ROV with single-float and double-float in terms of sway, surge, and heave; (<b>b</b>) RAOs of ROV with single-float and double-float in terms of roll, pitch, and yaw; (<b>c</b>) RAOs of FLOAT with single-float and double-float in terms of sway; (<b>c</b>) RAOs of roll, pitch, and yaw for FLOAT under single-float and double-float; (<b>d</b>) RAOs of roll, pitch, and yaw for FLOAT under single-float and double-float.</p> Full article ">Figure 14
<p>RAOs of 6DOFs at different berthing speeds: (<b>a</b>) Sway; (<b>b</b>) Surge; (<b>c</b>) Heave; (<b>d</b>) Roll; (<b>e</b>) Pitch; (<b>f</b>) Yaw.</p> Full article ">Figure 15
<p>Added Mass of 6DOFs at different berthing speeds: (<b>a</b>) Sway; (<b>b</b>) Surge; (<b>c</b>) Heave; (<b>d</b>) Roll; (<b>e</b>) Pitch; (<b>f</b>) Yaw.</p> Full article ">Figure 16
<p>Damping of 6DOFs at different berthing speeds: (<b>a</b>) Sway; (<b>b</b>) Surge; (<b>c</b>) Heave; (<b>d</b>) Roll; (<b>e</b>) Pitch; (<b>f</b>) Yaw.</p> Full article ">Figure 17
<p>Mean drift force of twin floats at different berthing speeds: (<b>a</b>) sway drift force; (<b>b</b>) surge drift force.</p> Full article ">Figure 18
<p>Cloud map of double floating body waves at different berthing speeds.</p> Full article ">Figure 19
<p>RAOs of 6DOFs at different distances: (<b>a</b>) Sway; (<b>b</b>) Surge; (<b>c</b>) Heave; (<b>d</b>) Roll; (<b>e</b>) Pitch; (<b>f</b>) Yaw.</p> Full article ">Figure 19 Cont.
<p>RAOs of 6DOFs at different distances: (<b>a</b>) Sway; (<b>b</b>) Surge; (<b>c</b>) Heave; (<b>d</b>) Roll; (<b>e</b>) Pitch; (<b>f</b>) Yaw.</p> Full article ">Figure 20
<p>Added Mass of 6DOFs at different distances: (<b>a</b>) Sway; (<b>b</b>) Surge; (<b>c</b>) Heave; (<b>d</b>) Roll; (<b>e</b>) Pitch; (<b>f</b>) Yaw.</p> Full article ">Figure 20 Cont.
<p>Added Mass of 6DOFs at different distances: (<b>a</b>) Sway; (<b>b</b>) Surge; (<b>c</b>) Heave; (<b>d</b>) Roll; (<b>e</b>) Pitch; (<b>f</b>) Yaw.</p> Full article ">Figure 21
<p>Damping of 6DOFs at different distances: (<b>a</b>) Sway; (<b>b</b>) Surge; (<b>c</b>) Heave; (<b>d</b>) Roll; (<b>e</b>) Pitch; (<b>f</b>) Yaw.</p> Full article ">Figure 21 Cont.
<p>Damping of 6DOFs at different distances: (<b>a</b>) Sway; (<b>b</b>) Surge; (<b>c</b>) Heave; (<b>d</b>) Roll; (<b>e</b>) Pitch; (<b>f</b>) Yaw.</p> Full article ">Figure 22
<p>Mean drift force of double floats with different distances: (<b>a</b>) sway drift force; (<b>b</b>) surge drift force.</p> Full article ">Figure 23
<p>Cloud map of double floating body waves at different distances.</p> Full article ">Figure 24
<p>RAOs of 6DOFs at different wave directions: (<b>a</b>) Sway; (<b>b</b>) Surge; (<b>c</b>) Heave; (<b>d</b>) Roll; (<b>e</b>) Pitch; (<b>f</b>) Yaw.</p> Full article ">Figure 24 Cont.
<p>RAOs of 6DOFs at different wave directions: (<b>a</b>) Sway; (<b>b</b>) Surge; (<b>c</b>) Heave; (<b>d</b>) Roll; (<b>e</b>) Pitch; (<b>f</b>) Yaw.</p> Full article ">Figure 25
<p>Added Mass of 6DOFs at different wave directions: (<b>a</b>) Sway; (<b>b</b>) Surge; (<b>c</b>) Heave; (<b>d</b>) Roll; (<b>e</b>) Pitch; (<b>f</b>) Yaw.</p> Full article ">Figure 25 Cont.
<p>Added Mass of 6DOFs at different wave directions: (<b>a</b>) Sway; (<b>b</b>) Surge; (<b>c</b>) Heave; (<b>d</b>) Roll; (<b>e</b>) Pitch; (<b>f</b>) Yaw.</p> Full article ">Figure 26
<p>Damping of 6DOFs at different wave directions: (<b>a</b>) Sway; (<b>b</b>) Surge; (<b>c</b>) Heave; (<b>d</b>) Roll; (<b>e</b>) Pitch; (<b>f</b>) Yaw.</p> Full article ">Figure 26 Cont.
<p>Damping of 6DOFs at different wave directions: (<b>a</b>) Sway; (<b>b</b>) Surge; (<b>c</b>) Heave; (<b>d</b>) Roll; (<b>e</b>) Pitch; (<b>f</b>) Yaw.</p> Full article ">Figure 27
<p>Mean drift force of double floats in different wave directions: (<b>a</b>) sway drift force; (<b>b</b>) surge drift force.</p> Full article ">Figure 28
<p>Cloud map of double floating body waves with different downward waves.</p> Full article ">Figure 29
<p>RAOs of 6DOFs at different towing speeds: (<b>a</b>) Sway; (<b>b</b>) Surge; (<b>c</b>) Heave; (<b>d</b>) Roll; (<b>e</b>) Pitch; (<b>f</b>) Yaw.</p> Full article ">Figure 29 Cont.
<p>RAOs of 6DOFs at different towing speeds: (<b>a</b>) Sway; (<b>b</b>) Surge; (<b>c</b>) Heave; (<b>d</b>) Roll; (<b>e</b>) Pitch; (<b>f</b>) Yaw.</p> Full article ">Figure 30
<p>Added Mass of 6DOFs at different towing speeds: (<b>a</b>) Sway; (<b>b</b>) Surge; (<b>c</b>) Heave; (<b>d</b>) Roll; (<b>e</b>) Pitch; (<b>f</b>) Yaw.</p> Full article ">Figure 30 Cont.
<p>Added Mass of 6DOFs at different towing speeds: (<b>a</b>) Sway; (<b>b</b>) Surge; (<b>c</b>) Heave; (<b>d</b>) Roll; (<b>e</b>) Pitch; (<b>f</b>) Yaw.</p> Full article ">Figure 31
<p>Damping of 6DOFs at different towing speeds: (<b>a</b>) Sway; (<b>b</b>) Surge; (<b>c</b>) Heave; (<b>d</b>) Roll; (<b>e</b>) Pitch; (<b>f</b>) Yaw.</p> Full article ">Figure 31 Cont.
<p>Damping of 6DOFs at different towing speeds: (<b>a</b>) Sway; (<b>b</b>) Surge; (<b>c</b>) Heave; (<b>d</b>) Roll; (<b>e</b>) Pitch; (<b>f</b>) Yaw.</p> Full article ">Figure 32
<p>Mean drift force of twin floats at different towing speeds: (<b>a</b>) sway drift force; (<b>b</b>) surge drift force.</p> Full article ">Figure 33
<p>Cloud map of double floating body waves at different speeds.</p> Full article ">
2 pages, 528 KiB  
Correction
Correction: Jung et al. Heat Integration of Liquid Hydrogen-Fueled Hybrid Electric Ship Propulsion System. J. Mar. Sci. Eng. 2023, 11, 2157
by Wongwan Jung, Jinkwang Lee and Daejun Chang
J. Mar. Sci. Eng. 2024, 12(9), 1687; https://doi.org/10.3390/jmse12091687 - 23 Sep 2024
Viewed by 336
Abstract
Error in Figure [...] Full article
22 pages, 14841 KiB  
Article
Hydrodynamics and Sediment Transport Under Solitary Waves in the Swash Zone
by Shuo Li, Wenxin Li, Huabin Shi and Xiafei Guan
J. Mar. Sci. Eng. 2024, 12(9), 1686; https://doi.org/10.3390/jmse12091686 - 23 Sep 2024
Viewed by 730
Abstract
Swash–swash interaction is a common natural phenomenon in the nearshore region, characterized by complex fluid motion. The characteristics of swash–swash interaction are crucial to sediment transport, subsequently affecting the beach morphology. This study investigates the hydrodynamics and sediment transport in swash–swash interaction under [...] Read more.
Swash–swash interaction is a common natural phenomenon in the nearshore region, characterized by complex fluid motion. The characteristics of swash–swash interaction are crucial to sediment transport, subsequently affecting the beach morphology. This study investigates the hydrodynamics and sediment transport in swash–swash interaction under two successive solitary waves using a two-phase Smoothed Particle Hydrodynamics (SPH) model. The effects of the time interval between the two waves are examined. It is shown that the time interval has a minor effect on the breaking and swash–swash interacting patterns as well as the final beach morphology but influences the run-up of the second wave and the instantaneous sediment flux. Under wave breaking in the swash–swash interaction, there is significant sediment suspension due to strong vortices, and the suspended sediment forms a plume upward from the bed. The sediment plumes gradually settle down as the vortices decay. These insights enhance the understanding of sediment transport and beach morphology under complex swash–swash interaction. Full article
(This article belongs to the Section Coastal Engineering)
►▼ Show Figures

Figure 1

Figure 1
<p>Sketch of the experimental setup. <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>H</mi> </mrow> <mrow> <mn>0</mn> </mrow> </msub> </mrow> </semantics></math> is the initial height of the generated solitary wave and <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>h</mi> </mrow> <mrow> <mn>0</mn> </mrow> </msub> </mrow> </semantics></math> is the initial water depth in the flume.</p> Full article ">Figure 2
<p>Comparisons between simulated (black solid curves) and measured (red circles) water depth <span class="html-italic">h</span> and near-bed horizontal velocity <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>u</mi> </mrow> <mrow> <mi>f</mi> </mrow> </msub> </mrow> </semantics></math> on the sandy slope at <math display="inline"><semantics> <mrow> <mi>x</mi> <mo>=</mo> <mn>11.51</mn> <mrow> <mo> </mo> <mi mathvariant="normal">m</mi> </mrow> </mrow> </semantics></math>.</p> Full article ">Figure 3
<p>Comparisons between simulated (black solid curve) and measured (red circles) beach profiles after the solitary-wave swash event. The black dashed line represents the initial beach profile (Initial bed).</p> Full article ">Figure 4
<p>Sketch of numerical experiments for swash–swash interaction under two successive solitary waves. <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>H</mi> </mrow> <mrow> <mn>0</mn> </mrow> </msub> </mrow> </semantics></math> is the initial height of the generated solitary wave, <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>h</mi> </mrow> <mrow> <mn>0</mn> </mrow> </msub> </mrow> </semantics></math> the initial water depth in the flume, and <math display="inline"><semantics> <mrow> <mi>L</mi> </mrow> </semantics></math> the distance between the peaks of the two successive solitary waves.</p> Full article ">Figure 5
<p>Trajectories of the wavemaker in four cases with different time interval <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>T</mi> </mrow> <mrow> <mi>s</mi> </mrow> </msub> </mrow> </semantics></math> between the two successive solitary waves. The black curve denotes the change in wavemaker displacement in the case P1 with a time interval of <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>T</mi> </mrow> <mrow> <mi>s</mi> </mrow> </msub> <mo>=</mo> <mn>3.7</mn> <mrow> <mo> </mo> <mi mathvariant="normal">s</mi> </mrow> </mrow> </semantics></math>, blue curve for that in P2 with <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>T</mi> </mrow> <mrow> <mi>s</mi> </mrow> </msub> <mo>=</mo> <mn>3.5</mn> <mrow> <mo> </mo> <mi mathvariant="normal">s</mi> </mrow> </mrow> </semantics></math>, cyan curve for that in P3 with <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>T</mi> </mrow> <mrow> <mi>s</mi> </mrow> </msub> <mo>=</mo> <mn>3.3</mn> <mrow> <mo> </mo> <mi mathvariant="normal">s</mi> </mrow> </mrow> </semantics></math>, and red curve for that in P4 with <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>T</mi> </mrow> <mrow> <mi>s</mi> </mrow> </msub> <mo>=</mo> <mn>3.1</mn> <mrow> <mo> </mo> <mi mathvariant="normal">s</mi> </mrow> </mrow> </semantics></math>.</p> Full article ">Figure 6
<p>The generated two successive solitary waves were observed at <math display="inline"><semantics> <mrow> <mi>x</mi> <mo>=</mo> <mn>5.0</mn> <mrow> <mo> </mo> <mi mathvariant="normal">m</mi> </mrow> </mrow> </semantics></math>, represented by the vertical displacement of the free surface above the initial elevation <math display="inline"><semantics> <mrow> <mi>η</mi> </mrow> </semantics></math>. <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>T</mi> </mrow> <mrow> <mi>s</mi> </mrow> </msub> </mrow> </semantics></math> is the time interval between the two successive solitary waves. The black curve denotes the change in wavemaker displacement in the case P1 with a time interval of <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>T</mi> </mrow> <mrow> <mi>s</mi> </mrow> </msub> <mo>=</mo> <mn>3.7</mn> <mrow> <mo> </mo> <mi mathvariant="normal">s</mi> </mrow> </mrow> </semantics></math>, blue curve for that in P2 with <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>T</mi> </mrow> <mrow> <mi>s</mi> </mrow> </msub> <mo>=</mo> <mn>3.5</mn> <mrow> <mo> </mo> <mi mathvariant="normal">s</mi> </mrow> </mrow> </semantics></math>, cyan curve for that in P3 with <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>T</mi> </mrow> <mrow> <mi>s</mi> </mrow> </msub> <mo>=</mo> <mn>3.3</mn> <mrow> <mo> </mo> <mi mathvariant="normal">s</mi> </mrow> </mrow> </semantics></math>, and red curve for that in P4 with <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>T</mi> </mrow> <mrow> <mi>s</mi> </mrow> </msub> <mo>=</mo> <mn>3.1</mn> <mrow> <mo> </mo> <mi mathvariant="normal">s</mi> </mrow> </mrow> </semantics></math>.</p> Full article ">Figure 7
<p>Snapshots of SPH particle distribution and their carried sediment concentration <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>α</mi> </mrow> <mrow> <mi>s</mi> </mrow> </msub> </mrow> </semantics></math> during the breaking of the preceding wave (<math display="inline"><semantics> <mrow> <mn>8.8</mn> <mo> </mo> <mi mathvariant="normal">s</mi> <mo>≤</mo> <mi>t</mi> <mo>≤</mo> <mn>9.4</mn> <mo> </mo> <mi mathvariant="normal">s</mi> </mrow> </semantics></math>) in Case P1. The color of the SPH particles represents the value of their sediment concentration.</p> Full article ">Figure 8
<p>Snapshots of SPH particle distribution and their carried sediment concentration <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>α</mi> </mrow> <mrow> <mi>s</mi> </mrow> </msub> </mrow> </semantics></math> during the breaking of the second wave (<math display="inline"><semantics> <mrow> <mn>12.5</mn> <mo> </mo> <mi mathvariant="normal">s</mi> <mo>≤</mo> <mi>t</mi> <mo>≤</mo> <mn>13.4</mn> <mo> </mo> <mi mathvariant="normal">s</mi> </mrow> </semantics></math>) in Case P1. The color of the SPH particles represents the value of their sediment concentration.</p> Full article ">Figure 9
<p>Distributions of fluid velocity <math display="inline"><semantics> <mrow> <msub> <mrow> <mi mathvariant="bold">u</mi> </mrow> <mrow> <mi>f</mi> </mrow> </msub> </mrow> </semantics></math> during the breaking of the second wave (<math display="inline"><semantics> <mrow> <mn>12.5</mn> <mo> </mo> <mi mathvariant="normal">s</mi> <mo>≤</mo> <mi>t</mi> <mo>≤</mo> <mn>13.4</mn> <mo> </mo> <mi mathvariant="normal">s</mi> </mrow> </semantics></math>) in Case P1.</p> Full article ">Figure 10
<p>Snapshots of SPH particle distribution and their carried sediment concentration <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>α</mi> </mrow> <mrow> <mi>s</mi> </mrow> </msub> </mrow> </semantics></math> after breaking of the second wave during <math display="inline"><semantics> <mrow> <mi>t</mi> <mo>=</mo> <mn>13.6</mn> <mo>~</mo> <mn>14.2</mn> <mo> </mo> <mi mathvariant="normal">s</mi> </mrow> </semantics></math> in Case P1. The color of the SPH particles represents the value of their sediment concentration. In (<b>b</b>), the solid squares outline the sediment plumes suspended from the sandy bed.</p> Full article ">Figure 11
<p>Distributions of fluid velocity <math display="inline"><semantics> <mrow> <msub> <mrow> <mi mathvariant="bold">u</mi> </mrow> <mrow> <mi>f</mi> </mrow> </msub> </mrow> </semantics></math> after breaking of the second wave during <math display="inline"><semantics> <mrow> <mi>t</mi> <mo>=</mo> <mn>13.6</mn> <mo>~</mo> <mn>14.2</mn> <mo> </mo> <mi mathvariant="normal">s</mi> </mrow> </semantics></math> in Case P1. In (<b>b</b>), the red circles outline the generated vortices from wave breaking and swash–swash interactions.</p> Full article ">Figure 12
<p>Snapshots of SPH particle distribution and the carried sediment concentration <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>α</mi> </mrow> <mrow> <mi>s</mi> </mrow> </msub> </mrow> </semantics></math> during <math display="inline"><semantics> <mrow> <mi>t</mi> <mo>=</mo> <mn>14.4</mn> <mo>~</mo> <mn>15.0</mn> <mo> </mo> <mi mathvariant="normal">s</mi> </mrow> </semantics></math> in Case P1. The color of the SPH particles represents the value of their sediment concentration. In (<b>c</b>), the solid square outlines the sediment plumes suspended from the sandy bed at the swash front.</p> Full article ">Figure 13
<p>Distributions of flow velocity <math display="inline"><semantics> <mrow> <msub> <mrow> <mi mathvariant="bold">u</mi> </mrow> <mrow> <mi>f</mi> </mrow> </msub> </mrow> </semantics></math> during <math display="inline"><semantics> <mrow> <mi>t</mi> <mo>=</mo> <mn>14.4</mn> <mo>~</mo> <mn>15.0</mn> <mo> </mo> <mi mathvariant="normal">s</mi> </mrow> </semantics></math> in Case P1.</p> Full article ">Figure 14
