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Volume 12
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J. Mar. Sci. Eng., Volume 12, Issue 12 (December 2024) – 45 articles

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39 pages, 2102 KiB  
Review
Current Status and Future Trends in Installation, Operation and Maintenance of Offshore Floating Wind Turbines
by Mingfeng Hu, Jinkun Shi, Sheng Yang, Mingsheng Chen, Yichang Tang and Suqian Liu
J. Mar. Sci. Eng. 2024, 12(12), 2155; https://doi.org/10.3390/jmse12122155 - 25 Nov 2024
Abstract
The installation and operation phases are critical stages in the lifecycle of offshore wind turbines, with costs associated with the installation and maintenance of floating wind turbines accounting for approximately 50% of the total investment. This paper presents the latest advancements in the [...] Read more.
The installation and operation phases are critical stages in the lifecycle of offshore wind turbines, with costs associated with the installation and maintenance of floating wind turbines accounting for approximately 50% of the total investment. This paper presents the latest advancements in the technologies for the installation and maintenance of floating wind turbines. First, it discusses the installation techniques and relevant research related to the foundations and components of floating wind turbines. Next, it explores various operational strategies for offshore wind turbines and studies on major component replacements. The interrelationship of research in the installation and maintenance fields for floating wind turbines is examined. Furthermore, this paper investigates various tools and equipment used for the installation and maintenance of offshore wind turbines. It also addresses the relevant regulations and standards governing offshore operations for floating wind turbines. Finally, this paper provides a forward-looking perspective on the installation and maintenance of floating wind turbines. Full article
(This article belongs to the Section Ocean Engineering)
21 pages, 2789 KiB  
Article
Nonlinear Dynamic Stability Analysis of Ground Effect Vehicles in Waves Using Poincaré–Lindstedt Perturbation Method
by Jafar Masri, Laurent Dala and Benoit Huard
J. Mar. Sci. Eng. 2024, 12(12), 2154; https://doi.org/10.3390/jmse12122154 - 25 Nov 2024
Abstract
In this study, we present an analytical tool that can be used to predict the nonlinear dynamic response of ground effect vehicles (GEVs) advancing through sinusoidal head-sea waves. GEVs exhibit a unique instability phenomenon known as porpoising, which is an oscillatory motion along [...] Read more.
In this study, we present an analytical tool that can be used to predict the nonlinear dynamic response of ground effect vehicles (GEVs) advancing through sinusoidal head-sea waves. GEVs exhibit a unique instability phenomenon known as porpoising, which is an oscillatory motion along the heave and pitch axes that can cause serious structural damage. The heaving and pitching equations of motion are presented in the form of coupled, forced, and nonlinear Duffing-type equations with cubic nonlinearity. The analytical model developed in this study leverages the Poincaré–Lindstedt perturbation method to express the amplitude and frequency of motion in terms of all physical parameters. The accuracy and reliability of the proposed model were validated through computational fluid dynamics (CFD) simulations based on incompressible unsteady Reynolds-averaged Navier–Stokes (RANS) equations. The results show a strong agreement between the analytical tool and the CFD simulations, with minor discrepancies due to assumptions inherent in the simulations, particularly the assumption that seawater only passes beneath the hull, resulting in increased buoyancy forces and reduced damping. This study offers a novel and practical method for predicting the dynamic stability of GEVs under realistic sea conditions, potentially enhancing safety and operational efficiency by mitigating the risks associated with porpoising. Full article
(This article belongs to the Section Ocean Engineering)
30 pages, 509 KiB  
Article
Analysis of Key Factors and Correlations Influencing the Adoption of Autonomous Ships by Shipping Companies—A Study Integrating Revised DEMATEL-AHP with BOCR
by Tien-Chun Ho and Hsuan-Shih Lee
J. Mar. Sci. Eng. 2024, 12(12), 2153; https://doi.org/10.3390/jmse12122153 - 25 Nov 2024
Abstract
In response to achieving the United Nations Sustainable Development Goals (SDGs) of Climate Action (#13) and Life Below Water (#14), the promotion of autonomous shipping technologies has advanced from the experimental stage to specific regional implementation, presenting the maritime industry with rapid and [...] Read more.
In response to achieving the United Nations Sustainable Development Goals (SDGs) of Climate Action (#13) and Life Below Water (#14), the promotion of autonomous shipping technologies has advanced from the experimental stage to specific regional implementation, presenting the maritime industry with rapid and significant changes and challenges. In the future era, where autonomous vessels dominate shipping, with automated operation systems taking the lead, how successfully shipping companies harness these new maritime transport modes will critically impact the safety, efficiency, and reliability of future vessel operations. With the emergence and development of autonomous vessels, it is crucial to effectively assess the importance and correlation of key factors influencing shipping companies’ adoption of autonomous ships. This study utilizes the Analytic Hierarchy Process (AHP) and Revised Decision Making and Trial Evaluation Laboratory (RDEMATEL) to survey senior managers in container and bulk shipping from Taiwan, China, Japan, and the European Union. Through a literature review on the benefits, opportunities, costs, and risks brought by autonomous shipping, this study aims to understand the critical factors important to shipping companies in adopting autonomous shipping, as well as the correlation between these influencing factors across different shipping sectors. The research findings indicate that “emergency response capability” is a critical factor influencing overall and bulk shipping in the adoption of autonomous vessels, while “incomplete regulations” are the primary factor influencing container shipping in the adoption of autonomous ships. Regarding the correlation of critical influencing factors, “vessel technology development” is the main influencing factor for overall, container, and bulk shipping; “operational performance enhancement” is the primary affected factor for overall and container shipping; and “enhancing personnel and vessel safety” is the main affected factor for bulk shipping. It is hoped that the results of this study can serve as a guide for shipping companies in understanding the benefits and opportunities to be emphasized when adopting autonomous shipping and assist in developing effective strategies to reduce costs and risks. Full article
(This article belongs to the Special Issue Intelligent Systems for Marine Transportation)
14 pages, 6056 KiB  
Article
Centrifugal Test Study on the Vertical Uplift Capacity of Single-Cylinder Foundation in High-Sensitivity Marine Soil
by Mingzhe Wei, Yanghui Ye, Wei Zhao, Zehao Wang, Fuhao Ge and Tingkai Nian
J. Mar. Sci. Eng. 2024, 12(12), 2152; https://doi.org/10.3390/jmse12122152 - 25 Nov 2024
Abstract
Offshore wind power is a new type of clean energy with broad development prospects. Accurate analysis of the uplift capacity of offshore wind turbine foundations is a crucial prerequisite for ensuring the safe operation of wind turbines under complex hydrodynamic conditions. However, current [...] Read more.
Offshore wind power is a new type of clean energy with broad development prospects. Accurate analysis of the uplift capacity of offshore wind turbine foundations is a crucial prerequisite for ensuring the safe operation of wind turbines under complex hydrodynamic conditions. However, current research on the uplift capacity of suction caissons often neglects the high-sensitivity characteristics of marine soils. Therefore, this paper first employs the freeze–thaw cycling procedure to prepare high-sensitivity saturated clay. Subsequently, a single−tube foundation for wind turbines is constructed within a centrifuge through a penetration approach. Ten sets of centrifuge model tests with vertical cyclic pullout are conducted. Through comparative analysis, this study explores the pullout capacity and its variation patterns of suction caisson foundations in clay with different sensitivities under cyclic loading. This research indicates the following: (1) The preparation of high-sensitivity soil through the freeze−thaw procedure is reliable; (2) the uplift capacity of suction caissons in high−sensitivity soil rapidly decreases with increasing numbers of cyclic loads and then tends to stabilize. The cumulative displacement rate of suction caissons in high-sensitivity soil is fast, and the total number of pressure–pullout cycles required to reach non-cumulative displacement is significantly smaller than that in low-sensitivity soil; (3) the vertical cyclic loading times and stiffness evolution patterns of single-tube foundations, considering the influence of sensitivity, have been analyzed. It was found that the secant stiffness exhibits a logarithmic function relationship with both the number of cycles and sensitivity. The findings of this study provide assistance and support for the design of suction caissons in high-sensitivity soils. Full article
(This article belongs to the Special Issue Advances in Marine Engineering: Geological Environment and Hazards—3rd Edition)
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Figure 1

Figure 1
<p>Microscopic images of kaolin and ball clay before and after freeze–thaw cycles: (<b>a</b>) microscopic image of ball clay before freeze–thaw; (<b>b</b>) microscopic image of kaolin before freeze–thaw; (<b>c</b>) microscopic image of ball clay after freeze–thaw; (<b>d</b>) microscopic image of kaolin after freeze–thaw.</p> Full article ">Figure 2
<p>Centrifuge test: (<b>a</b>) centrifugal model test device; (<b>b</b>) photo of test model box and suction cylinder; (<b>c</b>) point layout drawing; (<b>d</b>) schematic diagram of penetration, monotonic, and cyclic loading.</p> Full article ">Figure 3
<p>The experimental relationship between the normalized vertical displacement <span class="html-italic">w</span>/<span class="html-italic">L</span> and the cycle number <span class="html-italic">N</span>: (<b>a</b>) normalized vertical displacements versus number of cycles for low-sensitivity soil at <span class="html-italic">V<sub>C</sub></span> /<span class="html-italic">V</span><sub>0</sub> = 0.39, <span class="html-italic">V</span><sub>a</sub>/<span class="html-italic">V</span><sub>0</sub> = 0.01; <span class="html-italic">V<sub>C</sub></span> /<span class="html-italic">V</span><sub>0</sub> = 0.425, <span class="html-italic">V</span><sub>a</sub>/<span class="html-italic">V</span><sub>0</sub> = 0.03; <span class="html-italic">V<sub>C</sub></span> /<span class="html-italic">V</span><sub>0</sub> = 0.45 and <span class="html-italic">V</span><sub>a</sub>/<span class="html-italic">V</span><sub>0</sub> = 0.05 condition; (<b>b</b>) normalized vertical displacements versus number of cycles for low-sensitivity soil at <span class="html-italic">V<sub>C</sub></span>/<span class="html-italic">V</span><sub>0</sub> = 0.35, <span class="html-italic">V</span><sub>a</sub>/<span class="html-italic">V</span><sub>0</sub> = 0.01; <span class="html-italic">V<sub>C</sub></span>/<span class="html-italic">V</span><sub>0</sub> = 0.325, <span class="html-italic">V</span><sub>a</sub>/<span class="html-italic">V</span><sub>0</sub> = 0.02; <span class="html-italic">V<sub>C</sub></span>/<span class="html-italic">V</span><sub>0</sub> = 0.3 and <span class="html-italic">V</span><sub>a</sub>/<span class="html-italic">V</span><sub>0</sub> = 0.01 condition; (<b>c</b>) normalized vertical displacements versus number of cycles for highly sensitive soils at <span class="html-italic">V<sub>C</sub></span>/<span class="html-italic">V</span><sub>0</sub> = 0.4, <span class="html-italic">V</span><sub>a</sub>/<span class="html-italic">V</span><sub>0</sub> = 0.01; <span class="html-italic">V<sub>C</sub></span>/<span class="html-italic">V</span><sub>0</sub> = 0.425, <span class="html-italic">V</span><sub>a</sub>/<span class="html-italic">V</span><sub>0</sub> = 0.01 conditions; (<b>d</b>) normalized vertical displacements versus number of cycles for highly sensitive soils at <span class="html-italic">V<sub>C</sub></span>/<span class="html-italic">V</span><sub>0</sub> = 0.3, <span class="html-italic">V</span><sub>a</sub>/<span class="html-italic">V</span><sub>0</sub> = 0.01; <span class="html-italic">V<sub>C</sub></span>/<span class="html-italic">V</span><sub>0</sub> = 0.32, <span class="html-italic">V</span><sub>a</sub>/<span class="html-italic">V</span><sub>0</sub> = 0.01 conditions.</p> Full article ">Figure 4
<p>Normalized cyclic load and normalized displacement relationship diagram of low-sensitivity soil: (<b>a</b>) normalized cyclic load versus normalized vertical displacement for <span class="html-italic">V<sub>C</sub></span>/<span class="html-italic">V</span><sub>0</sub> = 0.3 and <span class="html-italic">V</span><sub>a</sub>/<span class="html-italic">V</span><sub>0</sub> = 0.01; (<b>b</b>) normalized cyclic load versus normalized vertical displacement for <span class="html-italic">V<sub>C</sub></span>/<span class="html-italic">V</span><sub>0</sub> = 0.35 and <span class="html-italic">V</span><sub>a</sub>/<span class="html-italic">V</span><sub>0</sub> = 0.02; (<b>c</b>) normalized cyclic load versus normalized vertical displacement for <span class="html-italic">V<sub>C</sub></span>/<span class="html-italic">V</span><sub>0</sub> = 0.45 and <span class="html-italic">V</span><sub>a</sub>/<span class="html-italic">V</span><sub>0</sub> = 0.05; (<b>d</b>) normalized cyclic load versus normalized vertical displacement for <span class="html-italic">V<sub>C</sub></span>/<span class="html-italic">V</span><sub>0</sub> = 0.325 and <span class="html-italic">V</span><sub>a</sub>/<span class="html-italic">V</span><sub>0</sub> = 0.01; (<b>e</b>) normalized cyclic load versus normalized vertical displacement for <span class="html-italic">V<sub>C</sub></span>/<span class="html-italic">V</span><sub>0</sub> = 0.39 and <span class="html-italic">V</span><sub>a</sub>/<span class="html-italic">V</span><sub>0</sub> = 0.01; (<b>f</b>) normalized cyclic load versus normalized vertical displacement for <span class="html-italic">V<sub>C</sub></span>/<span class="html-italic">V</span><sub>0</sub> = 0.425 and <span class="html-italic">V</span><sub>a</sub>/<span class="html-italic">V</span><sub>0</sub> = 0.03.</p> Full article ">Figure 5
<p>Normalized cyclic load and normalized displacement relationship diagram of high-sensitivity soil: (<b>a</b>) normalized cyclic load versus normalized vertical displacement for <span class="html-italic">V<sub>C</sub></span>/<span class="html-italic">V</span><sub>0</sub> = 0.3; <span class="html-italic">V</span><sub>a</sub>/<span class="html-italic">V</span><sub>0</sub> = 0.01; (<b>b</b>) normalized cyclic load versus normalized vertical displacement for <span class="html-italic">V<sub>C</sub></span>/<span class="html-italic">V</span><sub>0</sub> = 0.32; <span class="html-italic">V</span><sub>a</sub>/<span class="html-italic">V</span><sub>0</sub> = 0.01; (<b>c</b>) normalized cyclic load versus normalized vertical displacement for <span class="html-italic">V<sub>C</sub></span>/<span class="html-italic">V</span><sub>0</sub> = 0.4; <span class="html-italic">V</span><sub>a</sub>/<span class="html-italic">V</span><sub>0</sub> = 0.01; (<b>d</b>) normalized cyclic load versus normalized vertical displacement for <span class="html-italic">V<sub>C</sub></span>/<span class="html-italic">V</span><sub>0</sub> = 0.425; <span class="html-italic">V</span><sub>a</sub>/<span class="html-italic">V</span><sub>0</sub> = 0.01.</p> Full article ">Figure 6
<p>Definition of secant stiffness.</p> Full article ">Figure 7
<p>Scatter plot of secant stiffness: (<b>a</b>) scatter plot of cut-line stiffness of low-sensitivity soil; (<b>b</b>) scatter plot of stiffness of high-sensitivity earth cut line.</p> Full article ">
25 pages, 3822 KiB  
Article
Doppler Compensation Techniques for M-Ary Sequence Spread Spectrum Signals Based on Correlation Cost Factors in Mobile Underwater Acoustic Communication
by Yubo Han, Shuping Han, Heng Zhao, Yaohui Hu, Jingfeng Xu and Gang Yang
J. Mar. Sci. Eng. 2024, 12(12), 2151; https://doi.org/10.3390/jmse12122151 - 25 Nov 2024
Abstract
Unlike terrestrial radio, the speed of sound in the ocean is relatively slow, which results in mobile underwater M-ary spread spectrum communication typically exhibiting significant and variable multipath effects along with strong Doppler effects, leading to rapid carrier phase shifts in the received [...] Read more.
Unlike terrestrial radio, the speed of sound in the ocean is relatively slow, which results in mobile underwater M-ary spread spectrum communication typically exhibiting significant and variable multipath effects along with strong Doppler effects, leading to rapid carrier phase shifts in the received signal that severely impact decoding accuracy. This study aims to address the issue of rapid carrier phase shifts caused by significant time-varying Doppler shifts during mobile underwater M-SS communication. This paper innovatively proposes a method for updating matched filters based on correlation cost factors. By calculating the correlation cost factors for each received symbol, the method guides the direction of Doppler estimation and updates the matched filters. After identifying the optimal match, the received symbols are shifted, correlated, and decoded. Simulation and sea trial results indicate that this method demonstrates higher computational efficiency and improved decoding accuracy compared to traditional Doppler estimation matched filters under low signal-to-noise ratio conditions, and exhibits greater robustness under complex motion conditions. Full article
(This article belongs to the Section Ocean Engineering)
►▼ Show Figures

Figure 1

Figure 1
<p>Underwater acoustic communication system model.</p> Full article ">Figure 2
<p>Doppler estimation process.</p> Full article ">Figure 3
<p>Frame structure.</p> Full article ">Figure 4
<p>Time-varying channel impulse response (TVCIR) of KAU1 and KAU2, including both a 3D representation of the channel and its 2D projections.</p> Full article ">Figure 5
<p>The BER curves for different reception methods under two types of channels with varying motion states are presented: (<b>a</b>) represents the BER curve for constant velocity motion under the KAU1 channel; (<b>b</b>) represents the BER curve for constant velocity motion under the KAU2 channel; (<b>c</b>) represents the BER curve for variable velocity motion under the KAU1 channel; (<b>d</b>) represents the BER curve for variable velocity motion under the KAU2 channel.</p> Full article ">Figure 6
<p>(<b>a</b>) illustrates the variation in Doppler factors during variable velocity motion under two types of channels; (<b>b</b>) depicts the changes in acceleration during variable velocity motion under both channels (with identical acceleration variations).</p> Full article ">Figure 7
<p>Map of sea trial scope of Laoshan Bay.</p> Full article ">Figure 8
<p>(<b>a</b>) is the speed of the first group processes recorded by BDS. (<b>b</b>) is the first group processes channel impulse response.</p> Full article ">Figure 9
<p>The accuracy rates of each frame obtained by different methods: (<b>a</b>,<b>b</b>) correspond to the first sea trial, (<b>c</b>,<b>d</b>) correspond to the second sea trial, and (<b>e</b>,<b>f</b>) correspond to the third sea trial.</p> Full article ">Figure 10
<p>(<b>a</b>) is the CFO compensation estimated by CCF-MSS, (<b>b</b>) is the CFO compensation estimated by BDS output speed, (<b>c</b>) is the CFO compensation estimated by FDE-MSS (first group).</p> Full article ">Figure 11
<p>(<b>a</b>,<b>c</b>) is despreading correlation peaks of symbols 32 and 33 by CCF-MSS, (<b>b</b>,<b>d</b>) is despreading correlation peaks of symbols 32 and 33 by FDE-MSS (first frame of first group).</p> Full article ">Figure 12
<p>(<b>a</b>) is the speed of the second group processes recorded by BDS, (<b>b</b>) is the second group processes channel impulse response.</p> Full article ">Figure 13
<p>(<b>a</b>) is the CFO compensation estimated by CCF-MSS, (<b>b</b>) is the CFO compensation estimated by BDS output speed, (<b>c</b>) is the CFO compensation estimated by FDE-MSS (second group).</p> Full article ">Figure 14
<p>(<b>a</b>,<b>c</b>) is despreading correlation peaks of symbols 3 and 4 by CCF-MSS, (<b>b</b>,<b>d</b>) is despreading correlation peaks of symbols 3 and 4 by FDE-MSS (third frame of second group).</p> Full article ">Figure 15
<p>(<b>a</b>) is the speed of the third group processes recorded by BDS, (<b>b</b>) is the third group processes channel impulse response.</p> Full article ">Figure 16
<p>(<b>a</b>) is the CFO compensation estimated by CCF-MSS, (<b>b</b>) is the CFO compensation estimated by BDS output speed, (<b>c</b>) is the CFO compensation estimated by FDE-MSS (third group).</p> Full article ">Figure 17
<p>(<b>a</b>,<b>c</b>) is despreading correlation peaks of symbols 21 and 31 by CCF-MSS, (<b>b</b>,<b>d</b>) is despreading correlation peaks of symbols 21 and 31 by FDE-MSS (thirteen frame of third group).</p> Full article ">Figure 18
<p>BER curves of sea trial data with random ocean noise added under different motion states.</p> Full article ">
27 pages, 7716 KiB  
Article
An Innovative Online Adaptive High-Efficiency Controller for Micro Gas Turbine: Design and Simulation Validation
by Rui Yang, Yongbao Liu, Xing He and Zhimeng Liu
J. Mar. Sci. Eng. 2024, 12(12), 2150; https://doi.org/10.3390/jmse12122150 - 25 Nov 2024
Abstract
In this article, an innovative online adaptive high-efficiency control strategy is proposed to improve the power generation efficiency of a marine micro gas turbine under partial load. Firstly, a mathematical model of the micro-gas turbine is established, and a control strategy consisting of [...] Read more.
In this article, an innovative online adaptive high-efficiency control strategy is proposed to improve the power generation efficiency of a marine micro gas turbine under partial load. Firstly, a mathematical model of the micro-gas turbine is established, and a control strategy consisting of an on-board prediction model and an online update model is proposed. To evaluate the performance changes of the gas turbine, we applied deep learning techniques to enhance the extreme learning machine (ELM) algorithm, resulting in the development of a high-precision, high-real-time deep extreme learning machine (DL_ELM) prediction model. This model effectively monitors changes in the gas turbine’s performance. Furthermore, an online time-series deep extreme learning machine with a dynamic forgetting factor (DFF_DL_OSELM) model is designed to achieve the real-time tracking of performance variations. When the DL_ELM model detects a gas turbine’s performance change, a particle swarm optimization (PSO) algorithm is employed to iteratively calculate the DFF_DL_OSELM model, determining the optimal speed control scheme to ensure the gas turbine operates at maximum efficiency. To validate the superiority of the proposed control strategy, a comparison is made with traditional high-efficiency control strategies based on polynomial fitting and BP neural networks. The results demonstrate that although all three strategies can achieve efficient operation under constant conditions, traditional strategies fail to identify and adjust to performance changes in real time, leading to decreased control performance and potential engine damage as engine characteristics degrade. In contrast, the proposed online adaptive control strategy dynamically adjusts the speed control plan based on performance degradation, ensuring that the gas turbine operates efficiently while keeping the turbine inlet and exhaust temperatures within safe limits. Full article
(This article belongs to the Section Ocean Engineering)
►▼ Show Figures

Figure 1

Figure 1
<p>Design stations of the recuperated micro gas turbine.</p> Full article ">Figure 2
<p>Maps of the compressor. (<b>a</b>) Air mass flow rate vs. compressor pressure ratio. (<b>b</b>) Air mass flow rate vs. compressor efficiency.</p> Full article ">Figure 3
<p>Maps of the turbine. (<b>a</b>) Gas mass flow rate vs. turbine expansion ratio. (<b>b</b>) Gas mass flow rate vs. turbine efficiency.</p> Full article ">Figure 4
<p>Flow chart of gas turbine simulation calculation.</p> Full article ">Figure 5
<p>MGT experimental unit.</p> Full article ">Figure 6
<p>Comparison of the 100 kW MGT experimental and simulation data. (<b>a</b>) Output power. (<b>b</b>) Compressor outlet temperature. (<b>c</b>) Compressor outlet pressure. (<b>d</b>) Turbine outlet temperature.</p> Full article ">Figure 7
<p>Gas turbine variable-speed and constant-speed control. (<b>a</b>) Rotational speed curve. (<b>b</b>) Efficiency curve.</p> Full article ">Figure 8
<p>Optimal speed regulation control block diagram.</p> Full article ">Figure 9
<p>Block diagram of speed optimization control law.</p> Full article ">Figure 10
<p>Effect of ambient temperature on the optimum speed value of the MGT. (<b>a</b>) High-efficiency optimal speed curve. (<b>b</b>) Efficiency improvement value.</p> Full article ">Figure 11
<p>Block diagram of optimal speed regulation control based on BP neural network.</p> Full article ">Figure 12
<p>Optimal speed curve with degraded component performance. (<b>a</b>) Compressor degradation. (<b>b</b>) Turbine degradation.</p> Full article ">Figure 13
<p>Structure of online adaptive high-efficiency optimal speed control.</p> Full article ">Figure 14
<p>Deep network with <span class="html-italic">Q</span> hidden layers.</p> Full article ">Figure 15
<p>Block diagram of DL_ELM algorithm.</p> Full article ">Figure 16
<p>Dynamic forgetting factor adjustment law.</p> Full article ">Figure 17
<p>Predictive effect of DL_ELM algorithm. (<b>a</b>) Fitted graph of <math display="inline"><semantics> <mrow> <msub> <mi>T</mi> <mrow> <mi>t</mi> <mo>,</mo> <mi>i</mi> <mi>n</mi> </mrow> </msub> </mrow> </semantics></math>. (<b>b</b>) Relative error of <math display="inline"><semantics> <mrow> <msub> <mi>T</mi> <mrow> <mi>t</mi> <mo>,</mo> <mi>i</mi> <mi>n</mi> </mrow> </msub> </mrow> </semantics></math>. (<b>c</b>) Fitted graph of <math display="inline"><semantics> <mrow> <msub> <mi>T</mi> <mrow> <mi>t</mi> <mo>,</mo> <mi>o</mi> <mi>u</mi> <mi>t</mi> </mrow> </msub> </mrow> </semantics></math>. (<b>d</b>) Relative error of <math display="inline"><semantics> <mrow> <msub> <mi>T</mi> <mrow> <mi>t</mi> <mo>,</mo> <mi>o</mi> <mi>u</mi> <mi>t</mi> </mrow> </msub> </mrow> </semantics></math>. (<b>e</b>) Fitted graph of <math display="inline"><semantics> <mrow> <msub> <mi>N</mi> <mrow> <mi>o</mi> <mi>u</mi> <mi>t</mi> </mrow> </msub> </mrow> </semantics></math>. (<b>f</b>) Relative error of <math display="inline"><semantics> <mrow> <msub> <mi>N</mi> <mrow> <mi>o</mi> <mi>u</mi> <mi>t</mi> </mrow> </msub> </mrow> </semantics></math>.</p> Full article ">Figure 18
<p>High-efficiency operating curves of MGT under constant environmental conditions. (<b>a</b>) Output power curve. (<b>b</b>) Speed planning curve. (<b>c</b>) Efficiency curve. (<b>d</b>) Fuel flow rate curve. (<b>e</b>) Turbine inlet temperature curve. (<b>f</b>) Turbine outlet temperature curve.</p> Full article ">Figure 19
<p>On-board prediction model error.</p> Full article ">Figure 20
<p>MAPE values of DL_ELM prediction model under different turbine degradation levels.</p> Full article ">
36 pages, 10546 KiB  
Article
Shore-Side Downfall Pressures Due to Waves Impacting a Vertical Seawall: An Experimental Study
by Annelie Baines, Lee S. Cunningham and Benedict D. Rogers
J. Mar. Sci. Eng. 2024, 12(12), 2149; https://doi.org/10.3390/jmse12122149 - 25 Nov 2024
Abstract
As part of an investigation into downfall impacts from violent overtopping waves, experimental data are presented for the impact pressures and forces generated by regular and focused waves breaking onto a vertical wall and impacting a landward horizontal deck at a scale of [...] Read more.