<p>Distributions of (<b>a</b>) SPH particle position and their carried sediment concentration <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>α</mi> </mrow> <mrow> <mi>s</mi> </mrow> </msub> </mrow> </semantics></math>; (<b>b</b>) flow velocity <math display="inline"><semantics> <mrow> <msub> <mrow> <mi mathvariant="bold">u</mi> </mrow> <mrow> <mi>f</mi> </mrow> </msub> </mrow> </semantics></math> at the instant <math display="inline"><semantics> <mrow> <mi>t</mi> <mo>=</mo> <mn>16.9</mn> <mo> </mo> <mi mathvariant="normal">s</mi> </mrow> </semantics></math> when the swash front reaches the maximum run-up in Case P1.</p> Full article ">Figure 15
<p>Temporal variations of horizontal sediment flux <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>q</mi> </mrow> <mrow> <mi>s</mi> </mrow> </msub> </mrow> </semantics></math> across the sections at (<b>a</b>) <math display="inline"><semantics> <mrow> <mi>x</mi> <mo>=</mo> <mn>21.5</mn> <mo> </mo> <mi mathvariant="normal">m</mi> </mrow> </semantics></math>; (<b>b</b>) <math display="inline"><semantics> <mrow> <mi>x</mi> <mo>=</mo> <mn>22.0</mn> <mo> </mo> <mi mathvariant="normal">m</mi> </mrow> </semantics></math>; (<b>c</b>) <math display="inline"><semantics> <mrow> <mi>x</mi> <mo>=</mo> <mn>22.5</mn> <mo> </mo> <mi mathvariant="normal">m</mi> </mrow> </semantics></math>; (<b>d</b>) <math display="inline"><semantics> <mrow> <mi>x</mi> <mo>=</mo> <mn>23.0</mn> <mo> </mo> <mi mathvariant="normal">m</mi> </mrow> </semantics></math> in the four cases. Section <math display="inline"><semantics> <mrow> <mi>x</mi> <mo>=</mo> <mn>21.5</mn> <mo> </mo> <mi mathvariant="normal">m</mi> </mrow> </semantics></math> is below the initial water level in the flume, while the other three are above. Black curves represent the temporal evolution of sediment flux in the case P1 with a time interval between the two successive waves of <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>T</mi> </mrow> <mrow> <mi>s</mi> </mrow> </msub> <mo>=</mo> <mn>3.7</mn> <mo> </mo> <mi mathvariant="normal">s</mi> </mrow> </semantics></math>, blue curves for that in P2 with <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>T</mi> </mrow> <mrow> <mi>s</mi> </mrow> </msub> <mo>=</mo> <mn>3.5</mn> <mo> </mo> <mi mathvariant="normal">s</mi> </mrow> </semantics></math>, cyan curves for that in P3 with <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>T</mi> </mrow> <mrow> <mi>s</mi> </mrow> </msub> <mo>=</mo> <mn>3.3</mn> <mo> </mo> <mi mathvariant="normal">s</mi> </mrow> </semantics></math>, and red curves for that in P4 with <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>T</mi> </mrow> <mrow> <mi>s</mi> </mrow> </msub> <mo>=</mo> <mn>3.1</mn> <mo> </mo> <mi mathvariant="normal">s</mi> </mrow> </semantics></math>.</p> Full article ">Figure 16
<p>Comparisons between the beach profiles after two successive wave swash events in Cases P1–P4. The black solid curve is the profile in Case P1 with a time interval between the two successive waves of <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>T</mi> </mrow> <mrow> <mi>s</mi> </mrow> </msub> <mo>=</mo> <mn>3.7</mn> <mo> </mo> <mi mathvariant="normal">s</mi> </mrow> </semantics></math>, blue curve of the profile in P2 with <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>T</mi> </mrow> <mrow> <mi>s</mi> </mrow> </msub> <mo>=</mo> <mn>3.5</mn> <mo> </mo> <mi mathvariant="normal">s</mi> </mrow> </semantics></math>, cyan curve the profile in P3 with <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>T</mi> </mrow> <mrow> <mi>s</mi> </mrow> </msub> <mo>=</mo> <mn>3.3</mn> <mo> </mo> <mi mathvariant="normal">s</mi> </mrow> </semantics></math>, and the red curve of the profile in P4 with <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>T</mi> </mrow> <mrow> <mi>s</mi> </mrow> </msub> <mo>=</mo> <mn>3.1</mn> <mo> </mo> <mi mathvariant="normal">s</mi> </mrow> </semantics></math>. The four curves almost overlap each other. The dashed line represents the initial profile of the beach, and the dotted-dashed line is the initial water level in the flume.</p> Full article ">Figure 17
<p>Comparisons between the distributions of fluid velocity <math display="inline"><semantics> <mrow> <msub> <mrow> <mi mathvariant="bold">u</mi> </mrow> <mrow> <mi>f</mi> </mrow> </msub> </mrow> </semantics></math> at a similar stage after breaking of the second wave in the four cases, i.e., (<b>a</b>) <math display="inline"><semantics> <mrow> <mi>t</mi> <mo>=</mo> <mn>13.8</mn> <mo> </mo> <mi mathvariant="normal">s</mi> </mrow> </semantics></math> in Case P1; (<b>b</b>) <math display="inline"><semantics> <mrow> <mi>t</mi> <mo>=</mo> <mn>13.6</mn> <mo> </mo> <mi mathvariant="normal">s</mi> </mrow> </semantics></math> in Case P2; (<b>c</b>) <math display="inline"><semantics> <mrow> <mi>t</mi> <mo>=</mo> <mn>13.4</mn> <mo> </mo> <mi mathvariant="normal">s</mi> </mrow> </semantics></math> in Case P3; (<b>d</b>) <math display="inline"><semantics> <mrow> <mi>t</mi> <mo>=</mo> <mn>13.2</mn> <mo> </mo> <mi mathvariant="normal">s</mi> </mrow> </semantics></math> in Case P4.</p> Full article ">Figure 18
<p>Comparisons between the snapshots of SPH particle distribution and their carried sediment concentration <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>α</mi> </mrow> <mrow> <mi>s</mi> </mrow> </msub> </mrow> </semantics></math> at a similar stage after breaking of the second wave in the four cases. The color of SPH particles represents the value of their sediment concentration. In subfigure (<b>a</b>), the black solid squares highlight the sediment plumes suspended from the sandy bed.</p> Full article ">Figure 19
<p>Comparisons between temporal variations of horizontal sediment flux <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>q</mi> </mrow> <mrow> <mi>s</mi> </mrow> </msub> </mrow> </semantics></math> at the section <math display="inline"><semantics> <mrow> <mn>9.47</mn> <mrow> <mo> </mo> <mi mathvariant="normal">m</mi> </mrow> </mrow> </semantics></math> away from the slope toe, i.e., the section <math display="inline"><semantics> <mrow> <mi>x</mi> <mo>=</mo> <mn>21.5</mn> <mo> </mo> <mi mathvariant="normal">m</mi> </mrow> </semantics></math> shown in <a href="#jmse-12-01686-f015" class="html-fig">Figure 15</a>a, in the single-wave and two-wave cases. The time for plotting <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>t</mi> </mrow> <mrow> <mi>s</mi> </mrow> </msub> </mrow> </semantics></math> starts from the instant of the wave breaking. The black curve is the result in the single-wave case, i.e., the validation case in <a href="#sec2dot3-jmse-12-01686" class="html-sec">Section 2.3</a>. The red curve shows the result in the two-wave case, i.e., Case P1 in <a href="#sec3-jmse-12-01686" class="html-sec">Section 3</a>, with a time interval of 3.7 s between the two successive solitary waves.</p> Full article ">Figure 20
<p>Comparisons between the beach profiles after a single-wave and two-wave swash event. <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>x</mi> </mrow> <mrow> <mi>s</mi> </mrow> </msub> </mrow> </semantics></math> is the distance away from the slope toe. The black solid curve is the beach profile after the single-wave swash event in the validation case of <a href="#sec2dot3-jmse-12-01686" class="html-sec">Section 2.3</a>. The red solid curve is the beach profile after the two-wave swash–swash interaction event in Case P1 of <a href="#sec3-jmse-12-01686" class="html-sec">Section 3</a>. The black dashed line is the initial profile of the sandy beach, and the dotted-dashed line represents the initial water surface in the flume.</p> Full article ">
2 pages, 129 KiB  
Editorial
Challenges and Opportunities of Maritime Transport in the Post-Epidemic Era
by Guangnian Xiao and Lang Xu
J. Mar. Sci. Eng. 2024, 12(9), 1685; https://doi.org/10.3390/jmse12091685 - 23 Sep 2024
Cited by 4 | Viewed by 985
Abstract
The global outbreak of COVID-19 has cast a protracted shadow over the international economic landscape, with maritime transport emerging as a cornerstone of resilience and adaptation [...] Full article
(This article belongs to the Special Issue Challenges and Opportunities of Maritime Transport in the Post-Epidemic Era)
18 pages, 52176 KiB  
Article
Design and Deployment of a Floating Porous Screen Breakwater in a Mesotidal Environment
by Brandon Lieberthal, Richard Perry, Elisabeth Younce, Liam Hanley, Mary Bryant and Kimberly Huguenard
J. Mar. Sci. Eng. 2024, 12(9), 1684; https://doi.org/10.3390/jmse12091684 - 23 Sep 2024
Viewed by 867
Abstract
The performance of an intermediate-scale modular, permeable, floating breakwater comprised of an array of vertical screens is optimized and tested. A distinctive attribute of this breakwater design is its adaptive capacity to fluctuating water levels owing to its floating configuration, thereby preserving its [...] Read more.
The performance of an intermediate-scale modular, permeable, floating breakwater comprised of an array of vertical screens is optimized and tested. A distinctive attribute of this breakwater design is its adaptive capacity to fluctuating water levels owing to its floating configuration, thereby preserving its efficacy during high tide and storm tide scenarios—an advancement over conventional bottom-mounted structures. The initial validation of the concept was tested in a laboratory wave basin in regular waves, which demonstrated promising results for three porous panels. Next, the breakwater’s design parameters were optimized using a finite difference computational fluid dynamics software, (FLOW-3D version 2023R2), considering porosity, spacing, and panel count. A scaled prototype, representative of a 1:2 ratio was then deployed during the summer of 2022 along the coast of Castine, ME, within a mesotidal, semi-sheltered system characterized by tidal currents and waves. Notably, the breakwater succeeded in attenuating half of the wave energy for periods shorter than 4 s, evidenced by transmission coefficients below 0.5, making this technology suitable for locally generated waves with shorter periods. During storm events, instantaneous transmission coefficients decreased to as low as 0.25, coinciding with significant wave heights exceeding 0.8 m. Additionally, the efficacy of wave attenuation improved slightly over time as biofoulants adhered to the structure, thereby enhancing drag and mass. Full article
(This article belongs to the Section Ocean Engineering)
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<p>(<b>a</b>) Assembly diagram of a single porous screen. (<b>b</b>) Diagram of the 1:6-scale breakwater tested in the W<sup>2</sup> facility at the University of Maine ASCC. (<b>c</b>) Comparison of transmission coefficients <math display="inline"><semantics> <msub> <mi>K</mi> <mi>T</mi> </msub> </semantics></math> and the ratio of transmitted to incident wave energy for the simulated and laboratory-tested 1:6-scale breakwater models. All spatial dimensions are in meters.</p> Full article ">Figure 2
<p>Transmission coefficient <math display="inline"><semantics> <msub> <mi>K</mi> <mi>T</mi> </msub> </semantics></math> for 1–6 wave screens with a scaled wave height of 1 m over a range of periods, comparing laboratory tests (W-2) and computer simulations (FLOW-3D).</p> Full article ">Figure 3
<p>(<b>a</b>) Flow chart showing the black-box optimization process in FLOW-3D for the 1:2-scale model, (<b>b</b>) response curves of transmission and reflection energy coefficients based on design parameters, and (<b>c</b>) the original and optimized design parameters of the 1:2-scale model.</p> Full article ">Figure 4
<p>Simulation results for the optimized 1:2-scale floating breakwater in Castine Bay Beaufort Scale 2 conditions. Incident waves are generated at <math display="inline"><semantics> <mrow> <mi>x</mi> <mo>=</mo> <mn>0</mn> </mrow> </semantics></math> and propagate to the right. Relative energy loss profiles, defined as <math display="inline"><semantics> <mrow> <mo>Δ</mo> <mi>E</mi> <mo>=</mo> <mstyle scriptlevel="0" displaystyle="true"> <mfrac> <mrow> <mi>w</mi> <mi>a</mi> <mi>v</mi> <mi>e</mi> <mspace width="0.166667em"/> <mi>e</mi> <mi>n</mi> <mi>e</mi> <mi>r</mi> <mi>g</mi> <mi>y</mi> </mrow> <mrow> <mi>i</mi> <mi>n</mi> <mi>c</mi> <mi>i</mi> <mi>d</mi> <mi>e</mi> <mi>n</mi> <mi>t</mi> <mspace width="0.166667em"/> <mi>e</mi> <mi>n</mi> <mi>e</mi> <mi>r</mi> <mi>g</mi> <mi>y</mi> </mrow> </mfrac> </mstyle> <mo>−</mo> <mn>1</mn> </mrow> </semantics></math>, are shown with screen porosities of 0%, 10%, and 23%.</p> Full article ">Figure 5
<p>Lateral and vertical profiles for (<b>a</b>) x-velocity, (<b>b</b>) energy dissipation for the 1:2-scale model with 10% porosity, using the same wind-wave conditions as in <a href="#jmse-12-01684-f004" class="html-fig">Figure 4</a>. Data are averaged over the z-extent and y-extent of the breakwater.</p> Full article ">Figure 6
<p>(<b>a</b>) Crane lift of one unit of the breakwater. (<b>b</b>) Completed breakwater units on the bulkhead of Maine Maritime Academy. (<b>c</b>) Wave attenuation of the breakwater on field site. (<b>d</b>) Drone capture of wave attenuation on 25 July 2022.</p> Full article ">Figure 7
<p>(<b>a</b>) Layout of breakwater and spotter buoys. The assembly was situated approximately 400 m from shore, and the primary wave direction was north-east. (<b>b</b>,<b>c</b>) Site map of data collection area in Penobscot Bay where the wave direction rose. Contour lines indicate seabed depth in meters. Three additional buoys are located 0.5 km south of the breakwater assembly to observe wave transformations.</p> Full article ">Figure 8
<p>Metocean conditions for the F01 buoy upstream of the breakwater assembly. <b>The top panel</b> shows the principal component of velocity at a 2 m depth. <b>The bottom panel</b> shows significant wave heights (left) and the peak wave period (right). The locations of these data are identified in <a href="#jmse-12-01684-f007" class="html-fig">Figure 7</a>b.</p> Full article ">Figure 9
<p>(<b>a</b>) Heat map of wave periods and significant weight heights throughout the deployment period. (<b>b</b>) Plot of transmitted/reflected energy coefficient compared to wave frequency.</p> Full article ">Figure 10
<p>(<b>top</b>) Wind speed and barometric pressure data collected during 1:2-scale breakwater deployment in Castine, with storm periods boxed in red; (<b>middle</b>) Significant wave height and peak wave period; (<b>bottom</b>) transmission coefficient <math display="inline"><semantics> <msub> <mi>K</mi> <mrow> <mi>T</mi> <mn>4</mn> </mrow> </msub> </semantics></math> throughout the sampling period. The red dotted lines indicate the threshold for storm conditions.</p> Full article ">Figure 11
<p>(<b>a</b>) Alaria esculenta and other macroalgae on Assembly 1. (<b>b</b>) <span class="html-italic">Ascophyllum nodosum</span>, <span class="html-italic">f. scorpioides</span> and <span class="html-italic">Fucus vesiculosus</span> in the upper corner of Assembly 2. (<b>c</b>) Green crab that jumped from the breakwater during crane lift. (<b>d</b>) Microalgae, barnacles, and other organisms on a wall of Assembly 1. (<b>e</b>) Mussels and macroalgae on Assembly 1. (<b>f</b>) Full view of Assembly 2 being lifted onto the bulkhead, with notably less macroalgae than Assembly 1. (<b>g</b>) Macroalgae on lower Assembly 1. (<b>h</b>) Sea star that fell from the breakwater during crane lift.</p> Full article ">
34 pages, 23658 KiB  
Article
Deep Learning-Based Nonparametric Identification and Path Planning for Autonomous Underwater Vehicles
by Bin Mei, Chenyu Li, Dongdong Liu and Jie Zhang
J. Mar. Sci. Eng. 2024, 12(9), 1683; https://doi.org/10.3390/jmse12091683 - 22 Sep 2024
Viewed by 816
Abstract
As the nonlinear and coupling characteristics of autonomous underwater vehicles (AUVs) are the challenges for motion modeling, the nonparametric identification method is proposed based on dung beetle optimization (DBO) and deep temporal convolutional networks (DTCNs). First, the improved wavelet threshold is utilized to [...] Read more.