As part of an investigation into downfall impacts from violent overtopping waves, experimental data are presented for the impact pressures and forces generated by regular and focused waves breaking onto a vertical wall and impacting a landward horizontal deck at a scale of 1:38. Particular attention is given to the wave-by-wave uprush and impact downfall events. By selecting regular and focused wave conditions that produce impacts, new trends are identified for violent downfall phenomena that could easily be underestimated in current practice. The characteristics of the downfall impacts are investigated and three different types of downfall impact are identified and discussed. Using a Wavelet Filter to denoise the signal from pressure probes without losing the peak impact pressures or introducing a phase shift, the distinctive features and dynamic behaviours of the white-water impacts are considered, and it is shown that downfall pressure magnitudes of 30–40 ρgH are regularly achieved. Dynamic impulse times of the events are also presented with higher-impact events generally relating to shorter impulse times, highlighting the dynamic character of these impacts. The largest downfall pressures are found to occur further from the vertical wall than previously measured. Importantly, the spray travelling furthest from the point of the initial wave impact on the vertical wall causes some of the largest downfall pressures on the deck. The paper concludes that, while the dataset is small, there are strong indications that the effects of these types of impacts are structurally significant and present a risk to infrastructure located landward of seawalls. Full article
(This article belongs to the Section Coastal Engineering)
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<p>Breaking waves at a vertical seawall, Norbreck, Blackpool, UK, 13 November 2020: plume formation (<b>top</b>), resulting downfall on landward deck (<b>bottom</b>). Droplet dispersal is clearly evident.</p> Full article ">Figure 2
<p>Stages of spray formation (from Case H4T2 introduced later): (<b>a</b>) Sheet formation directly after wave impact. (<b>b</b>) Sheet breakup, with the heavier elements starting to fall back towards the structure. (<b>c</b>) Droplet breakup: separation of the droplets from the remaining sheet. (<b>d</b>) Droplet downfall and impact on deck.</p> Full article ">Figure 3
<p>Schematic of Experimental Set-up, Plan (<b>top</b>), Section (<b>bottom</b>).</p> Full article ">Figure 4
<p>Sketch of the model (not to scale): (<b>a</b>) side view, (<b>b</b>) front of structure viewed from offshore showing probes PF1, PF2, PF3, and (<b>c</b>) plan view of deck probes. Pressure probes in use are shown in green. Locations in red denote probe locations that were sealed using PVC stoppers.</p> Full article ">Figure 5
<p>The University of Manchester wave flume. (<b>a</b>) Side view with beach in situ. (<b>b</b>) View of flume from wavemaker. (<b>c</b>) Model structure with pressure probes arrangement.</p> Full article ">Figure 6
<p>Wavelet Filter vs. Fourier Transform filter. (<b>a</b>) Raw measured signal. (<b>b</b>) Low Pass FFT filter at 20 Hz. (<b>c</b>) Fifth order Wavelet Filter. Forces are unscaled.</p> Full article ">Figure 7
<p>Three consecutive waves’ time-histories from H4T2 (<b>a</b>) Vertical wall impact pressures (PF1, PF2, PF3); (<b>b</b>) Horizontal deck impact pressures (PD12, PD13, PD23, PD33, PD32); and (<b>c</b>) Measured surface-elevation.</p> Full article ">Figure 8
<p>Typical profile type 1 from H4T2. Higher impact pressure associated with a lower downfall pressure. <b>Left</b>: vertical pressure–time profile, <b>right</b>: deck pressure–time profile.</p> Full article ">Figure 9
<p>Typical profile type 2 from H4T2. Low impact pressure associated with higher downfall pressure. <b>Left</b>: vertical pressure–time profile, <b>right</b>: deck pressure–time profile.</p> Full article ">Figure 10
<p>Typical profile type 3 from H4T2. Long dynamic impulse on deck (PD23). <b>Left</b>: vertical pressure–time profile, <b>right</b>: deck pressure–time profile.</p> Full article ">Figure 11
<p>Peak recorded Face Pressures (FP1, FP2 and FP3) for each wave plotted with the corresponding Peak recorded Deck Pressures for PD12, PD13, PD23, PD33, and PD32 separated by wave height for T2, coloured by deck pressure probes. (<b>a</b>) H1T2, (<b>b</b>) H2T2, (<b>c</b>) H3T2, (<b>d</b>) H4T2, (<b>e</b>) H5T2, (<b>f</b>) H6T2, (<b>g</b>) H7T2.</p> Full article ">Figure 12
<p>Diagram of the identification of the dynamic rise time. Solid line: Filtered pressure using Wavelet Filter Dashed: 20 Hz Fourier Filter applied to the Wavelet filtered results. Circles: intercepts identified as start and end of dynamic impulse.</p> Full article ">Figure 13
<p>Normalised pressures on the deck plotted against the time of the dynamic impact in ms for T4 cases.</p> Full article ">Figure 14
<p>Normalised pressures on the deck plotted against the normalised pressure probe position, <math display="inline"><semantics> <mrow> <mi>x</mi> <mo>/</mo> <msub> <mrow> <mi>H</mi> </mrow> <mrow> <mi>i</mi> </mrow> </msub> </mrow> </semantics></math>, for each wave height. Dashed line on each plot represents the location of the first row of probes, which were not used in the final tests shown here. Separated by wave height (T2) (<b>a</b>) H1T2, (<b>b</b>) H2T2, (<b>c</b>) H3T2, (<b>d</b>) H4T2, (<b>e</b>) H5T2, (<b>f</b>) H6T2, and (<b>g</b>) H7T2.</p> Full article ">Figure 15
<p>Free-surface elevation for R1 to R5 (test series FG1), superimposed for WG1.</p> Full article ">Figure 16
<p>Pressure–time plots for face pressures for R2, R3, R4 (repetition 2, 3, and 4) of FG3, superimposed for PF1.</p> Full article ">Figure 17
<p>Pressure–time plots for deck pressures for R2, R3, and R4 of FG3, superimposed and separated by pressure probe: (<b>a</b>) PD12, (<b>b</b>) PD13, (<b>c</b>) PD32, and (<b>d</b>) PD33.</p> Full article ">Figure 18
<p>Typical profile type 1 (FG1) at T = 26.5 s <b>Left</b>: Face pressure at PF1, <b>Right</b>: Deck impact pressure profile showing PD12, PD13, PD33, and PD32.</p> Full article ">Figure 19
<p>Typical profile type 2 (FG1) at T = 217.2 s <b>Left</b>: Face pressure at PF1, <b>Right</b>: Deck impact pressure profile showing PD12, PD13, PD33, and PD32.</p> Full article ">Figure 20
<p>Typical profile type 3 (FG1) at T = 153.4 s. <b>Left</b>: Face pressure at PF1, <b>Right</b>: Deck impact pressure profile showing PD12, PD13, PD33, and PD32.</p> Full article ">Figure 21
<p>Images captured of the peak wave of the focused group FG1 (<b>a</b>,<b>b</b>) plunging wave breaking at the focal point (toe of beach). (<b>c</b>) plunging wave toe impacting with structure. (<b>d</b>–<b>f</b>) Stages of spray formation: (<b>d</b>) Sheet formation directly after wave impact. (<b>e</b>) Sheet breakup, with the heavier elements starting to fall back towards the structure. (<b>f</b>) Droplet breakup: separation of the droplets from the remaining sheet.</p> Full article ">
21 pages, 5239 KiB  
Article
Influence of Tropical Cyclones and Cold Waves on the Eastern Guangdong Coastal Hydrodynamics: Processes and Mechanisms
by Yichong Zhong, Fusheng Luo, Yunhai Li, Yunpeng Lin, Jia He, Yuting Lin, Fangfang Shu and Binxin Zheng
J. Mar. Sci. Eng. 2024, 12(12), 2148; https://doi.org/10.3390/jmse12122148 - 25 Nov 2024
Abstract
In response to the intensification of global warming, extreme weather events, such as tropical cyclones (TCs) and cold waves (CWs) have become increasingly frequent near the eastern Guangdong coast, significantly affecting the structure and material transport of coastal waters. Based on nearshore-measured and [...] Read more.