As the nonlinear and coupling characteristics of autonomous underwater vehicles (AUVs) are the challenges for motion modeling, the nonparametric identification method is proposed based on dung beetle optimization (DBO) and deep temporal convolutional networks (DTCNs). First, the improved wavelet threshold is utilized to select the optimal threshold and wavelet basis functions, and the raw model test data are denoising. Second, the bidirectional temporal convolutional networks, the bidirectional gated recurrent unit, and the attention mechanism are used to achieve the nonlinear nonparametric model of the AUV motion. And the hyperparameters are optimized by the DBO. Finally, the lazy-search-based path planning and the line-of-sight-based path following control are used for the proposed AUV model. The simulation shows that the prediction accuracy of the DBO-DTCN is better than other artificial intelligence methods and mechanical models, and the path following of AUV is feasible. The methods proposed in this paper can provide an effective strategy for AUV modeling, searching, and rescue cruising. Full article
(This article belongs to the Section Ocean Engineering)
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<p>Threshold functions comparison and application to surge velocity filtering. (<b>a</b>) Different threshold curves; (<b>b</b>) the result of filtering by different threshold functions.</p> Full article ">Figure 2
<p>Different decomposition curves with varies basis function of suge velocity. (<b>a</b>) Noise Data Curve; (<b>b</b>) curves of haar; (<b>c</b>) curves of db3; (<b>d</b>) curves of bior2.4; (<b>e</b>) curves of coif3; (<b>f</b>) curves of sym2; (<b>g</b>) curves of dmey; (<b>h</b>) curves of rbio2.4.</p> Full article ">Figure 3
<p>Noise reduction based on wavelet and comparison for different basis functions.</p> Full article ">Figure 4
<p>AUV earth-fixed and body-fixed coordinate systems.</p> Full article ">Figure 5
<p>TCN model structure.</p> Full article ">Figure 6
<p>GRU model structure.</p> Full article ">Figure 7
<p>Self-attention model structure.</p> Full article ">Figure 8
<p>The proposed DTCN model structure for AUV nonparameter identification.</p> Full article ">Figure 9
<p>Three-dimensional plot of the different functions and convergence curves. (<b>a</b>) F1 function and convergence curves; (<b>b</b>) F2 function and convergence curves; (<b>c</b>) F3 function and convergence curves; (<b>d</b>) F4 function and convergence curves; (<b>e</b>) F5 function and convergence curves; (<b>f</b>) F6 function and convergence curves.</p> Full article ">Figure 10
<p>The proposed flowchart for AUV nonparametric identification based on DBO-DTCN.</p> Full article ">Figure 11
<p>Cost function convergences of 6-DOF’s DTCN. (<b>a</b>) Learning rate iterative curve; (<b>b</b>) regularization iterative curve; (<b>c</b>) neuron number iterative curve; (<b>d</b>) keys iterative curve; (<b>e</b>) convergent iteration curve of DTCN.</p> Full article ">Figure 12
<p>Validation example of AUV for 10°/10° HZZ; (<b>a</b>) surge velocity; (<b>b</b>) sway velocity; (<b>c</b>) heave velocity; (<b>d</b>) yawing velocity; (<b>e</b>) roll velocity; (<b>f</b>) pitch velocity; (<b>g</b>) yaw angle; (<b>h</b>) roll angle; and (<b>i</b>) pitch angle [<a href="#B59-jmse-12-01683" class="html-bibr">59</a>].</p> Full article ">Figure 13
<p>Validation example of AUV for 10°/10° VZZ. (<b>a</b>) surge velocity; (<b>b</b>) sway velocity; (<b>c</b>) heave velocity; (<b>d</b>) yawing velocity; (<b>e</b>) roll velocity; (<b>f</b>) pitch velocity; (<b>g</b>) yaw angle; (<b>h</b>) roll angle; and (<b>i</b>) pitch angle [<a href="#B33-jmse-12-01683" class="html-bibr">33</a>,<a href="#B34-jmse-12-01683" class="html-bibr">34</a>,<a href="#B59-jmse-12-01683" class="html-bibr">59</a>].</p> Full article ">Figure 14
<p>Validation example of AUV for 20° Turning test. (<b>a</b>) Surge velocity; (<b>b</b>) sway velocity; (<b>c</b>) heave velocity; (<b>d</b>) yawing angular velocity; (<b>e</b>) roll velocity; (<b>f</b>) pitch velocity; (<b>g</b>) yaw angle; (<b>h</b>) roll angle; and (<b>i</b>) pitch angle [<a href="#B59-jmse-12-01683" class="html-bibr">59</a>].</p> Full article ">Figure 15
<p>Validation datasets for AUV DBO-DTCN trajectories. (<b>a</b>) The 10°/10° HZZ; (<b>b</b>) the 10°/10° VZZ; (<b>c</b>) the 20° Turning test [<a href="#B59-jmse-12-01683" class="html-bibr">59</a>].</p> Full article ">Figure 16
<p>Radar error of R<sup>2</sup> [<a href="#B59-jmse-12-01683" class="html-bibr">59</a>].</p> Full article ">Figure 17
<p>Stacked bar chart of SMAPE [<a href="#B59-jmse-12-01683" class="html-bibr">59</a>].</p> Full article ">Figure 18
<p>Violin diagram of surge velocity.</p> Full article ">Figure 18 Cont.
<p>Violin diagram of surge velocity.</p> Full article ">Figure 19
<p>Errors of predicted roll velocity. (<b>a</b>) 10°/10° HZZ; (<b>b</b>) 10°/10° VZZ; (<b>c</b>) 20° Turning test [<a href="#B59-jmse-12-01683" class="html-bibr">59</a>].</p> Full article ">Figure 20
<p>Marine environmental model based on 3D ECDIS data. (<b>a</b>) ECDIS of Ningbo; (<b>b</b>) vector data based on parsing and extracting ECDIS data; (<b>c</b>) two-dimensional chart of the marine environment; (<b>d</b>) three-dimensional chart of the marine environment.</p> Full article ">Figure 21
<p>Three algorithms path planning diagram. (<b>a</b>) Three-dimensional path planning; (<b>b</b>) two-dimensional path planning; (<b>c</b>) path planning on chart.</p> Full article ">Figure 22
<p>The LOS principle.</p> Full article ">Figure 23
<p>Path following architecture.</p> Full article ">Figure 24
<p>Path following simulation result with different current speed. (<b>a</b>) Surge velocity; (<b>b</b>) sway velocity; (<b>c</b>) heave velocity; (<b>d</b>) yawing velocity; (<b>e</b>) roll velocity; (<b>f</b>) pitch velocity; (<b>g</b>) yaw angle; (<b>h</b>) roll angle; and (<b>i</b>) pitch angle.</p> Full article ">Figure 25
<p>Rudder outputs and position errors of for AUV path following in SAR. (<b>a</b>) Vertical rudder simulation results; (<b>b</b>) horizontal rudder simulation results; (<b>c</b>) cross-tracking error <span class="html-italic">e<sub>y</sub></span>; (<b>d</b>) depth tracking error <span class="html-italic">e<sub>z</sub></span>.</p> Full article ">Figure 26
<p>Track result of AUV path following control at different current speed. (<b>a</b>) Three-dimensional top view; (<b>b</b>) three-dimensional side view.</p> Full article ">
25 pages, 8384 KiB  
Article
Functional Conception of Biomimetic Artificial Reefs Using Parametric Design and Modular Construction
by Dmytro Maslov, Fabio Cruz, Marisa Pinheiro, Tiago Miranda, Isabel Brito Valente, Vasco Ferreira and Eduardo Pereira
J. Mar. Sci. Eng. 2024, 12(9), 1682; https://doi.org/10.3390/jmse12091682 - 20 Sep 2024
Viewed by 1014
Abstract
Artificial reefs featuring different shapes and functions have been deployed around the world, causing impacts on marine ecosystems. However, the approaches typically used to deliver topological complexity, flexibility and expanding requirements to prospective structures during the initial design stages are not well established. [...] Read more.
Artificial reefs featuring different shapes and functions have been deployed around the world, causing impacts on marine ecosystems. However, the approaches typically used to deliver topological complexity, flexibility and expanding requirements to prospective structures during the initial design stages are not well established. The aim of this study was to highlight the advantages and provide evidence on how modularity and parametric design can holistically leverage the performance of multifunctional artificial reefs (MFARs). In particular, the goal was to develop a parametric design for MFAR and establish a direct relationship between specific design parameters and the MFAR target functions or design requirements. The idea of implementing the parametric design for generating the initial biomimetic geometry of the individual modular unit was explored. Furthermore, possible ways of manipulating the geometric parameters of the individual module and the whole assembly were proposed. The findings suggest that, by adopting the developed procedure and the examples studied, several functions may be reached within a single assembly: the promotion of marine biodiversity restoration, the support of scientific platforms with various sensors, as well as the development of recreational diving and of touristic attraction areas. Acquired knowledge suggests that the concept of a nature-like design approach was developed for artificial reefs with varying scales, complexity and functions, which widens the range of possibilities of how smart design of human-made underwater structures may contribute to benefiting the near shore ecosystems. Full article
(This article belongs to the Section Coastal Engineering)
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<p>Transition from a gray to a green marine shore infrastructure, adopted from [<a href="#B19-jmse-12-01682" class="html-bibr">19</a>].</p> Full article ">Figure 2
<p>(<b>a</b>) Typical geotextile AR installation geometry, adopted from [<a href="#B52-jmse-12-01682" class="html-bibr">52</a>]; (<b>b</b>) schematic view of the AR designated for coastal protection and surf enhancement, adopted from [<a href="#B51-jmse-12-01682" class="html-bibr">51</a>].</p> Full article ">Figure 3
<p>Representation of the existing AR shape in relation to the potential function: lower left corner—recreational diving and tourism attraction; upper corner—fisheries and biodiversity restoration; lower right corner—coastal erosion and surfing enhancement.</p> Full article ">Figure 4
<p>Large scale underwater rocky formations at the MPA close to Esposende, Portugal.</p> Full article ">Figure 5
<p>(<b>a</b>) Coral branch, used as an inspiration for the macro- and micro-structural features design of the future unit and MFAR design, (<b>b</b>) pilot design of the MFAR modular unit.</p> Full article ">Figure 6
<p>MFAR module design depiction: (<b>a</b>) base skeleton of the geometry with connection points (green crosses) where curves depict the curvature of tubes and the circle in the center depicts the sphere geometry; (<b>b</b>) module geometry prepared for subsequent prototyping.</p> Full article ">Figure 7
<p>Variations of the geometry made possible by the parametric design approach of the modular AR: (<b>a</b>) initial arrangement of four modules, (<b>b</b>,<b>c</b>) same arrangement but changed by only manipulating a double tube end location index in the graphical interface of the parametric design software (<b>bottom right</b>).</p> Full article ">Figure 8
<p>Possibilities of parametric programming when applied to the design of modular AR: (<b>a</b>) surface refinement of the AR unit using Boolean operations with various geometries and (<b>b</b>) randomly mutually oriented and distributed three-dimensional array of a module.</p> Full article ">Figure 9
<p>MFAR arrangements with only one type of module: (<b>a</b>) reticulated and (<b>b</b>) vertically expanded with additional surface refinement.</p> Full article ">Figure 10
<p>Prototyping the previously designed modules: (<b>a</b>) module prototype created using additive manufacturing and plastic; (<b>b</b>) module cast using concrete; (<b>c</b>) glued arrangement involving concrete parts, submerged in water.</p> Full article ">Figure 11
<p>Virtual representation of MFAR arrangement involving the modified geometry of the units: (<b>a</b>) increased sphere radius at the center of the module structure; (<b>b</b>) mirrored pieces joint; (<b>c</b>) MFAR arrangements offered by two types of module geometries—initial and mirrored, enumeration is explained in the text; divers are approximately 1.8 m tall, provided for the sake of scale.</p> Full article ">Figure 11 Cont.
<p>Virtual representation of MFAR arrangement involving the modified geometry of the units: (<b>a</b>) increased sphere radius at the center of the module structure; (<b>b</b>) mirrored pieces joint; (<b>c</b>) MFAR arrangements offered by two types of module geometries—initial and mirrored, enumeration is explained in the text; divers are approximately 1.8 m tall, provided for the sake of scale.</p> Full article ">Figure 12
<p>MFAR arrangement involving the modified module geometry: visual presentation of the MFAR with optimal modular structure (<b>a</b>); prototype of the MFAR assembled from two types of modular pieces (<b>b</b>).</p> Full article ">Figure 13
<p>Representation of the existing AR and proposed MFAR shape in relation to the potential function.</p> Full article ">Figure 14
<p>Reef barrier parametrically designed and assembled from the multiple MFARs with connecting nodes.</p> Full article ">Figure 15
<p>MFAR total surface area and projection area.</p> Full article ">Figure 16
<p>Four cases of surface refinement in order to evaluate the complexity index.</p> Full article ">
17 pages, 7723 KiB  
Article
Periodic Behavior and Noise Characteristics of Cavitating Flow around Two-Dimensional Hydrofoils
by Namug Heo and Ji-Hye Kim
J. Mar. Sci. Eng. 2024, 12(9), 1681; https://doi.org/10.3390/jmse12091681 - 20 Sep 2024
Viewed by 551
Abstract
The occurrence of cavitation in marine propellers is a major source of noise in ships. Consequently, the occurrence and noise characteristics of cavitation must be better understood to control this issue. This study focuses on identifying the occurrence and noise characteristics of cavitating [...] Read more.
The occurrence of cavitation in marine propellers is a major source of noise in ships. Consequently, the occurrence and noise characteristics of cavitation must be better understood to control this issue. This study focuses on identifying the occurrence and noise characteristics of cavitating flow around two-dimensional (2D) hydrofoils. Using the commercial computational fluid dynamics software STAR-CCM+, a numerical analysis was conducted on the partial cavity flow occurring around 2D hydrofoils at specific angles of attack. In addition, the cavitation noise characteristics were analyzed by conducting a frequency analysis using the predicted pressure data obtained via a fluctuating pressure sensor positioned vertically above the hydrofoil. Consequently, the numerical results were compared with existing experimental data to validate the accuracy of the simulation. This study identifies the limitations of the Reynolds-averaged Navier–Stokes (RANS) method by closely comparing it with the large eddy simulation (LES) method for assessing noise characteristics in unsteady cavitating flow. Although RANS has limitations in qualitatively assessing periodic behavior compared to LES, it effectively predicts cavitation extent and is valuable for relative assessments in practical applications. Full article
(This article belongs to the Section Ocean Engineering)
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<p>Computation domain and boundary conditions (validation case, Delft twisted 11).</p> Full article ">Figure 2
<p>Numerical grid system (validation case, Delft twisted 11).</p> Full article ">Figure 3
<p>Computation domain and boundary conditions (test case, NACA 0012). (<b>a</b>) Numerical domain overview; (<b>b</b>) numerical domain side view.</p> Full article ">Figure 4
<p>Numerical grid system (test case, NACA 0012).</p> Full article ">Figure 5
<p>Sound pressure level induced by pressure fluctuations [<a href="#B16-jmse-12-01681" class="html-bibr">16</a>] (validation case, Delft twisted 11).</p> Full article ">Figure 6
<p>Periodic behavior of cavity analyzed with the RANS method (σ = 2.53).</p> Full article ">Figure 7
<p>Periodic behavior of cavity analyzed with the large eddy simulation (LES) method (σ = 2.53).</p> Full article ">Figure 8
<p>Periodic behavior of cavity analyzed with the RANS method (σ = 2.27).</p> Full article ">Figure 9
<p>Periodic behavior of cavity analyzed with the LES method (σ = 2.27).</p> Full article ">Figure 10
<p>Periodic behavior of cavity analyzed with the RANS method (σ = 2.05).</p> Full article ">Figure 11
<p>Periodic behavior cavity analyzed with the LES method (σ = 2.05).</p> Full article ">Figure 12
<p>Comparison of non-dimensional cavity length: experimental vs. numerical results (reproduced with permission from Ref. [<a href="#B19-jmse-12-01681" class="html-bibr">19</a>]).</p> Full article ">Figure 13
<p>Comparison of periodic behavior of cavitating flow around the hydrofoil (σ = 2.05): Experiment: (<b>left</b>) (reprinted with permission from Ref. [<a href="#B19-jmse-12-01681" class="html-bibr">19</a>]), LES: (<b>right</b>).</p> Full article ">Figure 14
<p>Comparison of the sound pressure level of cavitating flow around the hydrofoil analyzed using the LES method.</p> Full article ">Figure 15
<p>Comparison of the sound pressure level of cavitating flow around the hydrofoil between experi-mental and numerical analyses (σ = 2.27, reproduced with permission from Ref. [<a href="#B19-jmse-12-01681" class="html-bibr">19</a>]).</p> Full article ">Figure 16
<p>Comparison of sound pressure level around the hydrofoil under cavitating and non-cavitating conditions (reproduced with permission from Ref. [<a href="#B19-jmse-12-01681" class="html-bibr">19</a>]).</p> Full article ">
20 pages, 5481 KiB  
Article
Ocean Currents Velocity Hindcast and Forecast Bias Correction Using a Deep-Learning Approach
by Ali Muhamed Ali, Hanqi Zhuang, Yu Huang, Ali K. Ibrahim, Ali Salem Altaher and Laurent M. Chérubin
J. Mar. Sci. Eng. 2024, 12(9), 1680; https://doi.org/10.3390/jmse12091680 - 20 Sep 2024
Viewed by 655
Abstract
Today’s prediction of ocean dynamics relies on numerical models. However, numerical models are often unable to accurately model and predict real ocean dynamics, leading to a lack of fulfillment of a range of services that require reliable predictions at various temporal and spatial [...] Read more.
Today’s prediction of ocean dynamics relies on numerical models. However, numerical models are often unable to accurately model and predict real ocean dynamics, leading to a lack of fulfillment of a range of services that require reliable predictions at various temporal and spatial scales. Indeed, a numerical model cannot fully resolve all the physical processes in the ocean due to various reasons, including biases in the initial field and calculation errors in the numerical solution of the model. Thus, bias-correcting methods have become crucial to improve the dynamical accuracy of numerical model predictions. In this study, we present a machine learning-based three-dimensional velocity bias correction method derived from historical observations that applies to both hindcast and forecast. Our approach is based on the modification of an existing deep learning model, called U-Net, designed specifically for image segmentation analysis in the biomedical field. U-Net was modified to create a Transform Model that retains the temporal and spatial evolution of the differences between the model and observations to produce a correction in the form of regression weights that evolves spatially and temporally with the model both forward and backward in time, beyond the observation period. Using daily ocean current observations from a 2.5-year current meter array deployment, we show that significant bias corrections can be conducted up to 50 days pre- or post-observations. Using a 3-year-long virtual array, valid bias corrections can be conducted for up to one year. Full article
(This article belongs to the Section Ocean Engineering)
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<p>(<b>a</b>) Observational array of individual mooring locations and types used in this study. The dates indicate the beginning and end of the measurements. The green contours show the depth in meters. The thick pink line shows the average path of the loop current. (<b>b</b>) Transform model concept with a deep learning approach. Despite the shorter duration of the observations, the overlapping model field can be corrected beyond the observation period.</p> Full article ">Figure 2
<p>The STU-Net structure used in this study in which the original SoftMax and segmentation layers were replaced with a regression layer.</p> Full article ">Figure 3
<p>Taylor diagrams of the spatially averaged current velocity magnitude for HYCOM and MITgcm-GoM and Dynloop (observations) at depths of (<b>a</b>) 0 m, (<b>b</b>) 20 m, (<b>c</b>) 100 m, and (<b>d</b>) 500 m. The red dot shows the RMSD/RMSE for each model at the same time by the relative position to the dashed green curves, the correlation coefficient by the straight blue dashed lines, the standard deviation on the <span class="html-italic">x</span> or <span class="html-italic">y</span> axis, and the red curve showing the correspondence between the two axes. The red dot on the <span class="html-italic">x</span> axis shows the standard deviation of the in-situ observations.</p> Full article ">Figure 4
<p>Surface velocity magnitude Root Mean Squared Error (RMSE) in m.s<sup>−1</sup> of the original and the transformed model for (<b>a</b>) the HYCOM model and (<b>b</b>) of the MITgcm-GoM model (note the different vertical axis range), and the correlation coefficient (CC) of the original and the transformed model for (<b>c</b>) the HYCOM model and (<b>d</b>) the MITgcm-GoM model. The shaded regions show the variance of the error field between the original and transformed.</p> Full article ">Figure 5
<p>First Empirical Orthogonal Function (EOF) mode of the surface velocity fields over the 50-day testing period of the Transform Model. From left to right is shown the observed, model original, and transformed field. (<b>a</b>) HYCOM and (<b>b</b>) MITgcm-GoM.</p> Full article ">Figure 6
<p>Average subsurface velocity metrics between the original and the transformed HYCOM velocity tensor time series. Root Mean Squared Error (RMSE) in m.s<sup>−1</sup> for (<b>a</b>) zonal flow and (<b>b</b>) meridional flow, and correlation coefficient (CC) for (<b>c</b>) zonal flow and (<b>d</b>) meridional flow. The shaded regions show the variance of the error field between the original and transformed.</p> Full article ">Figure 7
<p>Same as <a href="#jmse-12-01680-f006" class="html-fig">Figure 6</a> but for the MITgcm-GoM model. Root Mean Squared Error (RMSE) in m.s<sup>−1</sup> for (<b>a</b>) zonal flow and (<b>b</b>) meridional flow, and correlation coefficient (CC) for (<b>c</b>) zonal flow and (<b>d</b>) meridional flow. The shaded regions show the variance of the error field between the original and transformed.</p> Full article ">Figure 8
<p>Transformed HYCOM three-dimensional velocity structures on day 50 after the end of the observation period. Both the structures inside (<b>a</b>) and on the boundary (<b>b</b>) of the transformed volume are shown. The left, middle and right plots show the original HYCOM, the Dynloop, and the transformed flow fields.</p> Full article ">Figure 9
<p>Same as <a href="#jmse-12-01680-f008" class="html-fig">Figure 8</a> but for the MITgcm-GoM model. Both the structures inside (<b>a</b>) and on the boundary (<b>b</b>) of the transformed volume are shown. The left, middle and right plots show the original MITgcm-GoM, the Dynloop, and the transformed flow fields.</p> Full article ">Figure 10
<p>Surface velocity time series evaluation metrics for the original (blue line) and transformed (red line) MITgcm-GoM in the (<b>a</b>) RMSE in the forward direction, (<b>b</b>) RMSE in the backward direction, (<b>c</b>) CC in the forward direction, and (<b>d</b>) CC in the backward direction. The shaded regions show the variance of the error field between the original and transformed MITgcm-GoM.</p> Full article ">Figure 11
<p>First Empirical Orthogonal Function (EOF) mode of the surface velocity fields over the first 120 days of the transformed field in the forward (<b>a</b>) and backward (<b>b</b>) direction. From left to right is the observed, the original model, and the transformed field.</p> Full article ">Figure 12
<p>Velocity magnitude snapshots of the long-term transformation in the forward direction. The first, second and third columns show the virtual observation field (HYCOM), the MITgcm-GoM original field, and the transformed MITgcm-GoM, respectively. The last two columns show the difference between HYCOM and the transformed fields and between HYCOM and the original MITgcm-GoM fields, respectively. The rows indicate time. Note that no observations have been provided since January 2012.</p> Full article ">Figure 13
<p>Same as <a href="#jmse-12-01680-f012" class="html-fig">Figure 12</a> but in the backward direction.</p> Full article ">
4 pages, 171 KiB  
Editorial
Advances in Offshore Aquaculture and Renewable Energy Production
by Huu Phu Nguyen and Chien Ming Wang
J. Mar. Sci. Eng. 2024, 12(9), 1679; https://doi.org/10.3390/jmse12091679 - 20 Sep 2024
Viewed by 744
Abstract
With the world population projected to reach 10 billion by 2050, the demand for food and energy is expected to increase significantly [...] Full article
(This article belongs to the Special Issue Advances in Offshore Aquaculture and Renewable Energy Production)
23 pages, 7572 KiB  
Article
The Influence of the Atlantic Water Boundary Current on the Phytoplankton Composition and Biomass in the Northern Barents Sea and the Adjacent Nansen Basin
by Larisa Pautova, Marina Kravchishina, Vladimir Silkin, Alexey Klyuvitkin, Anna Chultsova, Svetlana Vazyulya, Dmitry Glukhovets and Vladimir Artemyev
J. Mar. Sci. Eng. 2024, 12(9), 1678; https://doi.org/10.3390/jmse12091678 - 20 Sep 2024
Viewed by 566
Abstract
The modern Arctic is characterized by a decreased ice cover and significant interannual variability. However, the reaction of the High Arctic ecosystem to such changes is still being determined. This study tested the hypothesis that the key drivers of changes in phytoplankton are [...] Read more.
The modern Arctic is characterized by a decreased ice cover and significant interannual variability. However, the reaction of the High Arctic ecosystem to such changes is still being determined. This study tested the hypothesis that the key drivers of changes in phytoplankton are the position and intensity of Atlantic water (AW) flow. The research was conducted in August 2017 in the northern part of the Barents Sea and in August 2020 in the Nansen Basin. In 2017, the Nansen Basin was ice covered; in 2020, the Nansen Basin had open water up to 83° N. A comparative analysis of phytoplankton composition, dominant species, abundance, and biomass at the boundary of the ice and open water in the marginal ice zone (MIZ) as well as in the open water was carried out. The total biomass of the phytoplankton in the photic layer of MIZ is one and a half orders of magnitude greater than in open water. In 2017, the maximum abundance and biomass of phytoplankton in the MIZ were formed by cold-water diatoms Thalassiosira spp. (T. gravida, T. rotula, T. hyalina, T. nordenskioeldii), associated with first-year ice. They were confined to the northern shelf of the Barents Sea. The large diatom Porosira glacialis grew intensively in the MIZ of the Nansen Basin under the influence of Atlantic waters. A seasonal thermocline, above which the concentrations of silicon and nitrogen were close to zero, and deep maxima of phytoplankton abundance and biomass were recorded in the open water. Atlantic species—haptophyte Phaeocystis pouchettii and large diatom Eucampia groenlandica—formed these maxima. P. pouchettii were observed in the Nansen Basin in the Atlantic water (AW) flow (2020); E. groenlandica demonstrated a high biomass (4848 mg m−3, 179.5 mg C m−3) in the Franz Victoria trench (2017). Such high biomass of this species in the northern Barents Sea shelf has not been observed before. The variability of the phytoplankton composition and biomass in the Franz Victoria trench and in the Nansen Basin is related to the intensity of the AW, which comes from the Frame Strait as the Atlantic Water Boundary Current. Full article
(This article belongs to the Special Issue Marine Environmental Analysis and Monitoring: Recent Harmful Events and New Substances)
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<p>Map of sampling stations (black dots) in 2017 (<b>upper</b>) and 2020 (<b>lower</b>). Bathymetric map (GEBCO, <a href="https://www.gebco.net" target="_blank">https://www.gebco.net</a> accessed on 29 April 2021) of the Barents Sea with circulation scheme modified after [<a href="#B71-jmse-12-01678" class="html-bibr">71</a>]. Solid red lines—Atlantic-derived water currents in upper layers; dotted red lines—subsurface Atlantic-derived water currents; blue lines—Arctic-derived currents; dark green lines—coastal currents. Ice cover images are composites over 1–8 August 2017 provided by Norwegian Meteorological Institute, <a href="https://cryo.met.no" target="_blank">https://cryo.met.no</a> accessed on 29 April 2021.</p> Full article ">Figure 1 Cont.
<p>Map of sampling stations (black dots) in 2017 (<b>upper</b>) and 2020 (<b>lower</b>). Bathymetric map (GEBCO, <a href="https://www.gebco.net" target="_blank">https://www.gebco.net</a> accessed on 29 April 2021) of the Barents Sea with circulation scheme modified after [<a href="#B71-jmse-12-01678" class="html-bibr">71</a>]. Solid red lines—Atlantic-derived water currents in upper layers; dotted red lines—subsurface Atlantic-derived water currents; blue lines—Arctic-derived currents; dark green lines—coastal currents. Ice cover images are composites over 1–8 August 2017 provided by Norwegian Meteorological Institute, <a href="https://cryo.met.no" target="_blank">https://cryo.met.no</a> accessed on 29 April 2021.</p> Full article ">Figure 2
<p>Vertical profiles of salinity and temperature at the studied stations in 2017 (<b>upper</b>) and 2020 (<b>lower</b>). The stations are plotted according to the RV cruise track.</p> Full article ">Figure 2 Cont.
<p>Vertical profiles of salinity and temperature at the studied stations in 2017 (<b>upper</b>) and 2020 (<b>lower</b>). The stations are plotted according to the RV cruise track.</p> Full article ">Figure 3
<p>T–S diagrams for 2017 (<b>upper</b>) and 2020 (<b>lower</b>). To the right of salinity values, 34.7 (<b>upper</b>) and 38.4 (<b>lower</b>) are AW, and to the left is PSW. The gray isolines show density values.</p> Full article ">
23 pages, 10692 KiB  
Article
Intelligent Fault Diagnosis Method for Constant Pressure Variable Pump Based on Mel-MobileViT Lightweight Network
by Yonghui Zhao, Anqi Jiang, Wanlu Jiang, Xukang Yang, Xudong Xia and Xiaoyang Gu
J. Mar. Sci. Eng. 2024, 12(9), 1677; https://doi.org/10.3390/jmse12091677 - 19 Sep 2024
Viewed by 706
Abstract
The sound signals of hydraulic pumps contain abundant key information reflecting their internal mechanical states. In environments characterized by high temperatures or high-speed rotation, or where sensor deployment is challenging, acoustic sensors offer non-contact and flexible arrangement features. Therefore, this study aims to [...] Read more.
The sound signals of hydraulic pumps contain abundant key information reflecting their internal mechanical states. In environments characterized by high temperatures or high-speed rotation, or where sensor deployment is challenging, acoustic sensors offer non-contact and flexible arrangement features. Therefore, this study aims to develop an intelligent fault diagnosis method for hydraulic pumps based on acoustic signals. Initially, the Adaptive Chirp Mode Decomposition (ACMD) method is employed to remove environmental noise from the acoustic signals, enhancing the feature signals. Subsequently, the Mel spectrum is extracted as the acoustic fingerprint features of various fault states of the hydraulic pump, and these features are used to train the MobileViT network, achieving accurate identification of the different fault modes. The results indicate that the proposed Mel-MobileViT model effectively identifies and classifies various faults in constant pressure variable pumps, outperforming other models. This study not only provides an efficient and reliable intelligent method for the fault diagnosis of critical industrial equipment such as hydraulic pumps, but also offers new perspectives on the application of deep learning in acoustic pattern analysis. Full article
(This article belongs to the Section Ocean Engineering)
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<p>Convolutional neural network structure.</p> Full article ">Figure 2
<p>MobileViT network architecture.</p> Full article ">Figure 3
<p>The simulated signal includes complex time-varying signals and noise. <math display="inline"><semantics> <mrow> <mi>s</mi> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </semantics></math>: (<b>a</b>) time-domain waveform; (<b>b</b>) spectrum.</p> Full article ">Figure 4
<p>ACMD algorithm results (blue: true; red: ACMD): (<b>a</b>) time-domain waveform comparison; (<b>b</b>) frequency comparison.</p> Full article ">Figure 5
<p>The time-frequency distribution of the simulated signal: (<b>a</b>) CWT; (<b>b</b>) STFT; (<b>c</b>) ACMD.</p> Full article ">Figure 6
<p>The processing results of the proposed simulation signal by the EMD, VMD, and ACMD algorithms: (<b>a</b>) the spectrum of the original signal; (<b>b</b>) the spectrum of the ACMD modal components; (<b>c</b>) the spectrum of the reconstructed signal from EMD; (<b>d</b>) the spectrum of the reconstructed signal from VMD (K = 8); (<b>e</b>) the EMD component spectrum; and (<b>f</b>) the VMD component spectrum.</p> Full article ">Figure 6 Cont.
<p>The processing results of the proposed simulation signal by the EMD, VMD, and ACMD algorithms: (<b>a</b>) the spectrum of the original signal; (<b>b</b>) the spectrum of the ACMD modal components; (<b>c</b>) the spectrum of the reconstructed signal from EMD; (<b>d</b>) the spectrum of the reconstructed signal from VMD (K = 8); (<b>e</b>) the EMD component spectrum; and (<b>f</b>) the VMD component spectrum.</p> Full article ">Figure 7
<p>Fault diagnosis process.</p> Full article ">Figure 8
<p>The schematic diagram of the hydraulic system of the constant pressure variable pump fault simulation test bench.</p> Full article ">Figure 9
<p>Sensor installation position of constant pressure variable pump fault simulation test system.</p> Full article ">Figure 10
<p>Physical images of constant pressure variable pump components with faults: (<b>a</b>) slipper pad wear (normal, light, severe); (<b>b</b>) loose slipper (normal, light, severe); (<b>c</b>) plunger wear (normal, light, severe); (<b>d</b>) inner race bearing fault; (<b>e</b>) outer race bearing fault; (<b>f</b>) rolling element bearing fault.</p> Full article ">Figure 11
<p>Mel spectrogram sample construction process.</p> Full article ">Figure 12
<p>Acoustic signal of plunger failure: (<b>a</b>) fault signal time domain; (<b>b</b>) spectrogram.</p> Full article ">Figure 13
<p>Sound signals of plunger fault after ACMD processing: (<b>a</b>) reconstructed signal time domain; (<b>b</b>) spectrum of the reconstructed signal.</p> Full article ">Figure 14
<p>Mel spectrograms of hydraulic pump under various fault conditions: (<b>a</b>) normal; (<b>b</b>) slipper boots (light); (<b>c</b>) slipper boots (heavy); (<b>d</b>) loose boots (light); (<b>e</b>) loose boots (heavy); (<b>f</b>) plunger (light); (<b>g</b>) plunger (heavy); (<b>h</b>) bearing inner ring; (<b>i</b>) bearing outer ring; (<b>j</b>) rolling element.</p> Full article ">Figure 15
<p>Training loss and accuracy curves for Mel-MobileViT: (<b>a</b>) training loss; (<b>b</b>) training accuracy.</p> Full article ">Figure 16
<p>Confusion matrices for different configurations of MobileViT: (<b>a</b>) S; (<b>b</b>) XS; (<b>c</b>) XXS.</p> Full article ">Figure 16 Cont.
<p>Confusion matrices for different configurations of MobileViT: (<b>a</b>) S; (<b>b</b>) XS; (<b>c</b>) XXS.</p> Full article ">Figure 17
<p>Clustering effect of each layer of network: (<b>a</b>) Input data; (<b>b</b>) Layer 1; (<b>c</b>) Layer 5; (<b>d</b>) Layer 6.</p> Full article ">Figure 17 Cont.
<p>Clustering effect of each layer of network: (<b>a</b>) Input data; (<b>b</b>) Layer 1; (<b>c</b>) Layer 5; (<b>d</b>) Layer 6.</p> Full article ">Figure 18
<p>Confusion matrices: (<b>a</b>) MobileViT-XXS; (<b>b</b>) MobileNetV1; (<b>c</b>) MobileNetV2.</p> Full article ">
27 pages, 9299 KiB  
Article
Three-Dimensional Path Planning of UAVs for Offshore Rescue Based on a Modified Coati Optimization Algorithm
by Fahui Miao, Hangyu Li and Xiaojun Mei
J. Mar. Sci. Eng. 2024, 12(9), 1676; https://doi.org/10.3390/jmse12091676 - 19 Sep 2024
Viewed by 777
Abstract
Unmanned aerial vehicles (UAVs) provide efficient and flexible means for maritime emergency rescue, with path planning being a critical technology in this context. Most existing unmanned device research focuses on land-based path planning in two-dimensional planes, which fails to fully leverage the aerial [...] Read more.
Unmanned aerial vehicles (UAVs) provide efficient and flexible means for maritime emergency rescue, with path planning being a critical technology in this context. Most existing unmanned device research focuses on land-based path planning in two-dimensional planes, which fails to fully leverage the aerial advantages of UAVs and does not accurately describe offshore environments. Therefore, this paper establishes a three-dimensional offshore environmental model. The UAV’s path in this environment is achieved through a novel swarm intelligence algorithm, which is based on the coati optimization algorithm (COA). New strategies are introduced to address potential issues within the COA, thereby solving the problem of UAV path planning in complex offshore environments. The proposed OCLCOA introduces a dynamic opposition-based search to address the population separation problem in the COA and incorporates a covariance search strategy to enhance its exploitation capabilities. To simulate the actual environment as closely as possible, the environmental model established in this paper considers three environmental factors: offshore flight-restricted area, island terrain, and sea winds. A corresponding cost function is designed to evaluate the path length and path deflection and quantify the impact of these three environmental factors on the UAV. Experimental results verify that the proposed algorithm effectively solves the UAV path planning problem in offshore environments. Full article
(This article belongs to the Section Ocean Engineering)
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<p>Schematic diagram of the key point generation principle.</p> Full article ">Figure 2
<p>Risk level calculation schematic.</p> Full article ">Figure 3
<p>Correspondence map between the search space and question space.</p> Full article ">Figure 4
<p>The search direction found by covariance matrix learning.</p> Full article ">Figure 5
<p>The flow chart of the proposed OCLOA for path planning.</p> Full article ">Figure 6
<p>The path result of Case 1.</p> Full article ">Figure 7
<p>The top view of the path result of Case 1.</p> Full article ">Figure 8
<p>The iteration result of Case 1.</p> Full article ">Figure 9
<p>The path result of Case 2.</p> Full article ">Figure 10
<p>The top view of the path result of Case 2.</p> Full article ">Figure 11
<p>The iteration result of Case 2.</p> Full article ">Figure 12
<p>The path result of Case 3.</p> Full article ">Figure 13
<p>The top view of the path result of Case 3.</p> Full article ">Figure 14
<p>The iteration result of Case 3.</p> Full article ">Figure 15
<p>The path result of Case 4.</p> Full article ">Figure 16
<p>The top view of the path result of Case 4.</p> Full article ">Figure 17
<p>The iteration result of Case 4.</p> Full article ">
15 pages, 5496 KiB  
Article
A Study on the Impact of Vertical Grid Parameter Perturbations in the Regional Ocean Modeling System
by Lei Wang, Feng Zhang, Chongwei Zheng, Yaozhao Zhong, Tianxiu Lu, Shaoping Shang, Siyu Pu, Guodong Xia, Huafei Chen and Wei Leng
J. Mar. Sci. Eng. 2024, 12(9), 1675; https://doi.org/10.3390/jmse12091675 - 19 Sep 2024
Viewed by 509
Abstract
In this study, the Regional Ocean Modeling System (ROMS) is employed to construct a three-dimensional barotropic ocean model with a monodirectional upper boundary and homogeneous and steady wind covering the entire computation area. Eight perturbation experiments are designed to determine the vertical grid [...] Read more.