In response to the intensification of global warming, extreme weather events, such as tropical cyclones (TCs) and cold waves (CWs) have become increasingly frequent near the eastern Guangdong coast, significantly affecting the structure and material transport of coastal waters. Based on nearshore-measured and remote sensing reanalysis data in the winter of 2011 and summer of 2012 on the eastern Guangdong coast, this study analyzed the nearshore hydrodynamic evolution process, influencing mechanism, and marine environmental effects under the influence of TCs and CWs, and further compared the similarities and differences between the two events. The results revealed significant seasonal variations in the hydrological and meteorological elements of the coastal waters, which were disrupted by the passage of TCs and CWs. The primary influencing factors were TC track and CW intensity. The current structure changed significantly during the TCs and CWs, with the TC destroying the original upwelling current and the CW affecting the prevailing northeastward current. Wind is one of the major forces driving nearshore hydrodynamic processes. According to the synchronous analysis of research data, the TC-induced water level rise is primarily attributed to the combined effects of wind stress curl and the Ekman effect, whereas the water level rise associated with CW is primarily linked to the Ekman effect. The water transport patterns during the TC and CW differed, with transport concentrated on the right side of the TC track and within the coastal strong-wind zones, respectively. Additionally, the temporal frequency domain of wavelet analysis highlighted the distinct nature of TC and CW signals, with 1–3 d and 4–8 d, respectively, and with TC signals being short-lived and rapid compared to the more sustained CW signals. This study enhances our understanding of the response of coastal hydrodynamics to extreme weather events on the eastern Guangdong coast, and the results can provide references for disaster management and protection of nearshore ocean engineering under extreme events. Full article
(This article belongs to the Section Physical Oceanography)
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<p>(<b>a</b>) Topographic map of the NSCS (GCC signifies the Guangdong Coastal Current; SCSWC signifies the Warm Current of the South China Sea; SCS, TWS, and NW Pacific signify the South China Sea, Taiwan Strait, and Northwest Pacific Ocean, respectively; the red box signifies the study area; blue, green, and yellow dots signify the tracks of TCs Talim and Doksuri, and the color and size of the dots signify the maximum wind speed and minimum central pressure of TCs, respectively; the blue arrows signify the direction of the CW). (<b>b</b>) Topographic map of the study area (red dots signify the location of each seabed-based observation station (W1 and W2)); the magenta inverted triangle signifies the location of the land meteorological observation station (F1); and the abscissa and ordinate axes of the coordinate system of station W1, W2, and F1 are along the shore (u’) and perpendicular to the shore (v’), respectively). Water depth data were obtained from the ETOPO Global Relief Model (public access). ETOPO Global Relief Model | National Centers for Environmental Information (NCEI) (noaa.gov), access on 20 June 2023).</p> Full article ">Figure 2
<p>(<b>a</b>–<b>i</b>) The sea surface wind field (SSWF), sea surface current field (SSCF), and sea surface height anomaly (SSHA) distribution in the NSCS during the TC from 17 June to 3 July 2012 (black, red, and magenta arrows represent wind, current, and measured current vectors, respectively, and background color represents SSHA; (<b>a</b>–<b>c</b>) and (<b>f</b>–<b>h</b>) are the periods before, during, and after T1 and T2, respectively). SSWF, SSCF, and SSHA data were obtained from NCEP Climate Forecast System Version 2 Product (public access).</p> Full article ">Figure 3
<p>(<b>a</b>–<b>i</b>) The sea surface wind field (SSWF), sea surface current field (SSCF), and sea surface height anomaly (SSHA) distribution in the NSCS during the CW from 5 November to 27 December 2011 (black, red, and magenta arrows represent wind, current vectors, and measured current vectors, respectively, and background color represents SSHA; (<b>a</b>–<b>c</b>), (<b>d</b>–<b>f</b>), and (<b>g</b>–<b>i</b>) are the periods before, during, and after C1, C2, and C3, respectively). SSWF, SSCF, and SSHA data were obtained from NCEP Climate Forecast System Version 2 Product (public access).</p> Full article ">Figure 4
<p>Curves of summer and winter wind vector, air temperature, and air pressure at F1 station. (<b>a</b>) Summer wind vector. (<b>b</b>) Summer air temperature and air pressure. (<b>c</b>) Winter wind vector. (<b>d</b>) Winter air temperature and air pressure (the size and color of the vector arrow represent the wind speed, and the direction of the arrow represents the wind direction; red line represents the air temperature, and the blue line represents the air pressure; the red boxes in summer data represent TCs, and the blue boxes in winter data represent CWs).</p> Full article ">Figure 5
<p>Alongshore current velocity at stations W1 (<b>a</b>) and W2 (<b>b</b>); cross-shore current velocity at stations W1 (<b>c</b>) and W2 (<b>d</b>); echo intensity at stations W1 (<b>e</b>) and W2 (<b>f</b>) (the black dashed line represents the instrument change time; the white area represents the missing data; the magenta line represents the residual water level (RWL); the blue line represents the alongshore wind speed (AW); the black line represents the bottom-water temperature (BWT); and the red boxes represent the TC events).</p> Full article ">Figure 6
<p>(<b>a</b>) Alongshore current velocity at stations W1 and W2. (<b>b</b>) cross-shore current velocity at stations W1 and W2. (<b>c</b>) echo intensity at stations W1 and W2 (the black dashed line represents the instrument change time; the white area represents the missing data; the magenta line represents the residual water level (RWL); the blue line represents the alongshore wind speed (AW); the black line represents the bottom-water temperature (BWT); and the blue boxes represent the CW events).</p> Full article ">Figure 7
<p>Alongshore residual current velocity at stations W1 (<b>a</b>) and W2 (<b>b</b>) in summer; cross-shore residual current velocity at stations W1 (<b>c</b>) and W2 (<b>d</b>) in summer (the red boxes represent the TC events).</p> Full article ">Figure 8
<p>Alongshore residual current velocity at stations W1 (<b>a</b>) and W2 (<b>c</b>) in winter; cross-shore residual current velocity at stations W1 (<b>b</b>) and W2 (<b>d</b>) in winter (the blue boxes represent the CW events; the black dashed line represents the instrument change time).</p> Full article ">Figure 9
<p>Wavelet analysis transform of alongshore surface current at station W2 in summer. (<b>a</b>) Measured current CWT; (<b>b</b>) residual current CWT; (<b>c</b>) WTC of the wind and surface current; (<b>d</b>) XWT of the wind and surface current (the thick lines represent areas that have passed a 95% significance level test. The colors of the subfigure represent the signal energy. The relative phase relationship is also depicted in the last two panels with in-phase pointing to the right and anti-phase pointing to the left, and if the former leads the latter by 90°, it will point straight downward. The former represents wind, and the latter represents the current).</p> Full article ">Figure 10
<p>Wavelet analysis transform of alongshore surface current at stations W1 and W2 in winter. (<b>a</b>) Measured current CWT; (<b>b</b>) residual current CWT; (<b>c</b>) WTC of the wind and surface current; (<b>d</b>) XWT of the wind and surface current (the thick lines represent areas that have passed a 95% significance level test. The colors of the subfigure represent the signal energy. The relative phase relationship is also depicted in the last two panels with in-phase pointing to the right and anti-phase pointing to the left, and if the former leads the latter by 90°, it will point straight downward. The former represents wind, and the latter represents the current).</p> Full article ">Figure 11
<p>Ekman volume transport composite distribution in the NSCS. (<b>a</b>) During T1; (<b>b</b>) relaxation stage of TCs; (<b>c</b>) during T2; (<b>d</b>) during C1; (<b>e</b>) relaxation stage of CWs; (<b>f</b>) during C2; (<b>g</b>) Ekman volume transport curve in the study area. The red circles represent the TC circles; white lines represent the TC tracks; red boxes represent the TCs; and blue boxes represent the CWs.</p> Full article ">Figure 12
<p>Schematic of impacts of TCs and CWs on coastal marine environment in the NSCS. (<b>a</b>) T1 and T2 represent different tracks; red and yellow circles represent the radius of T1 and T2, respectively; the black arrows represent the mixing caused by wind stress. (<b>b</b>) C1, C2, and C3 represent the different intensities of the CW; the blue arrow and wind direction symbol represent the CW direction and wind speed. The horizontal background color represents the water depth. Water depth data were obtained from the ETOPO Global Relief Model (public access).</p> Full article ">
14 pages, 3278 KiB  
Article
Data-Driven Based Path Planning of Underwater Vehicles Under Local Flow Field
by Fengqiao Jin, Bo Cheng and Weilin Luo
J. Mar. Sci. Eng. 2024, 12(12), 2147; https://doi.org/10.3390/jmse12122147 - 25 Nov 2024
Abstract
Navigating through complex flow fields, underwater vehicles often face insufficient thrust to traverse particularly strong current areas, necessitating consideration of the physical feasibility of paths during route planning. By constructing a flow field database through Computational Fluid Dynamics (CFD) simulations of the operational [...] Read more.
Navigating through complex flow fields, underwater vehicles often face insufficient thrust to traverse particularly strong current areas, necessitating consideration of the physical feasibility of paths during route planning. By constructing a flow field database through Computational Fluid Dynamics (CFD) simulations of the operational environment, we were able to analyze local uncertainties within the flow field. Our investigation into path planning using these flow field data has led to the proposal of a hierarchical planning strategy that integrates global sampling with local optimization, ensuring both completeness and optimality of the planner. Initially, we developed an improved global sampling algorithm derived from RRT to attain nearly optimal theoretical feasible solutions on a global scale. Subsequently, we implemented corrective measures using directed expansion to address locally infeasible sections. The algorithm’s efficacy was theoretically validated, and simulated results based on real flow field environments were provided. Full article
(This article belongs to the Section Ocean Engineering)
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<p>Two paths planned using the classical RRT.</p> Full article ">Figure 2
<p>Two methods of representing uncertain flow fields: (<b>a</b>) approach referenced from the literature, and (<b>b</b>) representation method proposed in this paper.</p> Full article ">Figure 3
<p>Division of flow field area.</p> Full article ">Figure 4
<p>Content structure of article.</p> Full article ">Figure 5
<p>Feasible ranges between <math display="inline"><semantics> <mi>c</mi> </semantics></math> and <math display="inline"><semantics> <mi>v</mi> </semantics></math> under three different relationships. (<b>a</b>) <math display="inline"><semantics> <mrow> <mi>c</mi> <mo>=</mo> <mi>v</mi> </mrow> </semantics></math>, (<b>b</b>) <math display="inline"><semantics> <mrow> <mi>c</mi> <mo>&gt;</mo> <mi>v</mi> </mrow> </semantics></math>, (<b>c</b>) <math display="inline"><semantics> <mrow> <mi>c</mi> <mo>&lt;</mo> <mi>v</mi> </mrow> </semantics></math>.</p> Full article ">Figure 6
<p>Feasible range of nodes at the region’s edges and interior. (<b>a</b>) Feasible range of nodes; (<b>b</b>) expansion way of node.</p> Full article ">Figure 7
<p>Rapidly-exploring Random Tree.</p> Full article ">Figure 8
<p>Schematic diagram of optimization. (<b>a</b>) path shrinkage; (<b>b</b>) corner optimization.</p> Full article ">Figure 9
<p>Path quality comparison. (<b>a</b>) circular obstacle; (<b>b</b>) rectangular obstacle.</p> Full article ">Figure 10
<p>Relationship between step size (<span class="html-italic">d</span><sub>2</sub>) and computational time and path length.</p> Full article ">Figure 11
<p>Graph of planning results at <span class="html-italic">V</span> = 1.2 m/s. (<b>a</b>) Planning tasks in the downstream direction. (<b>b</b>) Planning tasks in the upstream direction.</p> Full article ">
15 pages, 5527 KiB  
Article
Sensitivity Assessment on Satellite Remote Sensing Estimates of Primary Productivity in Shelf Seas
by Xiaolong Zhao, Jianan Sun, Qingjun Fu, Xiao Yan and Lei Lin
J. Mar. Sci. Eng. 2024, 12(12), 2146; https://doi.org/10.3390/jmse12122146 - 25 Nov 2024
Viewed by 29
Abstract
The vertically generalized production model (VGPM) is one of the most important methods for estimating marine net primary productivity (PP) using remote sensing. However, different data sources and parameterization schemes of the input variables for the VGPM can introduce uncertainties to the model [...] Read more.