In this study, the Regional Ocean Modeling System (ROMS) is employed to construct a three-dimensional barotropic ocean model with a monodirectional upper boundary and homogeneous and steady wind covering the entire computation area. Eight perturbation experiments are designed to determine the vertical grid distribution difference with high resolution at the surface and bottom. Two types are considered in the model, including removing the Coriolis force (type 1) and employing a different Coriolis force (type 2). According to the experiments, the velocity of the current in type 1 yields uncertainty, and wind energy could penetrate the upper ocean and reach the abyss. The surface velocity in type 2 is fundamentally compatible with the empirical relationship constructed by Ekman, and the curved lines of the vertical distribution of horizontal currents nearly match. For type 1, the velocity is very strong from the sea surface to the bottom. When comparing type 1 and type 2 cases, the Coriolis force obstructs the wind energy transfer into the deep ocean. In addition, the European Centre for Medium-Range Weather Forecasts (ECMWF)’s global surface wind distribution indicates that the realistic ocean upper wind boundary is similar to the numerical experiment in the Pacific and Atlantic oceans, where the wind direction is along the latitude line at the equator. In order to make the experimental situation as close as possible to the real ocean, validation experiments are conducted in this study to consider the uncertainty in the current profile at the equator. The simulation results of type 1 differ significantly from the data obtained from the real ocean. This uncertainty may transfer the signal to higher latitudes, causing incorrect simulation results, especially in the critical region. Overall, this research not only makes discoveries in physical ocean theory but also guides predictive and forecasting techniques for ocean modeling. Full article
(This article belongs to the Special Issue Advances in the Safety and Security of Intelligent Ships and Offshore Structures)
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<p>The flowchart of the experimental setup.</p> Full article ">Figure 2
<p>A vertical grid corresponding to the eight experiments (E1–E8) for type 1. (<b>a</b>–<b>d</b>) The scheme of Vtransform = 1 and Vstretching = 1. (<b>e</b>–<b>h</b>) The scheme of Vtransform = 2 and Vstretching = 4. The values of theta_s and theta_b are selected according to the corresponding numbers in <a href="#jmse-12-01675-t001" class="html-table">Table 1</a>.</p> Full article ">Figure 3
<p>(<b>a</b>) The current velocity distribution along the X-direction and water depth. The color represents the absolute value of the velocity; (<b>b</b>) the velocity versus time; (<b>c</b>) the spatial distribution of the current velocity, well-distributed horizontally; (<b>d</b>) the depth versus the current velocity for the eight experiments.</p> Full article ">Figure 4
<p>(<b>a</b>) The velocity spiral of the upper 2000 m. The dotted green line indicates the projection. (<b>b</b>) Four sections of the spatial distribution for the upper 400 m.</p> Full article ">Figure 5
<p>A comparison of the vertical lines of current velocities for different vertical grid schemes (5° N, 18° N, and 60° N). The almost overlapping dotted line, solid line, and dot–dashed line are chosen for illustration. (<b>a</b>) A comparison of the streamflow at 5°N. (<b>b</b>) The streamflow at 18°N. (<b>c</b>) The streamflow at 60°N.</p> Full article ">Figure 6
<p>The ECWMF 2009–2018 ten-year seasonal mean of the sea surface wind derived from monthly average data. The summer average is for months 6 to 8. The color represents the wind speed magnitude.</p> Full article ">Figure 7
<p>(<b>a</b>) The surface current field in the model after the steady state; the color represents the velocity magnitude. (<b>b</b>) The vertical distribution of the horizontal current velocity at the equator, which is homogeneous along the latitude line. The station point is chosen at (180, 0).</p> Full article ">Figure 8
<p>The three-dimensional flow field structure of experiment a1 for seven layers, including 5 m, 20 m, 50 m, 100 m, 500 m, 1000 m, and 1500 m. The color represents the current velocity magnitude, while the arrow indicates the direction.</p> Full article ">Figure 9
<p>The four experimental results contrast the vertical distribution of the equator’s horizontal velocity for the upper boundary’s wind speeds of 30 m/s and 10 m/s in (<b>a</b>) and (<b>b</b>), respectively.</p> Full article ">
19 pages, 8441 KiB  
Article
Extreme Wave-Induced Pressure Distribution and Wave Forces on Tandem Pile Groups: An Experimental Study
by Wanshui Han, Xi Yu, Jiajia Wang, Xin Xu and Xiaokun Chen
J. Mar. Sci. Eng. 2024, 12(9), 1674; https://doi.org/10.3390/jmse12091674 - 19 Sep 2024
Viewed by 500
Abstract
As the foundation of marine infrastructure, pile groups are subjected to extreme wave loads. Existing research primarily focuses on regular waves and wave forces. There is limited research on the pressure distribution of pile bodies under extreme waves. This paper describes a wave [...] Read more.
As the foundation of marine infrastructure, pile groups are subjected to extreme wave loads. Existing research primarily focuses on regular waves and wave forces. There is limited research on the pressure distribution of pile bodies under extreme waves. This paper describes a wave flume experiment where waves of a self-proposed extreme wave type were generated. The experiment considers three water depths (25/35/45 cm), three wave-pushing velocities (20/30/40 cm/s), and two clear distances (D, 2D). A total of 216 measuring points equipped with digital pressure sensors captured the vertical and circumferential pressure distribution and wave positive force. The results show that (1) the vertical and circumferential pressure distribution patterns of each component pile and the single pile are similar in various loading scenarios and clear distances. (2) The measuring point pressure, pressure after circumferential integration, and wave positive force are positively correlated with wave-pushing velocity. (3) The wave pressure is positively correlated with the water depth, while the pressure after circumferential integration is negatively correlated with the water depth. (4) When the clear distance is D, the wave positive force coefficient of each component pile is less than 1.0. Full article
(This article belongs to the Section Coastal Engineering)
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<p>Experimental flume arrangement.</p> Full article ">Figure 2
<p>Schematic diagram of wave flume.</p> Full article ">Figure 3
<p>Model and pressure measuring point arrangement: (<b>a</b>) model arrangement and (<b>b</b>) measuring point arrangement and model dimensions.</p> Full article ">Figure 4
<p>Three motion processes of the wave-pushing plate.</p> Full article ">Figure 5
<p>Repeatability verification of experimental data. (<b>a</b>) Water surface elevation repeatability verification. (<b>b</b>) Wave pressure repeatability verification.</p> Full article ">Figure 6
<p>Dimensions of the numerical wave flume. (<b>a</b>) Initial flume. (<b>b</b>) Extended flume.</p> Full article ">Figure 7
<p>ANSYS Fluent simulation result validation and comparison. (<b>a</b>) Water surface elevation comparison of ANSYS Fluent and experiment. (<b>b</b>) Water surface elevation comparison of the initial flume and the extended flume.</p> Full article ">Figure 8
<p>Time-varying curves of pressure at the 0° position of P1, P2, P3, and PS under the “45-40” loading scenario. (<b>a</b>) P1; (<b>b</b>) P2; (<b>c</b>) P3; and (<b>d</b>) PS.</p> Full article ">Figure 9
<p>Pressure distribution of the vertical measuring points at the 0° position under various loading scenarios. (<b>a</b>) <span class="html-italic">h</span> = 45 cm and <span class="html-italic">V</span><sub>uni</sub> = 40/30/20 cm/s. (<b>b</b>) <span class="html-italic">h</span> = 45/35/25 cm and <span class="html-italic">V</span><sub>uni</sub> = 40 cm/s.</p> Full article ">Figure 10
<p>Vertical pressure distribution at each circumferential position of P1, P2, P3, and PS under “45-40” (<b>a</b>) 0°; (<b>b</b>) 30°; (<b>c</b>) 60°; (<b>d</b>) 120°; (<b>e</b>) 150°; and (<b>f</b>) 180°.</p> Full article ">Figure 11
<p>Vertical pressure distribution after circumferential integration of P1, P2, P3, and PS under various loading scenarios. (<b>a</b>) P1; (<b>b</b>) P2; (<b>c</b>) P3; and (<b>d</b>) PS.</p> Full article ">Figure 12
<p>Vertical pressure distribution after the circumferential integration of different clear distances under the “45-40” scenario. (<b>a</b>) Clear distance = D. (<b>b</b>) Clear distance = 2D.</p> Full article ">Figure 13
<p>Pressure distribution of circumferential measuring points at the 4# and 9# heights under various loading scenarios. (<b>a</b>) h = 45/35/25 cm and V<sub>uni</sub> = 40 cm/s. (<b>b</b>) h = 45 cm and V<sub>uni</sub> = 40/30/20 cm/s.</p> Full article ">Figure 14
<p>Pressure distribution of circumferential measuring points at the 9# height under the “45-40” loading scenario. (<b>a</b>) Comparison between P1 and PS. (<b>b</b>) Comparison between P2 and PS. (<b>c</b>) Comparison between P3 and PS.</p> Full article ">Figure 15
<p>Time-varying curves of the wave positive force (<span class="html-italic">F<sub>x</sub></span>) under the “45-40” loading scenario.</p> Full article ">Figure 16
<p>The <span class="html-italic">F<sub>xmax</sub></span> of P1, P2, and P3 under different loading scenarios and clear distances. (<b>a</b>) P1; (<b>b</b>) P2; and (<b>c</b>) P3.</p> Full article ">Figure 17
<p>The <span class="html-italic">C<sub>f</sub></span> of P1, P2, and P3 under different clear distances and loading scenarios. (<b>a</b>) Clear distance = D. (<b>b</b>) Clear distance = 2D.</p> Full article ">Figure 18
<p>Comparison of the circumferential and vertical pressure distributions. (<b>a</b>) Comparison of vertical pressure distribution. (<b>b</b>) Comparison of circumferential pressure distribution.</p> Full article ">
18 pages, 8849 KiB  
Article
Research on Model Reduction of AUV Underwater Support Platform Based on Digital Twin
by Daohua Lu, Yichen Ning, Jia Wang, Kaijie Du and Cancan Song
J. Mar. Sci. Eng. 2024, 12(9), 1673; https://doi.org/10.3390/jmse12091673 - 19 Sep 2024
Viewed by 610
Abstract
Digital twin technology, as a data-driven and model-driven innovation means, plays a crucial role in the process of digital transformation and intelligent upgrading of the marine industry, helping the industry to move towards a new stage of more intelligent and efficient development. In [...] Read more.
Digital twin technology, as a data-driven and model-driven innovation means, plays a crucial role in the process of digital transformation and intelligent upgrading of the marine industry, helping the industry to move towards a new stage of more intelligent and efficient development. In order to solve the defects of the Autonomous Underwater Vehicle (AUV) underwater support platform structure deformation field, digital twin technology and model reduction technology are applied to an AUV underwater support platform, and a five-dimensional digital twin model of the AUV underwater support platform is studied, including five dimensions: physical world, digital world, twin data center, service application, and data connection. The digital twin of the subsea support platform is established by using the digital twin modeling technology. The POD method is used to calculate the deformation field matrix of the support structure of the subsea support platform under the 0–5 sea state, and the corresponding eigenvalues and eigenvectors are obtained. By intercepting the eigenvectors corresponding to the eigenvalues of the high energy proportion, the low-order equation is constructed, and the reduced-order model under each sea state can be quickly solved. The experimental results show that the model reduction technology can greatly shorten the model solving time, and the calculated results are highly consistent with the simulation results of the finite element full-order model, which can realize the rapid analysis of the deformation response of the subsea support platform structure, and provide a theoretical basis and technical support for the subsequent simulation, state evaluation, visual monitoring, and predictive maintenance. Full article
(This article belongs to the Special Issue Theories and Techniques in Intelligent Digital Twins in Marine Science and Engineering)
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<p>Digital twin architecture of subsea support platform.</p> Full article ">Figure 2
<p>Digital twin construction process of subsea support platform.</p> Full article ">Figure 3
<p>Subsea support platform digital twin.</p> Full article ">Figure 4
<p>Singular value decomposition principle.</p> Full article ">Figure 5
<p>Space–time relationship of singular value decomposition.</p> Full article ">Figure 6
<p>Data fusion flow chart.</p> Full article ">Figure 7
<p>Flow chart of the AUV subsea support platform model reduced-order.</p> Full article ">Figure 8
<p>Support structure 3D model.</p> Full article ">Figure 9
<p>Stress change curve of sling. (<b>a</b>) The force diagram of point C in the x, y and z directions respectively. (<b>b</b>) The force diagram of point E in the x, y and z directions respectively. (<b>c</b>) The force diagram of point D in the x, y and z directions respectively. (<b>d</b>) The force diagram of point G in the x, y and z directions respectively.</p> Full article ">Figure 10
<p>C, E, D, and G are the forces of the sling changing over time, and A, B, H, and F are the vertical downward forces, respectively.</p> Full article ">Figure 11
<p>Eigenvalue distribution.</p> Full article ">Figure 12
<p>Trend of eigenvalue proportion of displacement field.</p> Full article ">Figure 13
<p>Cloud image of deformation of Class 5 sea state support structure.</p> Full article ">
25 pages, 35656 KiB  
Article
Development and Application of an Advanced Automatic Identification System (AIS)-Based Ship Trajectory Extraction Framework for Maritime Traffic Analysis
by I-Lun Huang, Man-Chun Lee, Li Chang and Juan-Chen Huang
J. Mar. Sci. Eng. 2024, 12(9), 1672; https://doi.org/10.3390/jmse12091672 - 18 Sep 2024
Viewed by 952
Abstract
This study addresses the challenges of maritime traffic management in the western waters of Taiwan, a region characterized by substantial commercial shipping activity and ongoing environmental development. Using 2023 Automatic Identification System (AIS) data, this study develops a robust feature extraction framework involving [...] Read more.
This study addresses the challenges of maritime traffic management in the western waters of Taiwan, a region characterized by substantial commercial shipping activity and ongoing environmental development. Using 2023 Automatic Identification System (AIS) data, this study develops a robust feature extraction framework involving data cleaning, anomaly trajectory point detection, trajectory compression, and advanced processing techniques. Dynamic Time Warping (DTW) and the Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) algorithms are applied to cluster the trajectory data, revealing 16 distinct maritime traffic patterns, key navigation routes, and intersections. The findings provide fresh perspectives on analyzing maritime traffic, identifying high-risk areas, and informing safety and spatial planning. In practical applications, the results help navigators optimize route planning, improve resource allocation for maritime authorities, and inform the development of infrastructure and navigational aids. Furthermore, these outcomes are essential for detecting abnormal ship behavior, and they highlight the potential of route extraction in maritime surveillance. Full article
(This article belongs to the Section Ocean Engineering)
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<p>Methodological framework of research.</p> Full article ">Figure 2
<p>Principle diagram of DP algorithm; (<b>a</b>) Original trajectory. (<b>b</b>) Baseline construction and distance calculation. (<b>c</b>) Trajectory segmentation at farthest points. (<b>d</b>) Segmentation progression. (<b>e</b>) Incomplete segment handling. (<b>f</b>) Final simplified trajectory.</p> Full article ">Figure 3
<p>Container ship traffic density in the western waters of Taiwan, 2023.</p> Full article ">Figure 4
<p>Parameter analysis for anomaly trajectory point detection and trajectory compression; (<b>a</b>) Relationship between parameter α and the number of anomaly data points. (<b>b</b>) Distribution of ship length and width. (<b>c</b>) Relationship between <span class="html-italic">ε</span> and average reduced distance. (<b>d</b>) Relationship between <span class="html-italic">ε</span> and average reduced point.</p> Full article ">Figure 5
<p>Impact of <span class="html-italic">min_samples</span> on the number of clusters and non-clustered trajectories.</p> Full article ">Figure 6
<p>Impact of <span class="html-italic">min_samples</span> on Silhouette Coefficient (SC) and Davies–Bouldin Index (DBI).</p> Full article ">Figure 7
<p>Clustering analysis results for <span class="html-italic">min_samples</span> = 4 (26 clusters and 1 non-clustered trajectory data).</p> Full article ">Figure 8
<p>Clustering analysis results for <span class="html-italic">min_samples</span> = 17 (16 clusters and 1 non-clustered trajectory data).</p> Full article ">Figure 9
<p>Clustering analysis results for <span class="html-italic">min_samples</span> = 50 (10 clusters and 1 non-clustered trajectory data).</p> Full article ">Figure 10
<p>Route extraction of container ships in the western waters of Taiwan in 2023.</p> Full article ">
23 pages, 3030 KiB  
Article
Research on Precise Feeding Strategies for Large-Scale Marine Aquafarms
by Yizhi Wang, Yusen Zhang, Fengyuan Ma, Xiaomin Tian, Shanshan Ge, Chaoyuan Man and Maohua Xiao
J. Mar. Sci. Eng. 2024, 12(9), 1671; https://doi.org/10.3390/jmse12091671 - 18 Sep 2024
Viewed by 537
Abstract
Breeding in large-scale marine aquafarms faces many challenges in terms of precise feeding, including real-time decisions as to the precise feeding amount, along with disturbances caused by the feeding speed and the moving speed of feeding equipment. Involving many spatiotemporal distributed parameters and [...] Read more.
Breeding in large-scale marine aquafarms faces many challenges in terms of precise feeding, including real-time decisions as to the precise feeding amount, along with disturbances caused by the feeding speed and the moving speed of feeding equipment. Involving many spatiotemporal distributed parameters and variables, an effective predictive model for environment and growth stage perception is yet to obtained, further preventing the development of precise feeding strategies and feeding equipment. Therefore, in this paper, a hierarchical type-2 fuzzy system based on a quasi-Gaussian membership function for fast, precise, on-site feeding decisions is proposed and validated. The designed system consists of two layers of decision subsystems, taking in different sources of data and expert experience in feeding but avoiding the rule explosion issue. Meanwhile, the water quality evaluation is considered as the secondary membership function for type-2 fuzzy sets and used to adjust the parameters of the quasi-Gaussian membership function, decreasing the calculation load in type reduction. The proposed system is validated, and the results indicate that the shape of the primary fuzzy sets is altered with the secondary membership, which influences the defuzzification results accordingly. Meanwhile, the hardware of feeding bins for UAVs with variable-speed coupling control systems with disturbance compensation is improved and validated. The results indicate that the feeding speed can follow the disturbance in the level flying speed. Full article
(This article belongs to the Special Issue Advances in the Safety and Security of Intelligent Ships and Offshore Structures)
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<p>General structure of a hybrid HFS.</p> Full article ">Figure 2
<p>Type−2 fuzzy subset <math display="inline"><semantics> <mrow> <mover accent="true"> <mrow> <msub> <mi>X</mi> <mi>i</mi> </msub> </mrow> <mo stretchy="true">˜</mo> </mover> </mrow> </semantics></math>.</p> Full article ">Figure 3
<p>Precise feeding decision−making methodology.</p> Full article ">Figure 4
<p>Block diagram of the variable−speed coupling control system based on compensation.</p> Full article ">Figure 5
<p>Fuzzification results: (<b>a</b>) breeding quantity; (<b>b</b>) feeding correction; (<b>c</b>) final feeding amount <math display="inline"><semantics> <mrow> <msubsup> <mi>W</mi> <mi>m</mi> <mo>′</mo> </msubsup> </mrow> </semantics></math>.</p> Full article ">Figure 6
<p>Feeding amount fuzzy sets before and after type increase: (<b>a</b>) <math display="inline"><semantics> <mrow> <mover accent="true"> <mrow> <msub> <mi>W</mi> <mi>m</mi> </msub> </mrow> <mo stretchy="true">˜</mo> </mover> </mrow> </semantics></math> before type increase; (<b>b</b>) <math display="inline"><semantics> <mrow> <mover accent="true"> <mrow> <msub> <mi>W</mi> <mi>m</mi> </msub> </mrow> <mo stretchy="true">˜</mo> </mover> </mrow> </semantics></math> after type increase.</p> Full article ">Figure 7
<p>Inference results of <math display="inline"><semantics> <mrow> <msub> <msup> <mi>w</mi> <mo>′</mo> </msup> <mi>m</mi> </msub> </mrow> </semantics></math>: (<b>a</b>) fuzzy inference output; (<b>b</b>) fuzzy inference result.</p> Full article ">Figure 8
<p>Simulation of the variable-speed coupling feeding control system based on compensation.</p> Full article ">Figure 9
<p>Disturbance signals and variable-speed compensation feeding control of the UAV: (<b>a</b>) periodic disturbance; (<b>b</b>) random disturbance.</p> Full article ">Figure 10
<p>Feeding speed under disturbance: (<b>a</b>) periodic disturbance; (<b>b</b>) random disturbance (with sampling interval of 5 s).</p> Full article ">Figure 11
<p>Tracking of residual feeding amount: (<b>a</b>) periodic disturbance; (<b>b</b>) random disturbance.</p> Full article ">Figure 12
<p>Tracking of residual feed amount (T<sub>k</sub> = 1 s): (<b>a</b>) periodic disturbance; (<b>b</b>) random disturbance.</p> Full article ">
45 pages, 3879 KiB  
Article
The Hydrodynamic Similarity between Different Power Levels and a Dynamic Analysis of Ocean Current Energy Converter–Platform Systems with a Novel Pulley–Traction Rope Design for Irregular Typhoon Waves and Currents
by Shueei-Muh Lin, Wen-Rong Wang and Hsin Yuan
J. Mar. Sci. Eng. 2024, 12(9), 1670; https://doi.org/10.3390/jmse12091670 - 18 Sep 2024
Viewed by 510
Abstract
In the future, the power of a commercial ocean current energy convertor will be able to reach the MW class, and its corresponding mooring rope tension will be very good. However, the power of convertors currently being researched is still at the KW [...] Read more.
In the future, the power of a commercial ocean current energy convertor will be able to reach the MW class, and its corresponding mooring rope tension will be very good. However, the power of convertors currently being researched is still at the KW class, which can bear less rope tension. The main mooring rope usually has a single cable and a single foundation. To investigate the dynamic response and rope tension of an MW-class ocean current generator mooring system, here, a similarity rule is proposed for (1) coefficients without any fluid–structure interaction (FSI) using the Buckingham theorem and (2) ones with FSI. The overall hydrodynamic drag and moment including the hydrodynamic coefficients in these two situations are represented in a Taylor series. Assuming similarity between the commercial MW-class and KW-class ocean current convertors, all hydrodynamic parameters of the MW-class system are estimated based on the known KW-class parameters and based on the similarity formula. In order to overcome the extreme tension of the MW-class system and to provide good stability, in this paper, we propose a pulley–rope design to replace the traditional single-traction-rope design. The static and dynamic mathematical models of this mooring system subjected to the impact of typhoon waves and currents are proposed, and analytical solutions are obtained. We find that the pulley–rope design can significantly reduce the dynamic rope tensions of the mooring system. The effect of the length ratio of the main traction rope, rope A, to the seabed depth on the dynamic tension of stabilizing converter rope D is significant. The length ratio is within a safe range, and the maximum rope dynamic tension is less than the fracture strength. In addition, if the rope length ratio is over the critical value, the larger the ratio, the higher the safety factor of the rope. In summary, the pulley–rope design can be safely used in an MW-level ocean current generator system. Full article
(This article belongs to the Section Ocean Engineering)
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<p>Configuration of the mooring system for an ocean energy convertor.</p> Full article ">Figure 2
<p>Top view of the mooring system of an ocean energy converter.</p> Full article ">Figure 3
<p>Relation between the directions of the platform and current.</p> Full article ">Figure 4
<p>Effect of current direction on the tension of rope <italic>A</italic>.</p> Full article ">Figure 5
<p>Top view of the mooring system under the influence of a wave and a current.</p> Full article ">Figure 6
<p>The relation between the significant height <italic>H<sub>s</sub></italic> and the amplitudes of six regular simulating waves <italic>a<sub>i</sub></italic>.</p> Full article ">Figure 7
<p>Effects of the length of rope <italic>A</italic>, <italic>L<sub>A</sub></italic>, and current direction <inline-formula><mml:math id="mm337"><mml:semantics><mml:mrow><mml:msub><mml:mi>φ</mml:mi><mml:mrow><mml:mi>c</mml:mi><mml:mi>u</mml:mi><mml:mi>r</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:semantics></mml:math></inline-formula> on the maximum rope tensions of the 400 kW convertor.</p> Full article ">Figure 8
<p>Effects of the distance between the two foundations <italic>L<sub>F</sub></italic> and current direction <inline-formula><mml:math id="mm338"><mml:semantics><mml:mrow><mml:msub><mml:mi>φ</mml:mi><mml:mrow><mml:mi>c</mml:mi><mml:mi>u</mml:mi><mml:mi>r</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:semantics></mml:math></inline-formula> on the maximum rope tensions and the displacements of the elements.</p> Full article ">Figure 9
<p>Comparison of the dynamic tensions of two mooring systems with a single rope <italic>A</italic> and a pulley–rope design.</p> Full article ">Figure 10
<p>Effects of the length of rope <italic>A</italic>, <italic>L<sub>A</sub></italic>, and current direction <inline-formula><mml:math id="mm339"><mml:semantics><mml:mrow><mml:msub><mml:mi>φ</mml:mi><mml:mrow><mml:mi>c</mml:mi><mml:mi>u</mml:mi><mml:mi>r</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:semantics></mml:math></inline-formula> on the maximum rope tensions of the 1 MW convertor.</p> Full article ">Figure 11
<p>Effect of the buffer spring for the 1 MW convertor.</p> Full article ">Figure 12
<p>Effect of the buffer spring for the 700 kW convertor.</p> Full article ">Figure A1
<p>Top view of the mooring system.</p> Full article ">
21 pages, 3418 KiB  
Article
Performance of a Cable-Driven Robot Used for Cyber–Physical Testing of Floating Wind Turbines
by Yngve Jenssen, Thomas Sauder and Maxime Thys
J. Mar. Sci. Eng. 2024, 12(9), 1669; https://doi.org/10.3390/jmse12091669 - 18 Sep 2024
Viewed by 725
Abstract
Cyber–physical testing has been applied for a decade in hydrodynamic laboratories to assess the dynamic performance of floating wind turbines (FWTs) in realistic wind and wave conditions. Aerodynamic loads, computed by a numerical simulator fed with model test measurements, are applied in real [...] Read more.
Cyber–physical testing has been applied for a decade in hydrodynamic laboratories to assess the dynamic performance of floating wind turbines (FWTs) in realistic wind and wave conditions. Aerodynamic loads, computed by a numerical simulator fed with model test measurements, are applied in real time on the physical model using actuators. The present paper proposes a set of short and targeted benchmark tests that aim to quantify the performance of actuators used in cyber–physical FWT testing. They aim at ensuring good load tracking over all frequencies of interest and satisfactory disturbance rejection for large motions to provide a realistic test setup. These benchmark tests are exemplified on two radically different 15 MW FWT models tested at SINTEF Ocean using a cable-driven robot. Full article
(This article belongs to the Special Issue Modelling Techniques for Floating Offshore Wind Turbines)
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<p>Generic control loop of cyber–physical testing.</p> Full article ">Figure 2
<p>Figure shows n <math display="inline"><semantics> <mrow> <mo>(</mo> <mi>O</mi> <mo>,</mo> <msub> <mi>n</mi> <mn>1</mn> </msub> <mo>,</mo> <msub> <mi>n</mi> <mn>2</mn> </msub> <mo>,</mo> <msub> <mi>n</mi> <mn>3</mn> </msub> <mo>)</mo> </mrow> </semantics></math> and b <math display="inline"><semantics> <mrow> <mo>(</mo> <mi>B</mi> <mo>,</mo> <msub> <mi>b</mi> <mn>1</mn> </msub> <mo>,</mo> <msub> <mi>b</mi> <mn>2</mn> </msub> <mo>,</mo> <msub> <mi>b</mi> <mn>3</mn> </msub> <mo>)</mo> </mrow> </semantics></math> coordinate systems relative to each other.</p> Full article ">Figure 3
<p>The power spectrum of the load applied: (<b>a</b>) FWT1 and (<b>b</b>) FWT2—low-frequency range. From top to bottom: surge, sway, roll, pitch, and yaw.</p> Full article ">Figure 4
<p>The power spectrum of the load—wave-frequency range: (<b>a</b>) FWT1; (<b>b</b>) FWT2. From top to bottom: surge, sway, roll, pitch, and yaw. Note that the scale on the <span class="html-italic">y</span>-axis is not the same across the plots.</p> Full article ">Figure 5
<p>The power spectrum of the load—<math display="inline"><semantics> <mrow> <mn>3</mn> <mi>p</mi> </mrow> </semantics></math>-frequency range: (<b>a</b>) FWT1; (<b>b</b>) FWT2. From top to bottom: surge, sway, roll, pitch, and yaw.</p> Full article ">Figure 6
<p>The power spectrum of the load—<math display="inline"><semantics> <mrow> <mn>6</mn> <mi>p</mi> </mrow> </semantics></math>-frequency range: (<b>a</b>) FWT1; (<b>b</b>) FWT2. From top to bottom: surge, sway, roll, pitch, and yaw.</p> Full article ">Figure 7
<p>The Bode diagrams of the estimated transfer function between the commanded and measured force, (<b>a</b>) FWT1 and (<b>b</b>) FWT2, for each degree of freedom. The solid line is estimated from the benchmark chip test, and the dots represent the estimation during a wave and wind test under operating conditions. The latter is not displayed where the desired load was insignificant (as the ratio between measured and commanded would be singular).</p> Full article ">Figure 8
<p>Overview of the requirements and benchmark tests.</p> Full article ">Figure 9
<p>Chirp time series that consist of applying a constant amplitude load, centred on zero. First in surge, and then in sway at the tower top, at an increasing frequency.</p> Full article ">Figure 10
<p>The FWT2, pitch decay test: (<b>a</b>) with the CDRP connected; (<b>b</b>) with the CDPR disconnected. The top plots show the pitch time series. The red crosses are the measured peaks, and the red stars are the fitted peaks at opposing peak locations.</p> Full article ">Figure 11
<p>The motion power spectra (<b>a</b>) and time series (<b>b</b>) from tests with and without the CDPR connected—FWT1. Top: surge, mid: pitch, and bottom: yaw.</p> Full article ">Figure 12
<p>The mooring line tension (<b>a</b>) spectra and (<b>b</b>) time series from the tests with and without the CDPR connected—FWT1.</p> Full article ">Figure 13
<p>The tower base fore–aft bending moment: (<b>a</b>) the power spectrum; (<b>b</b>) the time series from the tests with and without the CDPR connected—FWT1.</p> Full article ">Figure 14
<p>A close-up view of the fore–aft bending moment time series during a slamming event triggering tower vibrations—FWT1.</p> Full article ">Figure 15
<p>Top: nacelle acceleration power spectrum (<b>a</b>) and time series (<b>b</b>); close-up of the acceleration spectrum for various frequency ranges: (<b>c</b>) LF, (<b>d</b>) WF, and (<b>e</b>) 3<span class="html-italic">p</span> and (<b>f</b>) 6<span class="html-italic">p</span>.</p> Full article ">Figure A1
<p>The Bode diagrams of the average RAO between the commanded and measured <span class="html-italic">tensions</span> in the cables: (<b>a</b>) FWT1 and (<b>b</b>) FWT2. The grey background illustrates ±2 standard deviations to the average. Amplitude is denoted <span class="html-italic">T</span> and phase denoted <math display="inline"><semantics> <mi>ϕ</mi> </semantics></math>.</p> Full article ">
26 pages, 4805 KiB  
Article
Dynamic Response Analysis and Liquefaction Potential Evaluation of Riverbed Induced by Tidal Bore
by Dongzi Pan and Ying Li
J. Mar. Sci. Eng. 2024, 12(9), 1668; https://doi.org/10.3390/jmse12091668 - 18 Sep 2024
Viewed by 474
Abstract
Tidal bores, defined by sudden upstream surges of tidal water in estuaries, exert significant hydrodynamic forces on riverbeds, leading to complex sedimentary responses. This study examines the dynamic response and liquefaction potential of riverbeds subjected to tidal bores in macro-tidal estuaries. An analytical [...] Read more.
Tidal bores, defined by sudden upstream surges of tidal water in estuaries, exert significant hydrodynamic forces on riverbeds, leading to complex sedimentary responses. This study examines the dynamic response and liquefaction potential of riverbeds subjected to tidal bores in macro-tidal estuaries. An analytical model, developed using the generalized Biot theory and integral transform methods, evaluates the dynamic behavior of riverbed sediments. Key factors such as permeability, saturation, and sediment properties are analyzed for their influence on momentary liquefaction. The results indicate that fine sand reduces liquefaction risk by facilitating pore water discharge, while silt soil increases sediment instability. Additionally, the study reveals that pressure gradients induced by tidal bores can trigger momentary liquefaction, with the maximum liquefaction depth predicted based on horizontal pressure gradients being five times that predicted based on vertical pressure gradients. This research highlights the critical role of sediment characteristics in riverbed stability, providing a comprehensive understanding of the interactions between tidal bores and riverbed dynamics. The findings contribute to the development of predictive models and guidelines for managing the risks of tidal bore-induced liquefaction in coastal and estuarine environments. Full article
(This article belongs to the Special Issue Wave/Current–Structure–Seabed Interactions around Offshore Foundations)
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<p>Distribution of typical tidal bores around the world.</p> Full article ">Figure 2
<p>Schematic diagram of the interaction between a tidal bore and the riverbed. Inset: the Qiantang River tidal bore at Yanguan, China; <span class="html-italic">c</span> represents the celerity of the tidal bore, <span class="html-italic">d</span> is the water depth before the bore arrives, <span class="html-italic">h</span> is the thickness of the riverbed, <span class="html-italic">H</span> is the height of the tidal bore, <span class="html-italic">σ<sub>x</sub></span> and <span class="html-italic">σ<sub>z</sub></span> correspond to the normal stresses in the <span class="html-italic">x</span> and <span class="html-italic">z</span> directions, respectively, <span class="html-italic">τ<sub>xz</sub></span> is the shear stress, and <span class="html-italic">SWL</span> indicates the still water line.</p> Full article ">Figure 3
<p>Comparison between the degenerate solution and the existing analytical solution [<a href="#B57-jmse-12-01668" class="html-bibr">57</a>]. (<b>a</b>) Dynamic water pressure, (<b>b</b>) horizontal seepage velocity, and (<b>c</b>) vertical seepage velocity.</p> Full article ">Figure 4
<p>Contours of tidal bore induced pore pressure within the riverbed. (<b>a</b>) <span class="html-italic">S<sub>r</sub></span> = 1 and (<b>b</b>) <span class="html-italic">S<sub>r</sub></span> = 0.99, <span class="html-italic">S<sub>r</sub></span> is the degree of saturation.</p> Full article ">Figure 5
<p>Contours of tidal bore-induced effective stresses within the riverbed. Vertical effective stress: (<b>a</b>) <span class="html-italic">S<sub>r</sub></span> = 1 and (<b>b</b>) <span class="html-italic">S<sub>r</sub></span> = 0.99; horizontal effective stress: (<b>c</b>) <span class="html-italic">S<sub>r</sub></span> = 1 and (<b>d</b>) <span class="html-italic">S<sub>r</sub></span> = 0.99; and shear stress: (<b>e</b>) <span class="html-italic">S<sub>r</sub></span> = 1 and (<b>f</b>) <span class="html-italic">S<sub>r</sub></span> = 0.99. <span class="html-italic">S<sub>r</sub></span> is the degree of saturation.</p> Full article ">Figure 5 Cont.