The vertically generalized production model (VGPM) is one of the most important methods for estimating marine net primary productivity (PP) using remote sensing. However, different data sources and parameterization schemes of the input variables for the VGPM can introduce uncertainties to the model results. This study compared the PP results from different data sources and parameterization schemes of three major input variables (i.e., chlorophyll-a concentration (Copt), euphotic depth (Zeu), and maximum photosynthetic rate (PoptB)) and evaluated the sensitivity of VGPM in the Yellow and Bohai Seas on the inputs. The results showed that the sensitivity in the annual mean PP was approximately 40%. Seasonally, the sensitivity was lowest in the spring (35%), highest in the winter (70%), and approximately 60% in the summer and autumn. Spatially, the sensitivity in nearshore water (water depth < 40 m) was more than 60% and around two times higher than that in deep water areas. Nevertheless, all VGPM results showed a decline trend in the PP from 2003 to 2020 in the Yellow and Bohai Seas. The influence of PoptB and Copt was important for the magnitude of annual mean PP. The PP seasonal variation pattern was highly related to the parameterization scheme of PoptB, whereas the spatial distribution was mostly sensitive to the data sources of Copt. Full article
(This article belongs to the Section Marine Environmental Science)
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<p>The bathymetry of the YBS. The gray dashed line is the 40-m isobath.</p> Full article ">Figure 2
<p>Climatological monthly variation in the primary productivity of the eight experiments in the YBS from 2003 to 2020.</p> Full article ">Figure 3
<p>(<b>a</b>) Climatological monthly variation in the average primary productivity of the eight experiments. (<b>b</b>) The monthly variation in the coefficients of variation (CV).</p> Full article ">Figure 4
<p>Interannual variations of the primary productivity of the 8 experiments in the YBS from 2003 to 2020.</p> Full article ">Figure 5
<p>The residuals of the EMD analysis on the monthly mean primary productivity for the 8 experiments in the YBS from 2003 to 2020 (subfigures (<b>a</b>–<b>h</b>) correspond to Experiments 1–8, respectively).</p> Full article ">Figure 6
<p>The spatial distribution of the climatological mean primary productivity of the 8 experiments in the YBS.</p> Full article ">Figure 7
<p>The spatial distribution of the standard deviation (<b>a</b>) and the CV (<b>b</b>) of the primary productivity for the 8 experiments in the YBS.</p> Full article ">Figure 8
<p>(<b>a</b>–<b>c</b>) Climatological monthly means of <math display="inline"><semantics> <mrow> <msub> <mi>C</mi> <mrow> <mi>o</mi> <mi>p</mi> <mi>t</mi> </mrow> </msub> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>Z</mi> <mrow> <mi>e</mi> <mi>u</mi> </mrow> </msub> </mrow> </semantics></math>, and <math display="inline"><semantics> <mrow> <msubsup> <mi>P</mi> <mrow> <mi>o</mi> <mi>p</mi> <mi>t</mi> </mrow> <mi>B</mi> </msubsup> </mrow> </semantics></math> of different sources and parameterization schemes, respectively. (<b>d</b>–<b>f</b>) Interannual variations from 2003 to 2020 of <math display="inline"><semantics> <mrow> <msub> <mi>C</mi> <mrow> <mi>o</mi> <mi>p</mi> <mi>t</mi> </mrow> </msub> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>Z</mi> <mrow> <mi>e</mi> <mi>u</mi> </mrow> </msub> </mrow> </semantics></math>, and <math display="inline"><semantics> <mrow> <msubsup> <mi>P</mi> <mrow> <mi>o</mi> <mi>p</mi> <mi>t</mi> </mrow> <mi>B</mi> </msubsup> </mrow> </semantics></math> of different sources or parameterization schemes, respectively.</p> Full article ">Figure 9
<p>The spatial distribution of the climatological mean <math display="inline"><semantics> <mrow> <msub> <mi>C</mi> <mrow> <mi>o</mi> <mi>p</mi> <mi>t</mi> </mrow> </msub> </mrow> </semantics></math> (<b>a</b>,<b>b</b>), <math display="inline"><semantics> <mrow> <msub> <mi>Z</mi> <mrow> <mi>e</mi> <mi>u</mi> </mrow> </msub> </mrow> </semantics></math> (<b>d</b>,<b>e</b>), and <math display="inline"><semantics> <mrow> <msubsup> <mi>P</mi> <mrow> <mi>o</mi> <mi>p</mi> <mi>t</mi> </mrow> <mi>B</mi> </msubsup> </mrow> </semantics></math> (<b>g</b>,<b>h</b>), of the different data sources or parameterization schemes and their differences (<b>c</b>,<b>f</b>,<b>i</b>), respectively.</p> Full article ">Figure 10
<p>Mean bias between the primary productivity of the VGPM (8 Exps.) and three alternative models (CAFE, CbPM, and Eppley-VGPM) and the observed values from Choi et al. in 1992 [<a href="#B31-jmse-12-02146" class="html-bibr">31</a>].</p> Full article ">
18 pages, 3781 KiB  
Article
A Multiscale Model to Assess Bridge Vulnerability Under Extreme Wave Loading
by Umberto De Maio, Fabrizio Greco, Paolo Lonetti and Paolo Nevone Blasi
J. Mar. Sci. Eng. 2024, 12(12), 2145; https://doi.org/10.3390/jmse12122145 - 25 Nov 2024
Viewed by 163
Abstract
A multiscale model is proposed to assess the impact of wave loading on coastal or inland bridges. The formulation integrates various scales to examine the effects of flooding actions on fluid and structural systems, transitioning from global to local representation scales. The fluid [...] Read more.
A multiscale model is proposed to assess the impact of wave loading on coastal or inland bridges. The formulation integrates various scales to examine the effects of flooding actions on fluid and structural systems, transitioning from global to local representation scales. The fluid flow was modeled using a turbulent two-phase level set formulation, while the structural system employed the 3D solid mechanics theory. Coupling between subsystems was addressed through an FSI formulation using the ALE moving mesh methodology. The proposed model’s validity was confirmed through comparisons with numerical and experimental data from the literature. A parametric study was conducted on wave load characteristics associated with typical flood or tsunami scenarios. This included verifying the wave load formulas from existing codes or refined formulations found in the literature, along with assessing the dynamic amplification’s effects on key bridge design variables and the worst loading cases involving bridge uplift and horizontal forces comparable to those typically used in seismic actions. Furthermore, a parametric study was undertaken to examine fluid flow and bridge characteristics, such as bridge elevation, speed, inundation ratio, and bearing system typology. The proposed study aims to identify the worst-case scenarios for bridge deck vulnerability. Full article
(This article belongs to the Special Issue Analysis and Design of Marine Structures)
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<p>General model and multiscale formulation for the fluid and structural systems.</p> Full article ">Figure 2
<p>Multiscale model: fluid (2D) and structural (3D) systems.</p> Full article ">Figure 3
<p>Schematic test configuration GM (<b>a</b>) and definition of the Reduced Model (RM) geometry (<b>b</b>).</p> Full article ">Figure 4
<p>Comparisons between experimental, numerical [<a href="#B38-jmse-12-02145" class="html-bibr">38</a>], and proposed model results in terms of (<b>a</b>) horizontal and (<b>b</b>) vertical forces.</p> Full article ">Figure 5
<p>Mesh discretization of Global Model (GM) and Reduced Model (RM): mesh discretization with details around the bridge.</p> Full article ">Figure 6
<p>DAFs of midspan centroid displacements (V<sub>2</sub>, V<sub>3</sub>) and hydrodynamic forces (F<sub>2</sub>, F<sub>3</sub>) vs. inundation ratio <span class="html-italic">H</span>* at fixed inlet speed ratio equal to <span class="html-italic">U</span>* = 0.5 (<b>a</b>) and <span class="html-italic">U</span>* = 0.7 (<b>b</b>).</p> Full article ">Figure 7
<p>DAFs of midspan centroid displacements (V<sub>2</sub>, V<sub>3</sub>) and hydrodynamic forces (F<sub>2</sub>, F<sub>3</sub>) vs. inlet speed factor <span class="html-italic">U</span>* at fixed inundation ratio equal to <span class="html-italic">H</span>* = 3 (<b>a</b>) or <span class="html-italic">H</span>* = 4 (<b>b</b>).</p> Full article ">Figure 8
<p>DAFs of midspan centroid transverse displacements V<sub>2</sub> (<b>a</b>) and V<sub>3</sub> (<b>b</b>) vs. deformability parameter (<span class="html-italic">s</span>*) for different values of inlet speed (<span class="html-italic">U</span>* = 0.3; 0.5; 0.7).</p> Full article ">Figure 9
<p>Time histories of the vertical reaction force R<sub>3</sub> normalized on the value under dead loads (Rg) for different values of inundation ratios <span class="html-italic">H</span>* (<b>a</b>) and inlet speed ratios equal to <span class="html-italic">U</span>* = 0.7 (<b>a</b>) and <span class="html-italic">U</span>* = 0.5 (<b>b</b>).</p> Full article ">Figure 10
<p>Time histories of the transverse reaction force (R<sub>2</sub>) normalized on the total weight of the deck (M<sub>b</sub>g) for different values of inlet speed ratio <span class="html-italic">U</span>* (<b>a</b>) and inundation ratios <span class="html-italic">H</span>* (<b>b</b>).</p> Full article ">Figure 11
<p>Maximum displacements along transverse (V<sub>2</sub>) or vertical (V<sub>3</sub>) normalized on the bridge length (<span class="html-italic">L<sub>B</sub></span>) vs. support stiffness ratio (<span class="html-italic">K</span>/<span class="html-italic">K<sub>ISO</sub></span>) for different values of inlet speed ratios (<span class="html-italic">U</span>*) at a fixed inundation ratio (<span class="html-italic">H</span>*).</p> Full article ">Figure 12
<p>Time histories of the vertical and transverse displacements (V<sub>3</sub>, V<sub>2</sub>) normalized on the maximum value: comparisons between isolated (I) or classical bridge configuration.</p> Full article ">Figure 13
<p>Reaction forces along transverse (R<sub>2</sub>) or vertical (R<sub>3</sub>) normalized girder weight (M<sub>b</sub>g) or hydrodynamic forces (F<sub>2</sub>, F<sub>3</sub>) vs. support stiffness ratio (<span class="html-italic">K</span>/<span class="html-italic">K<sub>ISO</sub></span>) at a fixed inundation ratio (<span class="html-italic">H</span>*) and inlet speed at <span class="html-italic">U</span>* = 0.5 (<b>a</b>) and <span class="html-italic">U</span>* = 0.5 (<b>b</b>).</p> Full article ">Figure A1
<p>Maximum tsunami-induced forces calculated by the present model with different mesh sizes in the main calculation domain.</p> Full article ">
16 pages, 1631 KiB  
Article
Assessment of Vessel Mooring Conditions Using Satellite Navigation System Real-Time Kinematic Application
by Ludmiła Filina-Dawidowicz, Vytautas Paulauskas, Donatas Paulauskas and Viktoras Senčila
J. Mar. Sci. Eng. 2024, 12(12), 2144; https://doi.org/10.3390/jmse12122144 - 25 Nov 2024
Viewed by 191
Abstract
When mooring a ship near the quay, it is important to monitor its speed at the time of contact with the quay to ensure the safe execution of the mooring operation. During mooring, the speed of the ship must not exceed specified values; [...] Read more.
When mooring a ship near the quay, it is important to monitor its speed at the time of contact with the quay to ensure the safe execution of the mooring operation. During mooring, the speed of the ship must not exceed specified values; therefore, it is very important to have the possibility to measure it with high accuracy and its appropriate adjustment. This article aims to present the assessment methodology of the forces acting on quay equipment when a ship is mooring using data provided by the real-time kinematic (RTK) application of the navigation satellite system, as well as a way to calculate the comparative index, which can show the advantages of using data provided by high-accuracy measurement systems compared with the typical one. The methodology of assessing the forces acting on quay equipment when the ship is mooring using data provided by high-precision systems was applied. To verify the developed methodology, the experiments were carried out on real ships and using a calibrated simulator. Based on the research results, it was stated that when planning and managing ships’ mooring operations in ports using data provided by the RTK application, it is possible to reduce the planned energy absorption of quay fenders up to 1.5–1.8 times while preparing the investment in quay development. The implementation of the developed methodology may contribute to the improvement of navigation safety when ships are mooring near the quays and thus allow for the reduction in the probability of undesirable situations occurring. The research results may be of interest to representatives of seaports authorities, traffic management offices, shipowners and other institutions involved in safe ships’ navigation in seaports and approaches to them. Full article
(This article belongs to the Special Issue Global Navigation Satellite System for Maritime Applications)
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<p>Navigation satellite system with RTK application (own elaboration based on [<a href="#B12-jmse-12-02144" class="html-bibr">12</a>]), where: GPS/GLONASS—Global Positioning System/Global Navigation Satellite System, VTS—Vessel Traffic Services, E.G. TIDE SENSOR—exemplary tide parameters sensor, E-SEA CAT–receiver of the satellite and reference station signals.</p> Full article ">Figure 2
<p>The algorithm of the research methodology.</p> Full article ">Figure 3
<p>Speed of ships contact with quay fenders during mooring operations depending on ships’ displacement, where red line—calculated speed of ships contacts with fenders, dots—data received using RTK application, and triangles—data received using DGPS.</p> Full article ">Figure 4
<p>The required absorption energy of the quay fenders (<math display="inline"><semantics> <mrow> <msub> <mrow> <mi>E</mi> </mrow> <mrow> <mi>a</mi> <mi>b</mi> <mi>s</mi> </mrow> </msub> <mo>×</mo> <mn>10</mn> </mrow> </semantics></math>, kNm) depending on the speed of the ship during contact with the quay fender and the displacement of the ship (<math display="inline"><semantics> <mrow> <mo>∆</mo> <mo>×</mo> <mn>1000</mn> </mrow> </semantics></math>, t) for different measurement accuracy: PIANC—PIANC recommendations; RTK—using the RTK application; DGPS—using the DGPS.</p> Full article ">Figure 5
<p>The visualisation of experiment carried out by simulator using data provided by RTK application (own elaboration based on [<a href="#B12-jmse-12-02144" class="html-bibr">12</a>]).</p> Full article ">Figure 6
<p>Parameters of POST PANAMAX tanker movement during mooring operation using RTK system (own elaboration based on [<a href="#B12-jmse-12-02144" class="html-bibr">12</a>]), where red line—transverse speed, m/s; blue line—longitudinal speed, knots; green line—propulsion engine power, HP.</p> Full article ">Figure 7
<p>Comparative index calculated based on data achieved using RTK application (red line) and DGPS (green line) and based on experiments on real ships depending on ships displacement, where triangle—results achieved for bulk cargo ships using data provided by DGPS; circle—results achieved for bulk cargo ships using data provided by RTK application; hexagon—results achieved for container ships using data provided by DGPS; cross—results achieved for container ships using data provided by RTK application.</p> Full article ">
22 pages, 1344 KiB  
Article
Array Optimization for a Wave Energy Converter with Adaptive Resonance Using Dual Bayesian Optimization
by Aghamarshana Meduri and Heonyong Kang
J. Mar. Sci. Eng. 2024, 12(12), 2143; https://doi.org/10.3390/jmse12122143 - 24 Nov 2024
Viewed by 277
Abstract
A novel Dual Bayesian optimization strategy is formed for an array of wave energy converters with adaptive resonance to maximize the annual performance through the energy conversion processes from irregular waves to electricity. A wave energy converter with adaptive resonance changes the natural [...] Read more.