<p>Contours of tidal bore-induced effective stresses within the riverbed. Vertical effective stress: (<b>a</b>) <span class="html-italic">S<sub>r</sub></span> = 1 and (<b>b</b>) <span class="html-italic">S<sub>r</sub></span> = 0.99; horizontal effective stress: (<b>c</b>) <span class="html-italic">S<sub>r</sub></span> = 1 and (<b>d</b>) <span class="html-italic">S<sub>r</sub></span> = 0.99; and shear stress: (<b>e</b>) <span class="html-italic">S<sub>r</sub></span> = 1 and (<b>f</b>) <span class="html-italic">S<sub>r</sub></span> = 0.99. <span class="html-italic">S<sub>r</sub></span> is the degree of saturation.</p> Full article ">Figure 6
<p>Time histories of the horizontal and vertical seepage velocities induced by the tidal bore at three different elevations (0, − 0.25 <span class="html-italic">h</span>, and − 0.5 <span class="html-italic">h</span>). (<b>a</b>) Dynamic water pressure at the interface of water and riverbed, (<b>b</b>) horizontal seepage velocity, and (<b>c</b>) vertical seepage velocity.</p> Full article ">Figure 7
<p>Contours of tidal bore-induced seepage velocity within the riverbed. Vertical seepage velocity: (<b>a</b>) <span class="html-italic">S<sub>r</sub></span> = 1 and (<b>b</b>) <span class="html-italic">S<sub>r</sub></span> = 0.99; horizontal seepage velocity: (<b>c</b>) <span class="html-italic">S<sub>r</sub></span> = 1 and (<b>d</b>) <span class="html-italic">S<sub>r</sub></span> = 0.99. <span class="html-italic">S<sub>r</sub></span> is the degree of saturation.</p> Full article ">Figure 8
<p>(<b>a</b>) Dynamic water pressure at the water–riverbed interface. (<b>b</b>) The boundary between the liquefaction zone and the stable zone based on Equations (53) and (55), respectively.</p> Full article ">Figure 9
<p>Maximum liquefaction depth <span class="html-italic">z<sub>l</sub></span>/<span class="html-italic">d</span> as a function of tidal bore height <span class="html-italic">H/d</span> for a partially saturated riverbed composed of isotropic silt soil and fine sand.</p> Full article ">Figure 10
<p>Maximum liquefaction depth <span class="html-italic">z<sub>l</sub></span>/<span class="html-italic">d</span> as a function of water depth <span class="html-italic">d/h</span> before the arrival of the tidal bore for a partially saturated riverbed composed of isotropic silt soil and fine sand.</p> Full article ">Figure 11
<p>Maximum liquefaction depth <span class="html-italic">z<sub>l</sub></span>/<span class="html-italic">d</span> as a function of relative riverbed thickness <span class="html-italic">h</span>/<span class="html-italic">d</span> for a partially saturated riverbed composed of isotropic silt soil and fine sand.</p> Full article ">Figure 12
<p>Maximum liquefaction depth <span class="html-italic">z<sub>l</sub></span>/<span class="html-italic">d</span> as a function of soil stiffness <span class="html-italic">Gβ</span> for a riverbed composed of isotropic silt soil and fine sand.</p> Full article ">Figure 13
<p>Maximum liquefaction depth <span class="html-italic">z<sub>l</sub></span>/<span class="html-italic">d</span> as a function of soil permeability <span class="html-italic">k<sub>z</sub></span> for a partially saturated isotropic riverbed.</p> Full article ">
19 pages, 2228 KiB  
Article
Thermodynamic Analysis of a Marine Diesel Engine Waste Heat-Assisted Cogeneration Power Plant Modified with Regeneration Onboard a Ship
by Haydar Kepekci and Cuneyt Ezgi
J. Mar. Sci. Eng. 2024, 12(9), 1667; https://doi.org/10.3390/jmse12091667 - 18 Sep 2024
Viewed by 737
Abstract
The objective of this study is to perform a thermodynamic analysis on a marine diesel engine waste heat-assisted cogeneration power plant modified with regeneration onboard a ship. The proposed system utilizes the waste heat from the main engine jacket water and exhaust gases [...] Read more.
The objective of this study is to perform a thermodynamic analysis on a marine diesel engine waste heat-assisted cogeneration power plant modified with regeneration onboard a ship. The proposed system utilizes the waste heat from the main engine jacket water and exhaust gases to generate electricity and heat, thereby reducing the fuel consumption and CO2 emissions. The methodology includes varying different turbine inlet pressures, extraction pressures, and fractions of steam extracted from the turbine to evaluate their effects on the efficiency, utilization factor, transformation energy equivalent factor, process heat rate, electrical power output, saved fuel flow rate, saved fuel cost, and reduced CO2 emissions. The analysis demonstrates that the proposed system can achieve an efficiency of 48.18% and utilization factor of 86.36%, savings of up to 57.325 kg/h in fuel, 65.606 USD/h in fuel costs, and 180.576 kg/h in CO2 emissions per unit mass flow rate through a steam turbine onboard a ship. Full article
(This article belongs to the Section Ocean Engineering)
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<p>System design onboard ship.</p> Full article ">Figure 2
<p>T–s diagram of system.</p> Full article ">Figure 3
<p>Efficiency and utilization factor versus extraction pressure for turbine inlet pressure of 5000 kPa.</p> Full article ">Figure 4
<p>Process heat rate and electrical power versus extraction pressure for turbine inlet pressure of 5000 kPa.</p> Full article ">Figure 5
<p>Saved fuel flow rate, saved fuel cost, and reduced CO<sub>2</sub> emissions versus extraction pressure for turbine inlet pressure of 5000 kPa.</p> Full article ">Figure 6
<p>Utilization factor and transformation energy equivalent factor of cogeneration system versus power and heat ratio for turbine inlet pressure of 5000 kPa.</p> Full article ">Figure 7
<p>Efficiency and utilization factor versus extraction pressure for turbine inlet pressure of 8000 kPa.</p> Full article ">Figure 8
<p>Process heat rate and electrical power versus extraction pressure for turbine inlet pressure of 8000 kPa.</p> Full article ">Figure 9
<p>Saved fuel flow rate, saved fuel cost, and reduced CO<sub>2</sub> emissions versus extraction pressure for turbine inlet pressure of 8000 kPa.</p> Full article ">Figure 10
<p>Efficiency and utilization factor versus extraction pressure for turbine inlet pressure of 10,000 kPa.</p> Full article ">Figure 11
<p>Process heat rate and electrical power versus extraction pressure for turbine inlet pressure of 10,000 kPa.</p> Full article ">Figure 12
<p>Saved fuel flow rate, saved fuel cost, and reduced CO<sub>2</sub> emissions versus extraction pressure for turbine inlet pressure of 10,000 kPa.</p> Full article ">Figure 13
<p>Utilization factor and transformation energy equivalent factor of cogeneration system versus power and heat ratio for turbine inlet pressure of 10,000 kPa.</p> Full article ">
27 pages, 7064 KiB  
Article
Uncertainty of Wave Spectral Shape and Parameters Associated with the Spectral Estimation
by Guilherme Clarindo, Ricardo M. Campos and Carlos Guedes Soares
J. Mar. Sci. Eng. 2024, 12(9), 1666; https://doi.org/10.3390/jmse12091666 - 18 Sep 2024
Viewed by 880
Abstract
The uncertainty in estimating the wave spectrum from the records of wave elevation by heave–pitch–roll buoys is studied, considering the effects of the estimation method and the spectral resolution adopted in the process. This investigation utilizes measurements from a wave buoy moored in [...] Read more.
The uncertainty in estimating the wave spectrum from the records of wave elevation by heave–pitch–roll buoys is studied, considering the effects of the estimation method and the spectral resolution adopted in the process. This investigation utilizes measurements from a wave buoy moored in deep water in the South Atlantic Ocean. First, the spectra are computed using the autocorrelation function and the direct Fourier method. Second, the spectral resolution is tested in terms of degrees of freedom. The degrees of freedom are varied, and the resulting spectra and integrated parameters are computed, showing significant variability. A simple and robust methodology for determining the wave spectrum is suggested, which involves calculating the average energy density in each frequency band. The results of this methodology reduce the variability of the estimated parameters, improving overall accuracy while preserving frequency resolution, which is crucial in complex sea states. Additionally, to demonstrate the feasibility of the implemented approach, the final spectrum is fitted using an empirical model ideal for that type of spectrum. Finally, the performance and the goodness of the fit process for the final averaged curve are checked by widely used statistical metrics, such as R2 = 0.97 and root mean square error = 0.49. Full article
(This article belongs to the Special Issue Impact of Ocean Wave Loads on Marine Structures)
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<p>Location of the moored buoy at the South Coast of Brazil and the Axys 3M data series buoy.</p> Full article ">Figure 2
<p>Significant wave height recorded by the buoy during the passage of a storm on 20 and 21 August 2009.</p> Full article ">Figure 3
<p>Illustration of sea surface displacement on 21 August 2009 as well the number of waves recorded by the heave movement of the buoy.</p> Full article ">Figure 4
<p>The wave spectra estimated by ACF exemplified by various lag numbers.</p> Full article ">Figure 5
<p>Functions and parameters related to the autocorrelations.</p> Full article ">Figure 6
<p>Wave spectrum obtained from ACF (original spectrum) and the smoothed spectrum by Hanning Window and Moving Average approaches.</p> Full article ">Figure 7
<p>Wave spectrum obtained from FFT (original spectrum) and the smoothed spectrum computed by Hanning Window and Moving Average.</p> Full article ">Figure 8
<p>Comparison of wave spectra obtained from ACF and FFT.</p> Full article ">Figure 9
<p>(<b>a</b>) Smoothed spectra computed by the Welch method for several degrees of freedom. (<b>b</b>) Attention to the small number of degrees of freedom to assess the bimodal system.</p> Full article ">Figure 10
<p><b>Top panel</b>: 8 degrees of freedom spectra fitted by the JONSWAP model. <b>Bottom panel</b>: 99% of confidence interval (predictions boundaries).</p> Full article ">Figure 11
<p><b>Top panel</b>: 16 degrees of freedom spectra fitted by the JONSWAP model. <b>Bottom panel</b>: 99% of confidence interval.</p> Full article ">Figure 12
<p>Welch spectra for 8 degrees of freedom as well as the JONSWAP bimodal fit.</p> Full article ">Figure 13
<p>Welch spectra for 16 degrees of freedom as well as the JONSWAP bimodal fit.</p> Full article ">Figure 14
<p>The wave spectrum for various degrees of freedom and the robust spectra resulting from average density over frequencies, respectively.</p> Full article ">Figure 15
<p>The fitted final spectrum resulting from averaging several DoF.</p> Full article ">Figure 16
<p>The fit process considering the confidence intervals in order to cover all frequency bands.</p> Full article ">Figure 17
<p>Residual plot of fitting the JONSWAP spectral model data.</p> Full article ">
19 pages, 5119 KiB  
Article
Estimation of Source Range and Location Using Ship-Radiated Noise Measured by Two Vertical Line Arrays with a Feed-Forward Neural Network
by Moon Ju Jo, Jee Woong Choi and Dong-Gyun Han
J. Mar. Sci. Eng. 2024, 12(9), 1665; https://doi.org/10.3390/jmse12091665 - 18 Sep 2024
Viewed by 787
Abstract
Machine learning-based source range estimation is a promising method for enhancing the performance of tracking both the dynamic and static positions of targets in the underwater acoustic environment using extensive training data. This study constructed a machine learning model for source range estimation [...] Read more.
Machine learning-based source range estimation is a promising method for enhancing the performance of tracking both the dynamic and static positions of targets in the underwater acoustic environment using extensive training data. This study constructed a machine learning model for source range estimation using ship-radiated noise recorded by two vertical line arrays (VLAs) during the Shallow-water Acoustic Variability Experiment (SAVEX-15), employing the Sample Covariance Matrix (SCM) and the Generalized Cross Correlation (GCC) as input features. A feed-forward neural network (FNN) was used to train the model on the acoustic characteristics of the source at various distances, and the range estimation results indicated that the SCM outperformed the GCC with lower error rates. Additionally, array tilt correction using the array invariant-based method improved range estimation accuracy. The impact of the training data composition corresponding to the bottom depth variation between the source and receivers on range estimation performance was also discussed. Furthermore, the estimated ranges from the two VLA locations were applied to localization using trilateration. Our results confirm that the SCM is the more appropriate feature for the FNN-based source range estimation model compared with the GCC and imply that ocean environment variability should be considered in developing a general-purpose machine learning model for underwater acoustics. Full article
(This article belongs to the Special Issue Applications of Underwater Acoustics in Ocean Engineering)
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<p>(<b>a</b>) Bathymetry of the experimental area and ship track of the R/V Onnuri. Range variations in the R/V Onnuri (<b>b</b>) from VLA1 and (<b>c</b>) from VLA2 as a function of time. The black dotted line represents the ship track and range variations used for the training and validation data, the black solid line corresponds to the test data, and the black dashed lines in the range variations represent other ships. The red box represents the ship track corresponding to the acoustic data analyzed in <a href="#jmse-12-01665-f002" class="html-fig">Figure 2</a>.</p> Full article ">Figure 2
<p>(<b>a</b>) Spectrogram of acoustic data measured from VLA1 at a depth of 25 m from May 25 21:35 to 21:59 UTC. The black thick dashed line represents the time of the closest point of approach (21:48 UTC). (<b>b</b>) The spectral probability density of acoustic data for 24 min. Thick magenta lines represents the average values of the mean intensity level (dashed) and the median spectrum level corresponding to the 50th percentile (solid).</p> Full article ">Figure 3
<p>The estimated array tilt angle of the two VLAs, calculated every minute before (blue crosses) and after (orange circles) removing the outliers using a median filter with a window size of 10. (<b>a</b>) VLA1 and (<b>b</b>) VLA2.</p> Full article ">Figure 4
<p>Source–receiver range estimation and localization results of the same VLA test. Range estimation results are shown for (<b>a</b>) the VLA1 test using the SCM, (<b>b</b>) the VLA2 test using the SCM, (<b>d</b>) the VLA1 test using the GCC, and (<b>e</b>) the VLA2 test using the GCC. Estimated ranges before and after tilt correction are represented by blue crosses and orange circles, respectively. Source localization results after tilt correction are represented by the purple circles in (<b>c</b>) when using the SCM and in (<b>f</b>) when using the GCC. The black solid line represents the ship track and range variations used for the test data. The black dashed line represents another ship during the test time.</p> Full article ">Figure 5
<p>Source–receiver range estimation and localization results of the different VLA tests. Range estimation results are shown for (<b>a</b>) the VLA1 test using the SCM, (<b>b</b>) the VLA2 test using the SCM, (<b>d</b>) the VLA1 test using the GCC, and (<b>e</b>) the VLA2 test using the GCC. Estimated ranges before and after tilt correction are represented by blue crosses and orange circles, respectively. Source localization results after tilt correction are represented by the purple circles in (<b>c</b>) when using the SCM and in (<b>f</b>) when using the GCC. The black solid line represents the ship track and range variations used for the test data. The black dashed line represents another ship during the test time.</p> Full article ">Figure 6
<p>Source–receiver range estimation and localization results of the large training data test. Range estimation results are shown for (<b>a</b>) the VLA1 test using the SCM, (<b>b</b>) the VLA2 test using the SCM, (<b>d</b>) the VLA1 test using the GCC, and (<b>e</b>) the VLA2 test using the GCC. Estimated ranges before and after tilt correction are represented by blue crosses and orange circles, respectively. Source localization results after tilt correction are represented by the purple circles in (<b>c</b>) when using the SCM and in (<b>f</b>) when using the GCC. The black solid line represents the ship track and range variations used for the test data. The black dashed line represents another ship during the test time.</p> Full article ">Figure 7
<p>Variations in the relative bottom depth and the percentage error of the range estimation as a function of the time of the test data (feature: SCM after tilt correction). (<b>a</b>) The same VLA test, (<b>b</b>) the different VLA test, and (<b>c</b>) the large training data test. Relative bottom depth and percentage error are represented by blue crosses and orange circles, respectively.</p> Full article ">Figure 8
<p>The training data distribution as a function of the average bottom depth and the source range obtained from VLA1 and VLA2: (<b>a</b>) before data resampling and (<b>b</b>) after data resampling.</p> Full article ">Figure A1
<p>MAPEs of estimated ranges according to the array tilt change.</p> Full article ">Figure A2
<p>Estimated ranges when the relative bottom depths are (<b>a</b>) −3 m, (<b>b</b>) −2 m, (<b>c</b>) −1 m, (<b>d</b>) 1 m, (<b>e</b>) 2 m, and (<b>f</b>) 3 m.</p> Full article ">Figure A3
<p>Box plot of percentage errors of the estimated ranges according to the relative bottom depth when the input feature is (<b>a</b>) the SCM or (<b>b</b>) the GCC. Within each box, horizontal lines denote median values; boxes extend from the 25th percentile to the 75th percentile of percentage errors; vertical extending lines denote adjacent values (i.e., the most extreme values within the 1.5 interquartile range of the 25th and 75th percentiles of percentage errors); dots denote observations outside the range of adjacent values. The orange line denotes the mean value.</p> Full article ">
26 pages, 13312 KiB  
Article
Investigation of Vessel Manoeuvring Abilities in Shallow Depths by Applying Neural Networks
by Lúcia Moreira and C. Guedes Soares
J. Mar. Sci. Eng. 2024, 12(9), 1664; https://doi.org/10.3390/jmse12091664 - 17 Sep 2024
Viewed by 1603
Abstract
A set of planar motion mechanism experiments of the Duisburg Test Case Post-Panamax container model executed in a towing tank with shallow depth is applied to train a neural network to analyse the ability of the proposed model to learn the effects of [...] Read more.