A novel Dual Bayesian optimization strategy is formed for an array of wave energy converters with adaptive resonance to maximize the annual performance through the energy conversion processes from irregular waves to electricity. A wave energy converter with adaptive resonance changes the natural frequency of power take-off dynamics for varying irregular waves, resulting in the maximum annual energy production. The first step of the two-step Dual Bayesian optimization determines the geometric layout of the array, which maximizes the first energy conversion to the total array excitation for irregular waves occurring annually. The second step optimizes the operational parameters of individual wave energy converters in the optimized array to maximize the power generation in varying sea states through simultaneous conversion to mechanical and electrical energy. The coupled hydrodynamics are solved in the frequency domain, and the power performance is evaluated by solving the Cummins’ equation in the time domain extended for multiple floating bodies, each strongly coupled with nonlinear power take-off dynamics. The proposed method is applied to a surface-riding wave energy converter, already optimized for single unit operation at individual sea states. Investigating two array layouts, linear and random, the optimized arrays after Step 1 increase the excitation spectral area by up to 40% relative to the single unit operation, indicating the synergy enhancing the first energy conversion. Subsequently, the dual-optimized linear layout attained a q-factor up to 1.13 in commonly occurring sea states, achieving improved average power generation in 60% of the evaluated sea states. The performance of the random layout exhibited the average power fluctuating along the wave spectra with a peak q-factor of 1.07. The individual adaptive resonance is confirmed in the optimized arrays, such that each surface-riding wave energy converter of both layouts adaptively resonates with the peak of the wave excitation spectra, maximizing the power generation for the different irregular waves. Full article
(This article belongs to the Special Issue Feature Papers on Marine Energy in 2024)
15 pages, 1897 KiB  
Article
Extent of Benthic Habitat Disturbance by Offshore Infrastructure
by Robert M. Cerrato, Roger D. Flood, Justin Bopp and Henry J. Bokuniewicz
J. Mar. Sci. Eng. 2024, 12(12), 2142; https://doi.org/10.3390/jmse12122142 - 24 Nov 2024
Viewed by 137
Abstract
The effects of the interaction between sandy, mobile, low-relief (sorted) bedforms and two sewage outfalls were investigated along the south shore of Long Island, NY. Sand bedforms at scales from ripples to ridges are common on continental shelves. In dynamic environments, these features [...] Read more.
The effects of the interaction between sandy, mobile, low-relief (sorted) bedforms and two sewage outfalls were investigated along the south shore of Long Island, NY. Sand bedforms at scales from ripples to ridges are common on continental shelves. In dynamic environments, these features can migrate 10s to 100s of meters per year, especially during storms. Beyond engineering considerations, little is known of the interaction between these mobile features and anthropogenic structures. Modification of bedform topography and sediment grain-size distribution can be expected to alter the species composition, abundance, and diversity of the benthic community. At the study site, the interaction increased the scour of modern fine- to medium-grained sediments extending out to a kilometer and uncovered coarser-grained late Pleistocene sediments. This alteration of the seafloor in turn resulted in changes in composition, higher abundance, and lower diversity in the species assemblage found in the impacted area. The most advantaged species was Pseudunciola obliquua, a sightless, tube-building, surface deposit-feeding amphipod that is known to prefer a dynamic coarse sand habitat. Overall, the ecological effects of artificial structures on a wave-dominated seabed with sorted bedforms have not been adequately assessed. In particular, and of great importance, is the pending large-scale development of wind farms off the East Coast of the U.S. Full article
(This article belongs to the Special Issue Morphological Changes in the Coastal Ocean)
17 pages, 7457 KiB  
Article
An Assessment of the Tipping Point Behavior for Shoreline Retreat: A PCR Model Application at Vung Tau Beach, Vietnam
by Xiaoting Wang, Ali Dastgheib, Johan Reyns, Fan Li, Trang Minh Duong, Weiguo Zhang, Qinke Sun and Roshanka Ranasinghe
J. Mar. Sci. Eng. 2024, 12(12), 2141; https://doi.org/10.3390/jmse12122141 - 24 Nov 2024
Viewed by 232
Abstract
Storm waves and rising sea levels pose significant threats to low-lying coastal areas, particularly sandy beaches, which are especially vulnerable. The research on the long-time-scale changes in sandy coasts, especially the identification of tipping points in the shoreline-retreat rate, is limited. Vung Tau [...] Read more.
Storm waves and rising sea levels pose significant threats to low-lying coastal areas, particularly sandy beaches, which are especially vulnerable. The research on the long-time-scale changes in sandy coasts, especially the identification of tipping points in the shoreline-retreat rate, is limited. Vung Tau beach, characterized by its low terrain and rapid tourism-driven economic growth, was selected as a typical study area to quantify the shoreline retreat throughout the 21st century under various sea-level rise (SLR) scenarios, and to identify the existence of tipping points by investigating the projected annual change in shoreline retreat (m/yr). This study employs the Probabilistic Coastline Recession (PCR) model, a physics-based tool specifically designed for long-term coastline change assessments. The results indicate that shoreline retreat accelerates over time, particularly after a tipping point is reached around 2050 in the SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios. Under the SSP5-8.5 scenario, the median retreat distance is projected to increase from 19 m in 2050 to 89 m by 2100, nearly a fourfold rise. In comparison, the retreat distances are smaller under the SSP1-2.6 and SSP2-4.5 scenarios, but the same accelerating trend is observed beyond 2050. These findings highlight the growing risks associated with sea-level rise, especially the rapid increase in exceedance probabilities for retreat distances by the end of the century. By 2100, the probability of losing the entire beach at Vung Tau is projected to be 22% under SSP5-8.5. The approach of identifying tipping points based on the PCR model presented here can be applied to other sandy coastal regions, providing critical references for timely planning and the implementation of adaptation measures. Full article
(This article belongs to the Special Issue Coastal Evolution and Erosion under Climate Change)
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<p>Map showing Vung Tau and its location in Vietnam. The solid red line represents the study area’s coastline. The land-use data are source from OpenStreetMap [<a href="#B24-jmse-12-02141" class="html-bibr">24</a>]. The coastal bathymetry is nearly 6.5 km off the coast, derived from the Southern Institute of Water Resources Research, Vietnam.</p> Full article ">Figure 2
<p>The wave rose of Vung Tau beach. The wave data are downloaded from the Copernicus Climate Date Store and are derived from ERA5 hourly reanalysis data from 1993 to 2022.</p> Full article ">Figure 3
<p>Vung Tau average beach profile.</p> Full article ">Figure 4
<p>Regional relative sea-level rise curves for the SSP1-2.6, SSP2-4.5, and SS5-8.5 scenarios, calculated using the approach given by Nicholls et al. [<a href="#B28-jmse-12-02141" class="html-bibr">28</a>] together with the IPCC AR6 sea-level projections.</p> Full article ">Figure 5
<p>The scheme of the PCR model implementation. <span class="html-italic">Hs</span>: maximum wave height in one storm, <span class="html-italic">T<sub>p</sub></span>: peak period associated with H, <span class="html-italic">Dur</span>: duration of storm, <span class="html-italic">Dir</span>: mean direction of storm, <span class="html-italic">S</span>: gap between two storms, <span class="html-italic">SLR</span>: sea-level rise, <span class="html-italic">CDF</span>: cumulative distribution function.</p> Full article ">Figure 6
<p>Storm identification of Vung Tau over a 30-year period from 1993 to 2022. Green represents the storm identification threshold, blue indicates all wave data, and red shows identified storm events.</p> Full article ">Figure 7
<p>Cumulative distribution curve of historical storm events from 1993 to 2022. Generalized extreme value distribution fitting (GEV-fit) of the maximum significant wave height (<b>a</b>) and storm duration (<b>b</b>).</p> Full article ">Figure 8
<p>Dependency distribution: maximum significant wave height and mean wave direction during the storm (<b>a</b>), and maximum significant wave height and storm duration (<b>b</b>). The blue circles represent 154 storm events from 1993 to 2022.</p> Full article ">Figure 9
<p>Joint probability model of the cumulative distribution function (CDF) of maximum wave height and storm duration in Vung Tau. The black circles represent 154 storm events from 1993 to 2022.</p> Full article ">Figure 10
<p>Linear distribution of maximum significant wave height and mean peak wave period during storms in Vung Tau.</p> Full article ">Figure 11
<p>The exceedance probability curves of cumulative shoreline retreat for 2050, 2080, and 2100 under the SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios. The positive values of shoreline retreat indicate coastal erosion and the negative values of shoreline retreat represent coastal accretion. The gray rectangle represents the road location.</p> Full article ">Figure 12
<p>The annual shoreline changes in the 3-year average position compared to the previous year’s shoreline position under the SSP1-2.6 (<b>a</b>), SSP2-4.5 (<b>b</b>), and SSP5-8.5 (<b>c</b>) scenarios.</p> Full article ">Figure 13
<p>The average annual change rate of P50 during the three time periods (2030–2050, 2051–2080, and 2081–2100) under the SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios.</p> Full article ">
18 pages, 1120 KiB  
Article
Concentric Compressive Behavior and Design of Stainless Steel–Concrete Double-Skin Composite Tubes Influenced by Dual Hydraulic Pressures
by Jian-Tao Wang, Yang Yang, Kai-Lin Yang, Deng-Long Hu, Long-Bo Xu and Jun-Xin Li
J. Mar. Sci. Eng. 2024, 12(12), 2140; https://doi.org/10.3390/jmse12122140 - 23 Nov 2024
Viewed by 387
Abstract
The external hydraulic pressure and internal medium pressure acting on submarine pipelines can lead to the coupling effect of active and passive constraints on the mechanical performance of steel–concrete double-skin composite tubes, resulting in a significantly different bearing capacity mechanism compared to terrestrial [...] Read more.
The external hydraulic pressure and internal medium pressure acting on submarine pipelines can lead to the coupling effect of active and passive constraints on the mechanical performance of steel–concrete double-skin composite tubes, resulting in a significantly different bearing capacity mechanism compared to terrestrial engineering. In this paper, the full-range concentric compressive mechanism of new-type stainless steel–concrete double-skin (SSCDS) composite tubes subjected to dual hydraulic pressure was analyzed by the finite element method. The influence of geometric–physical parameters at various water depths was discussed. The key results reveal that imposing dual hydraulic pressures significantly improves the confinement of double-skin tubes to encased concrete, resulting in a higher axial compressive strength and a non-uniform stress distribution; increasing the material strengths of concrete, outer tubes and inner tubes results in an approximately linear enhancement in axial bearing capacity; enhancing the diameter-to-thickness ratios of outer tubes and inner tubes can decrease the bearing capacity of SSCDS composite tubes; and the axial compression strength of SSCDS composite tubes with a higher hollow ratio of 0.849 tends to decrease with increasing outer hydraulic pressure. A practical method that integrates the effects of dual hydraulic pressures was developed and validated for the strength calculation of SSCDS composite tubes. This research provides fundamental guidelines for the application of pipe-in-pipe structures in deep-sea engineering. Full article
(This article belongs to the Special Issue Analysis and Design of Marine Structures)
13 pages, 4997 KiB  
Article
Numerical Study on the Influence of Drift Angle on Wave Properties in a Two-Layer Flow
by Xiaoxing Zhao, Liuliu Shi and Eryun Chen
J. Mar. Sci. Eng. 2024, 12(12), 2139; https://doi.org/10.3390/jmse12122139 - 23 Nov 2024
Viewed by 246
Abstract
This study examines the influence of drift angle on the wave and flow field generated by a submarine navigating through a density-stratified fluid. Employing a numerical methodology, this research computed the viscous flow field around the SUBOFF bare hull under conditions of oblique [...] Read more.
This study examines the influence of drift angle on the wave and flow field generated by a submarine navigating through a density-stratified fluid. Employing a numerical methodology, this research computed the viscous flow field around the SUBOFF bare hull under conditions of oblique shipping maneuvers. The analytical framework relies on the Reynolds-Averaged Navier–Stokes (RANS) equations, supplemented by the Re-Normalization Group (RNG) k-ε turbulence model and the Volume of Fluid (VOF) method. The initial phases of this study involved verifying grid convergence and the accuracy of the numerical methods used. Subsequently, numerical simulations were performed across a spectrum of drift angles while maintaining a fixed Froude number of Fn = 0.5, with submergence depths set at 1.1 D and 2.0 D. The analysis focused on the wave profiles at both the free surface and the internal surface. The results indicate that the presence of a drift angle produces significant alterations in the characteristics of the free surface and internal surface when compared with straight-ahead motion. Specifically, the asymmetry in the flow field is enhanced, and the variability in the roughness of the free surface is pronounced. Full article
(This article belongs to the Section Ocean Engineering)
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<p>Schematic of the DARPA SUBOFF bare hull model.</p> Full article ">Figure 2
<p>Schematic of the computational domain.</p> Full article ">Figure 3
<p>Grids in the vertical central plane.</p> Full article ">Figure 4
<p>Grids in proximity to the submarine’s surface.</p> Full article ">Figure 5
<p>Rankine ovoid model.</p> Full article ">Figure 6
<p>Comparison of numerical and experimental results [<a href="#B24-jmse-12-02139" class="html-bibr">24</a>].</p> Full article ">Figure 7
<p>Free surface wave of the Rankine ovoid.</p> Full article ">Figure 8
<p>Distribution of free surface waves at a submergence depth of h = 1.1 D.</p> Full article ">Figure 9
<p>Distribution of free surface waves at a submergence depth of h = 2.0 D.</p> Full article ">Figure 10
<p>Free surface wave profiles at different submergence depths.</p> Full article ">Figure 11
<p>Internal surface wave profiles at different submergence depths.</p> Full article ">Figure 12
<p>Lateral waveforms at different streamwise locations.</p> Full article ">Figure 13
<p>Distribution of surface pressure along the length of the submarine within the horizontal center plane.</p> Full article ">Figure 14
<p>Distribution of surface pressure along the length of the submarine within the vertical center plane.</p> Full article ">Figure 15
<p>Distributions of the convergence and divergence of surface velocity at the free surface.</p> Full article ">
25 pages, 4279 KiB  
Article
Dynamic Response Assessment of Floating Offshore Wind Turbine Mooring Systems with Different In-Line Tensioner Configurations Based on Fully Coupled Load Calculations
by Wenhua Li, Guanlin Du, Shanying Lin, Zhenju Chuang and Fei Wang
J. Mar. Sci. Eng. 2024, 12(12), 2138; https://doi.org/10.3390/jmse12122138 - 23 Nov 2024
Viewed by 179
Abstract
In-line tensioning technology has significantly reduced the cost barriers that previously hindered the expansion of the floating offshore wind industry. However, assessing the impact of in-line tensioners on the dynamic response of floating offshore wind turbines (FOWTs) lacks effectiveness, and the relevant mooring [...] Read more.
In-line tensioning technology has significantly reduced the cost barriers that previously hindered the expansion of the floating offshore wind industry. However, assessing the impact of in-line tensioners on the dynamic response of floating offshore wind turbines (FOWTs) lacks effectiveness, and the relevant mooring configuration specifications are not complete. Thus, a fully coupled calculation method is introduced in this paper to solve the relevant issues in mooring systems with in-line tensioners using a classic spar platform model. Three distinct design scenarios were selected to study the variation in mooring configurations of in-line tensioners along different mooring lines and at varied positions within each line. The potential occurrence of reverse tension phenomena was deliberated and assessed. We identified the varying tension patterns at the fairlead and in-line tensioner locations in mooring systems with in-line tensioners, and the influence of such variations on platform dynamics. The findings also demonstrate that the appropriate configuration of in-line tensioners should be selected to avoid the risk of reverse tension. This research has potential to contribute to the security and economy of the deployment of this emerging in-line mooring method. Full article
(This article belongs to the Section Ocean Engineering)
13 pages, 4339 KiB  
Article
Experimental Investigation on Wave Dissipation of Perforated Pipe Breakwater Under Regular Wave Conditions
by Shaopeng Yang, Lipeng Yang, Bing Shi, Jing Na and Yakun Guo
J. Mar. Sci. Eng. 2024, 12(12), 2137; https://doi.org/10.3390/jmse12122137 - 23 Nov 2024
Viewed by 286
Abstract
The permeable breakwater is an innovative, eco-friendly coastal protection structure that reduces wave impact while minimizing “dead water” and environmental harm. This study introduces a perforated pipe breakwater design with an increasing pipe diameter from top to bottom, evaluated through physical model tests [...] Read more.
The permeable breakwater is an innovative, eco-friendly coastal protection structure that reduces wave impact while minimizing “dead water” and environmental harm. This study introduces a perforated pipe breakwater design with an increasing pipe diameter from top to bottom, evaluated through physical model tests using transmission coefficient Kt and reflection coefficient Kr serving as the primary parameters. The results indicate that Kt decreases as the relative width (B/L), wave steepness (H/L), and relative water depth (h/L) increase, but rises with a steeper breakwater slope. When B/L exceeds 0.3, H/L surpasses 0.06, or the h/L ratio is greater than 0.3, Kt gradually declines until reaching a stable state, resulting in a more pronounced wave reduction. As B/L and H/L increase, the coefficient Kr initially drops, then rises. The slope ratio of 1:1.5 demonstrates the most effective wave energy dissipation, with primary dissipation occurring on the front slope. The mixed pipe diameter design shows superior wave absorption over a uniform diameter. Compared to a porous horizontal plate, the perforated pipe breakwater exhibits better wave absorption. These findings offer valuable guidance for designing eco-friendly coastal protection projects. Full article
(This article belongs to the Section Ocean Engineering)
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<p>Sketch of Hou’s design for a permeable pipe breakwater.</p> Full article ">Figure 2
<p>Sketch of experimental layout.</p> Full article ">Figure 3
<p>Experimental model for testing breakwaters.</p> Full article ">Figure 4
<p>Variation of <span class="html-italic">K<sub>t</sub></span> (<b>a</b>) and <span class="html-italic">K<sub>r</sub></span> (<b>b</b>) with different relative top widths of the breakwater (<span class="html-italic">B</span>/<span class="html-italic">L</span>).</p> Full article ">Figure 5
<p>Variation of <span class="html-italic">K<sub>t</sub></span> (<b>a</b>) and <span class="html-italic">K<sub>r</sub></span> (<b>b</b>) with various relative top widths (<span class="html-italic">B</span>/<span class="html-italic">L</span>) and slopes.</p> Full article ">Figure 6
<p>Sketch of breakwater with the same front and rear slope.</p> Full article ">Figure 7
<p>Sketch of breakwater with different front and rear slopes.</p> Full article ">Figure 8
<p>Variation of <span class="html-italic">K<sub>t</sub></span> and <span class="html-italic">K<sub>r</sub></span> with different <span class="html-italic">B</span>/<span class="html-italic">L</span> and slopes behind breakwater.</p> Full article ">Figure 9
<p>Variation of <span class="html-italic">K<sub>t</sub></span> and <span class="html-italic">K<sub>r</sub></span> with the relative top width of the breakwater for different pipe diameters (<span class="html-italic">H</span> = 4 cm).</p> Full article ">Figure 10
<p>Variation of <span class="html-italic">K<sub>t</sub></span> and <span class="html-italic">K<sub>r</sub></span> with different wave steepness (<span class="html-italic">H</span>/<span class="html-italic">L</span>).</p> Full article ">Figure 11
<p>Variation of <span class="html-italic">K<sub>t</sub></span> and <span class="html-italic">K<sub>r</sub></span> with different relative water depths (<span class="html-italic">h</span>/<span class="html-italic">L</span>).</p> Full article ">Figure 12
<p>Variation of <span class="html-italic">K<sub>t</sub></span> and <span class="html-italic">K<sub>r</sub></span> with the relative structure width of different breakwater conditions (<span class="html-italic">H</span> = 8 cm, <span class="html-italic">B</span> = 40 cm).</p> Full article ">
34 pages, 21017 KiB  
Article
Operation Analysis of the Floating Derrick for Offshore Wind Turbine Installation Based on Machine Learning
by Jia Yu, Honglong Li, Shan Wang and Xinghua Shi
J. Mar. Sci. Eng. 2024, 12(12), 2136; https://doi.org/10.3390/jmse12122136 - 22 Nov 2024
Viewed by 405
Abstract
To investigate the influencing factors on the operation of an offshore wind turbine installation ship, a neural network, as a machine-learning method, is built to predict and analyze the motion response of a floating derrick in the process of a lifting operation under [...] Read more.
To investigate the influencing factors on the operation of an offshore wind turbine installation ship, a neural network, as a machine-learning method, is built to predict and analyze the motion response of a floating derrick in the process of a lifting operation under an external environmental load. The numerical method for the double floating body, from the software SESAM/SIMA, is validated against the experiments. The numerical method is used to establish the floating derrick-lifting impeller model to obtain the motions of the ship and impeller and the coupling effect. Based on the numerical results, the BP neural network model is built to predict the ship’s operation. The results show that the BP neural network model for the floating derrick and impeller motion prediction is very feasible. Combined with the Rules for Lifting Appliances of Ships and Offshore Installations and the Noble Denton Guidelines for Marine Lifting Operations, the operation of the floating crane system can be determined based on the environmental parameters. Full article
(This article belongs to the Special Issue Impact of Ocean Wave Loads on Marine Structures)
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<p>Flowchart of the operation analysis of the floating derrick lifting the impeller.</p> Full article ">Figure 2
<p>Model of wind turbine impeller.</p> Full article ">Figure 3
<p>Definition of coordinate for two adjacent floating bodies.</p> Full article ">Figure 4
<p>Motion analysis diagram of floating derrick and lifting objective.</p> Full article ">Figure 5
<p>Structure diagram of BP neural network.</p> Full article ">Figure 6
<p>Layout of the moored floating derrick and the deck cargo ship.</p> Full article ">Figure 7
<p>Model of floating crane and impeller.</p> Full article ">Figure 8
<p>Numerical model and experiment model of double floating body system. (<b>a</b>) Numerical model of double floating body system. (<b>b</b>) Experiment model of double floating body system.</p> Full article ">Figure 9
<p>The motion response of the two barges in double floating body system by experiment and numerical method. (<b>a</b>) Maximum value results of barge 1. (<b>b</b>) Minimum value results of barge 1. (<b>c</b>) Error of barge 1. (<b>d</b>) Maximum value results of barge 2. (<b>e</b>) Minimum value results of barge 2. (<b>f</b>) Error of barge 2.</p> Full article ">Figure 10
<p>Added mass of the floating derrick. (<b>a</b>) Surge, (<b>b</b>) sway, (<b>c</b>) heave, (<b>d</b>) roll, (<b>e</b>) pitch, and (<b>f</b>) yaw.</p> Full article ">Figure 11
<p>Added mass of the deck cargo ship. (<b>a</b>) Surge, (<b>b</b>) sway, (<b>c</b>) heave, (<b>d</b>) roll, (<b>e</b>) pitch, and (<b>f</b>) yaw.</p> Full article ">Figure 11 Cont.