A set of planar motion mechanism experiments of the Duisburg Test Case Post-Panamax container model executed in a towing tank with shallow depth is applied to train a neural network to analyse the ability of the proposed model to learn the effects of different depth conditions on ship’s manoeuvring capabilities. The motivation of the work presented in this paper is to contribute an alternative and effective approach to model non-linear systems through artificial neural networks that address the manoeuvring simulation of ships in shallow water. The system is developed using the Levenberg–Marquardt backpropagation training algorithm and the resilient backpropagation scheme to demonstrate the correlation between the vessel forces and the respective trajectories and velocities. Sensitivity analyses were performed to identify the number of layers necessary for the proposed model to predict the vessel manoeuvring characteristics in two different depths. The outcomes achieved with the proposed system have shown excellent accuracy and ability in predicting ship manoeuvring with varying depths of shallow water. Full article
(This article belongs to the Special Issue Models and Simulations of Ship Manoeuvring)
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<p>Setup for captive tests.</p> Full article ">Figure 2
<p>Vessel- and earth-bound (towing tank) reference frame projections: (<b>a</b>) on the <span class="html-italic">O</span>0<span class="html-italic">x</span>0<span class="html-italic">y</span>0 plane; (<b>b</b>) on the <span class="html-italic">O</span>0<span class="html-italic">x</span>0<span class="html-italic">z</span>0 plane; (<b>c</b>) on the <span class="html-italic">O</span>0<span class="html-italic">y</span>0<span class="html-italic">z</span>0 plane.</p> Full article ">Figure 3
<p>A general elementary feed-forward network representation.</p> Full article ">Figure 4
<p>Picture of the network outline.</p> Full article ">Figure 5
<p>Training results for surge force <span class="html-italic">X</span> prediction in different water depths: (<b>a</b>) <span class="html-italic">h</span> = 0.3254 m; (<b>b</b>) <span class="html-italic">h</span> = 0.1952 m.</p> Full article ">Figure 6
<p>Training results for sway force <span class="html-italic">Y</span> prediction in different water depths: (<b>a</b>) <span class="html-italic">h</span> = 0.3254 m; (<b>b</b>) <span class="html-italic">h</span> = 0.1952 m.</p> Full article ">Figure 7
<p>Training results for yaw moment <span class="html-italic">N</span> prediction in different water depths: (<b>a</b>) <span class="html-italic">h</span> = 0.3254 m; (<b>b</b>) <span class="html-italic">h</span> = 0.1952 m.</p> Full article ">Figure 8
<p>Boxplots of the errors for surge force <span class="html-italic">X</span> prediction in different water depths: (<b>a</b>) <span class="html-italic">h</span> = 0.3254 m; (<b>b</b>) <span class="html-italic">h</span> = 0.1952 m.</p> Full article ">Figure 9
<p>Boxplots of the errors for sway force <span class="html-italic">Y</span> prediction in different water depths: (<b>a</b>) <span class="html-italic">h</span> = 0.3254 m; (<b>b</b>) <span class="html-italic">h</span> = 0.1952 m.</p> Full article ">Figure 10
<p>Boxplots of the errors for yaw moment <span class="html-italic">N</span> prediction in different water depths: (<b>a</b>) <span class="html-italic">h</span> = 0.3254 m; (<b>b</b>) <span class="html-italic">h</span> = 0.1952 m.</p> Full article ">Figure 11
<p>Training results for surge velocity <span class="html-italic">u</span> prediction in different water depths: (<b>a</b>) <span class="html-italic">h</span> = 0.3254 m; (<b>b</b>) <span class="html-italic">h</span> = 0.1952 m.</p> Full article ">Figure 12
<p>Training results for sway velocity <span class="html-italic">v</span> prediction in different water depths: (<b>a</b>) <span class="html-italic">h</span> = 0.3254 m; (<b>b</b>) <span class="html-italic">h</span> = 0.1952 m.</p> Full article ">Figure 13
<p>Training results for yaw rate <span class="html-italic">r</span> prediction in different water depths: (<b>a</b>) <span class="html-italic">h</span> = 0.3254 m (<b>b</b>) <span class="html-italic">h</span> = 0.1952 m.</p> Full article ">Figure 14
<p>Boxplots of the error for surge velocity <span class="html-italic">u</span> prediction in different water depths: (<b>a</b>) <span class="html-italic">h</span> = 0.3254 m; (<b>b</b>) <span class="html-italic">h</span> = 0.1952 m.</p> Full article ">Figure 15
<p>Boxplots of the error for sway velocity <span class="html-italic">v</span> prediction in different water depths: (<b>a</b>) <span class="html-italic">h</span> = 0.3254 m; (<b>b</b>) <span class="html-italic">h</span> = 0.1952 m.</p> Full article ">Figure 16
<p>Boxplots of the error for yaw rate <span class="html-italic">r</span> prediction in different water depths: (<b>a</b>) <span class="html-italic">h</span> = 0.3254 m; (<b>b</b>) <span class="html-italic">h</span> = 0.1952 m.</p> Full article ">
14 pages, 3465 KiB  
Article
Revised Friction Groups for Evaluating Hydraulic Parameters: Pressure Drop, Flow, and Diameter Estimation
by Dejan Brkić
J. Mar. Sci. Eng. 2024, 12(9), 1663; https://doi.org/10.3390/jmse12091663 - 17 Sep 2024
Viewed by 823
Abstract
Suitable friction groups are provided for solving three typical hydraulic problems. While the friction group based on viscous forces is used for calculating the pressure drop or head loss in pipes and open channels, commonly referred to as the Type 1 problem in [...] Read more.
Suitable friction groups are provided for solving three typical hydraulic problems. While the friction group based on viscous forces is used for calculating the pressure drop or head loss in pipes and open channels, commonly referred to as the Type 1 problem in hydraulic engineering, additional friction groups with similar behaviors are introduced for calculating steady flow discharge as the Type 2 problem and, for estimating hydraulic diameter as the Type 3 problem. Contrary to the viscous friction group, the traditional Darcy–Weisbach friction factor demonstrates a negative correlation with the Reynolds number. This results in curves that slope downward from small to large Reynolds numbers on the well-known Moody chart. In contrast, the friction group used here, based on viscous forces, establishes a more appropriate relationship. In this case, the friction and Reynolds number are positively correlated, meaning that both increase or decrease simultaneously. Here, rearranged diagrams for all three mentioned problems show similar behaviors. This paper compares the Moody diagram with the diagram for the viscous force friction group. The turbulent parts of both diagrams are based on the Colebrook equation, with the newly reformulated version using the viscous force friction group. As the Colebrook equation is implicit with respect to friction, requiring an iterative solution, an explicit solution using the Lambert W-function for the reformulated version is offered. Examples are provided for both pipes and open channel flow. Full article
(This article belongs to the Topic Advances in Environmental Hydraulics)
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<p>Moody chart for the Darcy–Weisbach flow friction factor, <math display="inline"><semantics> <mrow> <mi mathvariant="normal">f</mi> </mrow> </semantics></math>, as a function of the Reynolds number, <math display="inline"><semantics> <mrow> <mi mathvariant="normal">R</mi> <mi mathvariant="normal">e</mi> <mo>,</mo> </mrow> </semantics></math> and the relative roughness of inner pipe surface, <math display="inline"><semantics> <mrow> <mi mathvariant="sans-serif">ε</mi> <mo>/</mo> <mi mathvariant="normal">D</mi> </mrow> </semantics></math>. The red line represents laminar flow, full black lines represent partially developed turbulent flow, while point-lines represent fully turbulent flow.</p> Full article ">Figure 2
<p>Transformed Moody chart based on viscous flow friction factor λ: Its turbulent part is based on the transformed Colebrook equation—The red line represents laminar flow, full lines represent partially developed turbulent flow, while point-lines represent fully turbulent flow.</p> Full article ">Figure 3
<p>Branches of the Lambert W-function.</p> Full article ">Figure 4
<p>Limitations for verification of <math display="inline"><semantics> <mrow> <mi mathvariant="normal">W</mi> <mfenced separators="|"> <mrow> <msup> <mrow> <mi mathvariant="normal">e</mi> </mrow> <mrow> <mi mathvariant="normal">x</mi> </mrow> </msup> </mrow> </mfenced> </mrow> </semantics></math> in computer registers.</p> Full article ">Figure 5
<p>Chart suitable for determination of flow discharge.</p> Full article ">Figure 6
<p>Chart suitable for estimation of pipe diameters.</p> Full article ">Figure A1
<p>Elements for defining the hydraulic diameter.</p> Full article ">Figure A2
<p>Hydraulic diameter in specific cases: (<b>a</b>) partially filled circular pipes—Equation (8) and (<b>b</b>) trapezoidal channels—Equation (9).</p> Full article ">
14 pages, 3132 KiB  
Article
Application of Self-Polishing Copolymer and Tin-Free Nanotechnology Paint for Ships
by Yushi Wang, Cheunghwa Hsu, Guanhong Pan and Chenghao Chen
J. Mar. Sci. Eng. 2024, 12(9), 1662; https://doi.org/10.3390/jmse12091662 - 16 Sep 2024
Viewed by 699
Abstract
During a ship’s voyage, it is difficult to maintain its hull, and prolonged exposure to seawater can lead to the attachment of marine organisms, which can negatively impact the ship’s speed. The original self-polishing copolymer was a tributyltin-containing paint used for applying two [...] Read more.
During a ship’s voyage, it is difficult to maintain its hull, and prolonged exposure to seawater can lead to the attachment of marine organisms, which can negatively impact the ship’s speed. The original self-polishing copolymer was a tributyltin-containing paint used for applying two layers of protective coating onto a ship’s bottom plate. According to International Maritime Organization (abbreviated as IMO) regulations, users are no longer allowed to use paints containing tributyltin. Therefore, manufacturers have developed a tributyltin-free paint, known as tin-free nanotechnology paint, which can be used as a replacement for the base coat on ship bottom plates. This study involves the use of a self-polishing copolymer spray and tin-free nanotechnology paint. A model coated with these two types of paint will be observed underwater to study the growth of marine organisms. Additionally, fuel consumption will be analyzed through underwater inspections and sea trials. Based on the experimental data, it is known that tin-free nanotechnology paint can significantly reduce the need for repairs in factories and greatly decrease maintenance costs when compared to self-polishing copolymers. Full article
(This article belongs to the Section Ocean Engineering)
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<p>Experimental model material.</p> Full article ">Figure 2
<p>Underwater inspection site (A) hull, (B) seabed doors, (C) shafting, (D) propeller, (E) rudder plates.</p> Full article ">Figure 3
<p>Sea test voyage chart.</p> Full article ">Figure 4
<p>Experimental model recording points.</p> Full article ">Figure 5
<p>Model 1 underwater steel plate record: (<b>a</b>) Model 1 first week; (<b>b</b>) Model 1 fifth week; (<b>c</b>) Model 1 tenth week; (<b>d</b>) Model 1 fifteenth week.</p> Full article ">Figure 6
<p>Model 2 underwater steel plate record: (<b>a</b>) Model 2 first week; (<b>b</b>) Model 2 fifth week; (<b>c</b>) Model 2 tenth week; (<b>d</b>) Model 2 fifteenth week.</p> Full article ">Figure 7
<p>Model 3 underwater steel plate record: (<b>a</b>) Model 3 first week; (<b>b</b>) Model 3 fifth week; (<b>c</b>) Model 3 tenth week; (<b>d</b>) Model 3 fifteenth week.</p> Full article ">Figure 8
<p>Model 4 underwater steel plate record: (<b>a</b>) Model 4 first week; (<b>b</b>) Model 4 fifth week; (<b>c</b>) Model 4 tenth week; (<b>d</b>) Model 4 fifteenth week.</p> Full article ">Figure 9
<p>Self-polishing copolymer underwater inspection status: (<b>a</b>) hull plate; (<b>b</b>) seabed door; (<b>c</b>) shafting; (<b>d</b>) propeller; (<b>e</b>) rudder plate.</p> Full article ">Figure 10
<p>Tin-free nanotechnology paint underwater inspection status: (<b>a</b>) hull plate; (<b>b</b>) seabed door; (<b>c</b>) shafting; (<b>d</b>) propeller; (<b>e</b>) rudder plate.</p> Full article ">
17 pages, 4687 KiB  
Article
Research on LSTM-Based Maneuvering Motion Prediction for USVs
by Rong Guo, Yunsheng Mao, Zuquan Xiang, Le Hao, Dingkun Wu and Lifei Song
J. Mar. Sci. Eng. 2024, 12(9), 1661; https://doi.org/10.3390/jmse12091661 - 16 Sep 2024
Viewed by 640
Abstract
Maneuvering motion prediction is central to the control and operation of ships, and the application of machine learning algorithms in this field is increasingly prevalent. However, challenges such as extensive training time, complex parameter tuning processes, and heavy reliance on mathematical models pose [...] Read more.
Maneuvering motion prediction is central to the control and operation of ships, and the application of machine learning algorithms in this field is increasingly prevalent. However, challenges such as extensive training time, complex parameter tuning processes, and heavy reliance on mathematical models pose substantial obstacles to their application. To address these challenges, this paper proposes an LSTM-based modeling algorithm. First, a maneuvering motion model based on a real USV model was constructed, and typical operating conditions were simulated to obtain data. The Ornstein–Uhlenbeck process and the Hidden Markov Model were applied to the simulation data to generate noise and random data loss, respectively, thereby constructing a sample set that reflects real experiment characteristics. The sample data were then pre-processed for training, employing the MaxAbsScaler strategy for data normalization, Kalman filtering and RRF for data smoothing and noise reduction, and Lagrange interpolation for data resampling to enhance the robustness of the training data. Subsequently, based on the USV maneuvering motion model, an LSTM-based black-box motion prediction model was established. An in-depth comparative analysis and discussion of the model’s network structure and parameters were conducted, followed by the training of the ship maneuvering motion model using the optimized LSTM model. Generalization tests were then performed on a generalization set under Zigzag and turning conditions to validate the accuracy and generalization performance of the prediction model. Full article
(This article belongs to the Special Issue Safe Maneuvering, Efficient Navigation and Intelligent Management for Ships)
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<p>Model of the USV.</p> Full article ">Figure 2
<p>USV motion prediction model based on LSTM.</p> Full article ">Figure 3
<p>Impact of Network Structure on Prediction (the mean represents the average value of the corresponding parameter).</p> Full article ">Figure 4
<p>Impact of Network Structure on Training Time.</p> Full article ">Figure 5
<p>Impact of Training Settings on Prediction Performance.</p> Full article ">Figure 6
<p>Impact of training parameters on Training Time.</p> Full article ">Figure 7
<p>The network parameters and structure of the optimized LSTM model.</p> Full article ">Figure 8
<p>Comparison of motion prediction results for the 20°/20° zigzag test under wave conditions.</p> Full article ">Figure 9
<p>Comparison of motion prediction results for the 35° turning test under wave conditions.</p> Full article ">
22 pages, 2329 KiB  
Review
Updated Review of Longshore Sediment Transport: Advantages, Disadvantages, and Comparisons Using a Satellite Image Methodology
by César M. Alpaca-Chacón, Bismarck Jigena-Antelo, César A. Quispe-Gonzales, Douglas D. Sarango-Julca, Antonio Contreras-de-Villar and Juan J. Muñoz-Perez
J. Mar. Sci. Eng. 2024, 12(9), 1660; https://doi.org/10.3390/jmse12091660 - 16 Sep 2024
Viewed by 998
Abstract
This review updates the different categories and formulations of the calculation of longshore sediment transport (LST) and summarizes their advantages and disadvantages. Most of these methodologies require calibration for areas different from those studied by their authors. Thus, a method of validation and [...] Read more.
This review updates the different categories and formulations of the calculation of longshore sediment transport (LST) and summarizes their advantages and disadvantages. Most of these methodologies require calibration for areas different from those studied by their authors. Thus, a method of validation and calibration is presented here by processing satellite images with CoastSat software (release v 2.7) to determine accretion and erosion volumes. This low-cost methodology was applied to Salaverry Beach (Peru) to compare the results of the different formulations. A range of variation between −96% and +68% was observed concerning the error, with van Rijn’s formula being the most accurate for this particular case. Full article
(This article belongs to the Special Issue Recent Experiences and Monitoring in Coastal, Fluvial and Marine Hydrography)
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<p>Port of Salaverry (Perú). Fuente: Google Earth Pro (2024).</p> Full article ">Figure 2
<p>Sketch showing LST based on wind direction and coastal waves.</p> Full article ">Figure 3
<p>Wave angle in wave breaking in LST.</p> Full article ">Figure 4
<p>The conservation equation LST (Pernald-Considere) [<a href="#B50-jmse-12-01660" class="html-bibr">50</a>] used with the images downloaded from CoastSat.</p> Full article ">Figure 5
<p>The error of the different formulas used related to the CoastSat results for Salaverry Beach (Peru).</p> Full article ">
23 pages, 1100 KiB  
Article
Research on Response Strategies for Inland Waterway Vessel Traffic Risk Based on Cost-Effect Trade-Offs
by Yanyi Chen, Ziyang Ye, Tao Wang, Baiyuan Tang, Chengpeng Wan, Hao Zhang and Yunpeng Li
J. Mar. Sci. Eng. 2024, 12(9), 1659; https://doi.org/10.3390/jmse12091659 - 16 Sep 2024
Viewed by 699
Abstract
Compared to maritime vessel traffic accidents, there is a scarcity of available, and only incomplete, accident data for inland waterway accidents. Additionally, the characteristics of different waterway segments vary significantly, and the factors affecting navigation safety risks and their mechanisms may also differ. [...] Read more.
Compared to maritime vessel traffic accidents, there is a scarcity of available, and only incomplete, accident data for inland waterway accidents. Additionally, the characteristics of different waterway segments vary significantly, and the factors affecting navigation safety risks and their mechanisms may also differ. Meanwhile, in recent years, extreme weather events have been frequent in inland waterways, and there has been a clear trend towards larger vessels, bringing about new safety hazards and management challenges. Currently, research on inland waterway navigation safety risks mainly focuses on risk assessment, with scarce quantitative studies on risk mitigation measures. This paper proposes a new method for improving inland waterway traffic safety, based on a cost-effectiveness trade-off approach to mitigate the risk of vessel traffic accidents. The method links the effectiveness and cost of measures and constructs a comprehensive cost-benefit evaluation model using fuzzy Bayesian and quantification conversion techniques, considering the reduction effects of risk mitigation measures under uncertain conditions and the various costs they may incur. Taking the upper, middle, and lower reaches of the Yangtze River as examples, this research evaluates key risk mitigation measures for different waterway segments and provides the most cost-effective strategies. Findings reveal that, even if different waterways share the same key risk sources, the most cost-effective measures vary due to environmental differences. Moreover, there is no inherent correlation between the best-performing measures in terms of benefits and the lowest-cost measures, nor are they necessarily recommended. The proposed method and case studies provide theoretical support for scientifically formulating risk mitigation measures in complex environments and offer guidance for inland waterway management departments to determine future key work directions. Full article
(This article belongs to the Special Issue Innovative Technologies in Safety and Reliability of Marine Engineering)
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<p>Method Framework.</p> Full article ">Figure 2
<p>Risk Assessment of S6 in the Lower Reaches of the Yangtze River.</p> Full article ">Figure 3
<p>Cost-Benefit Values of Risk Mitigation Strategies along the Yangtze River Upstream, Midstream, and Downstream.</p> Full article ">
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