<p>Added mass of the deck cargo ship. (<b>a</b>) Surge, (<b>b</b>) sway, (<b>c</b>) heave, (<b>d</b>) roll, (<b>e</b>) pitch, and (<b>f</b>) yaw.</p> Full article ">Figure 12
<p>Radiation damping of the floating derrick. (<b>a</b>) Surge, (<b>b</b>) sway, (<b>c</b>) heave, (<b>d</b>) roll, (<b>e</b>) pitch, and (<b>f</b>) yaw.</p> Full article ">Figure 13
<p>Radiation damping of the cargo ship. (<b>a</b>) Surge, (<b>b</b>) sway, (<b>c</b>) heave, (<b>d</b>) roll, (<b>e</b>) pitch, and (<b>f</b>) yaw.</p> Full article ">Figure 14
<p>RAOs of the floating derrick. (<b>a</b>) Surge, (<b>b</b>) sway, (<b>c</b>) heave, (<b>d</b>) roll, (<b>e</b>) pitch, and (<b>f</b>) yaw.</p> Full article ">Figure 14 Cont.
<p>RAOs of the floating derrick. (<b>a</b>) Surge, (<b>b</b>) sway, (<b>c</b>) heave, (<b>d</b>) roll, (<b>e</b>) pitch, and (<b>f</b>) yaw.</p> Full article ">Figure 15
<p>RAOs of the cargo ship. (<b>a</b>) Surge, (<b>b</b>) sway, (<b>c</b>) heave, (<b>d</b>) roll, (<b>e</b>) pitch, and (<b>f</b>) yaw.</p> Full article ">Figure 16
<p>First-order wave force of the floating derrick. (<b>a</b>) Surge, (<b>b</b>) sway, (<b>c</b>) heave, (<b>d</b>) roll, (<b>e</b>) pitch, and (<b>f</b>) yaw.</p> Full article ">Figure 17
<p>First-order wave force of the cargo ship. (<b>a</b>) Surge, (<b>b</b>) sway, (<b>c</b>) heave, (<b>d</b>) roll, (<b>e</b>) pitch, and (<b>f</b>) yaw.</p> Full article ">Figure 18
<p>Second-order wave force of the floating derrick. (<b>a</b>) Surge, (<b>b</b>) sway, (<b>c</b>) heave, (<b>d</b>) roll, (<b>e</b>) pitch, and (<b>f</b>) yaw.</p> Full article ">Figure 19
<p>Second-order wave force of the cargo ship. (<b>a</b>) Surge, (<b>b</b>) sway, (<b>c</b>) heave, (<b>d</b>) roll, (<b>e</b>) pitch, and (<b>f</b>) yaw.</p> Full article ">Figure 19 Cont.
<p>Second-order wave force of the cargo ship. (<b>a</b>) Surge, (<b>b</b>) sway, (<b>c</b>) heave, (<b>d</b>) roll, (<b>e</b>) pitch, and (<b>f</b>) yaw.</p> Full article ">Figure 20
<p>Joint frequency distribution of the wave period and significant wave height in East China Sea.</p> Full article ">Figure 21
<p>Time history curves of the motions of the floating derrick and the impeller under different wind velocities. (<b>a</b>) The floating derrick, (<b>b</b>) the impeller.</p> Full article ">Figure 22
<p>Time history curve of the floating derrick and the lifting impeller under different wave heights. (<b>a</b>) The floating derrick, (<b>b</b>) the impeller.</p> Full article ">Figure 23
<p>Time history curve of motions of the floating derrick and impeller under different spectral peak periods. (<b>a</b>) The floating derrick, (<b>b</b>) the impeller.</p> Full article ">Figure 24
<p>Time history curves of the motions of floating derrick and impeller under different current velocities. (<b>a</b>) The floating derrick, (<b>b</b>) the impeller.</p> Full article ">Figure 25
<p>Amplitude of motion response of floating derrick and impeller under different lifting heights. (<b>a</b>) The floating derrick. (<b>b</b>) The lifting impeller.</p> Full article ">Figure 25 Cont.
<p>Amplitude of motion response of floating derrick and impeller under different lifting heights. (<b>a</b>) The floating derrick. (<b>b</b>) The lifting impeller.</p> Full article ">Figure 26
<p>Time history curves of the motion of the floating derrick and impeller under different boom rotation angles. (<b>a</b>) The floating derrick, (<b>b</b>) the impeller.</p> Full article ">Figure 27
<p>The change in model performance evaluation index under different numbers of neurons. (<b>a</b>) <span class="html-italic">W<sub>e</sub></span>. (<b>b</b>) <span class="html-italic">W<sub>f</sub></span>. (<b>c</b>) <span class="html-italic">W<sub>g</sub></span>.</p> Full article ">Figure 28
<p>The change in model performance evaluation index under different numbers of network layers. (<b>a</b>) <span class="html-italic">W<sub>e</sub></span>. (<b>b</b>) <span class="html-italic">W<sub>f</sub></span>. (<b>c</b>) <span class="html-italic">W<sub>g</sub></span>.</p> Full article ">Figure 29
<p>Model training performance under different samples: (<b>a</b>) 1500 sets of data, (<b>b</b>) 2000 sets of data, (<b>c</b>) 3000 sets of data, and (<b>d</b>) 4900 sets of data.</p> Full article ">Figure 29 Cont.
<p>Model training performance under different samples: (<b>a</b>) 1500 sets of data, (<b>b</b>) 2000 sets of data, (<b>c</b>) 3000 sets of data, and (<b>d</b>) 4900 sets of data.</p> Full article ">Figure 30
<p>Model training errors under different samples: (<b>a</b>) 1500 sets of data, (<b>b</b>) 2000 sets of data, (<b>c</b>) 3000 sets of data, and (<b>d</b>) 4900 sets of data.</p> Full article ">Figure 31
<p>Statistics of wave and wind on a wind farm in the East China Sea in December. (<b>a</b>) The wave. (<b>b</b>) The wind.</p> Full article ">Figure 32
<p>The amplitude of the roll and pitch of the floating derrick. (<b>a</b>) Roll and (<b>b</b>) pitch.</p> Full article ">Figure 33
<p>The amplitude of surge and heave of the lifting impeller. (<b>a</b>) Surge and (<b>b</b>) heave.</p> Full article ">Figure 34
<p>Operation analysis of the floating crane system.</p> Full article ">
24 pages, 5064 KiB  
Article
High-Precision Permeability Evaluation of Complex Carbonate Reservoirs in Marine Environments: Integration of Gaussian Distribution and Thomeer Model Using NMR Logging Data
by Hengyang Lv, Jianhong Guo, Baoxiang Gu, Yuhan Liu, Li Wang, Long Wang, Zuomin Zhu and Zhansong Zhang
J. Mar. Sci. Eng. 2024, 12(12), 2135; https://doi.org/10.3390/jmse12122135 - 22 Nov 2024
Viewed by 542
Abstract
Accurate evaluation of permeability parameters is critical for the exploration and development of oil and gas fields. Among the available techniques, permeability assessment based on nuclear magnetic resonance (NMR) logging data is one of the most widely used and precise methods. However, the [...] Read more.
Accurate evaluation of permeability parameters is critical for the exploration and development of oil and gas fields. Among the available techniques, permeability assessment based on nuclear magnetic resonance (NMR) logging data is one of the most widely used and precise methods. However, the rapid biochemical variations in marine environments give rise to complex pore structures and strong reservoir heterogeneity, which diminish the effectiveness of traditional SDR and Timur–Coates models. To address these challenges in complex carbonate reservoirs, this study proposes a high-precision permeability evaluation method that integrates the Gaussian distribution model with the Thomeer model for more accurate permeability calculations using NMR logging data. Multimodal Gaussian distributions more accurately capture the size and distribution of multiscale pores. In this study, we innovatively employ the Gaussian distribution function to construct NMR-derived pseudo-pore size distribution curves. Subsequently, Thomeer model parameters are derived from Gaussian distribution parameters, enabling precise permeability calculation. The application of this method to the marine dolomite intervals of the Asmari Formation, Section A, within Oilfield A in southeastern Iraq, demonstrates its superior performance under both bimodal and unimodal pore size distributions. Compared to traditional models, this approach significantly reduces errors, providing crucial support for the accurate evaluation of complex reservoirs and the development of hydrocarbon resources. Full article
(This article belongs to the Special Issue Petroleum and Gas Hydrate Exploration and Marine Geology)
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<p>The acquisition of nuclear magnetic resonance (NMR) logging data and mercury injection capillary pressure (MICP) experiments. (<b>a</b>) Schematic diagram of NMR logging; (<b>b</b>) NMR logging T2 distribution spectrum; (<b>c</b>) ① schematic diagram of coring sample, ② schematic diagram of plunger sample acquisition; (<b>d</b>) mercury injection instrument; (<b>e</b>) MICP experimental results schematic.</p> Full article ">Figure 2
<p>Comparison of pore size distribution curves and NMR logging spectra. (<b>a</b>) MICP pore size distribution curve; (<b>b</b>) NMR porosity distribution spectrum.</p> Full article ">Figure 3
<p>Fitting results and accuracy. (<b>a</b>–<b>d</b>) the pore size distribution processing results for core sample ①; (<b>e</b>–<b>h</b>) the processing results of the NMR distribution spectrum for core sample ①; (<b>i</b>–<b>l</b>) the pore size distribution processing results for core sample ②; (<b>m</b>–<b>p</b>) the processing results of the NMR distribution spectrum for core sample ②.</p> Full article ">Figure 4
<p>An analysis of the weight and mean. (<b>a</b>) comparison of MICP Gaussian weights and NMR Gaussian weights; (<b>b</b>) relationship between MICP Gaussian Mean and NMR Gaussian Mean.</p> Full article ">Figure 5
<p>An analysis of StdDev for large and small pores. (<b>a</b>) Fitting results of <math display="inline"><semantics> <mrow> <mi>log</mi> <msub> <mi>σ</mi> <mrow> <msub> <mn>1</mn> <mrow> <mrow> <mo>(</mo> <mrow> <mi>N</mi> <mi>M</mi> <mi>R</mi> </mrow> <mo>)</mo> </mrow> </mrow> </msub> </mrow> </msub> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>log</mi> <msub> <mi>σ</mi> <mn>1</mn> </msub> </mrow> </semantics></math> for large pores; (<b>b</b>) fitting results of <math display="inline"><semantics> <mrow> <mi>log</mi> <msub> <mi>σ</mi> <mrow> <msub> <mn>2</mn> <mrow> <mrow> <mo>(</mo> <mrow> <mi>N</mi> <mi>M</mi> <mi>R</mi> </mrow> <mo>)</mo> </mrow> </mrow> </msub> </mrow> </msub> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>log</mi> <msub> <mi>σ</mi> <mn>2</mn> </msub> </mrow> </semantics></math> for small pores.</p> Full article ">Figure 6
<p>Thomeer hyperbolic curve.</p> Full article ">Figure 7
<p>The effect of Thomeer parameters on the pore size distribution curve. (<b>a</b>) Characterization of displacement pressure (<math display="inline"><semantics> <mrow> <msub> <mi>P</mi> <mi>d</mi> </msub> </mrow> </semantics></math>) on pore throat size; (<b>b</b>) characterization of pore throat characteristics by geometric factors (<math display="inline"><semantics> <mi>G</mi> </semantics></math>); (<b>c</b>) invisible characterization of rock samples by maximum mercury injection volume (<math display="inline"><semantics> <mrow> <msub> <mi>B</mi> <mrow> <mi>v</mi> <mo>∞</mo> </mrow> </msub> </mrow> </semantics></math>).</p> Full article ">Figure 8
<p>Permeability calculation. (<b>a</b>) Fitting results of <math display="inline"><semantics> <mrow> <msub> <mi>P</mi> <mi>d</mi> </msub> </mrow> </semantics></math> and <math display="inline"><semantics> <mi>k</mi> </semantics></math>; (<b>b</b>) comparison of predicted <math display="inline"><semantics> <mi>k</mi> </semantics></math> and actual <math display="inline"><semantics> <mi>k</mi> </semantics></math>.</p> Full article ">Figure 9
<p>A flowchart of the permeability logging evaluation process.</p> Full article ">Figure 10
<p>Gaussian parameters and permeability calculation results. (<b>a</b>) Fitting Gaussian parameter results; (<b>b</b>) permeability results based on Gaussian parameters vs. core experiment results; (<b>c</b>) permeability results from the Timur–Coates model vs. core experiment results; (<b>d</b>) the permeability results from the SDR model vs. core experiment results; (<b>e</b>) the permeability results from the pore–permeability relationship vs. core experiment results.</p> Full article ">Figure 11
<p>A comparison of four permeability calculation models. (<b>a</b>) Permeability results based on Gaussian parameters versus actual values; (<b>b</b>) Permeability results from pore permeability relationships versus actual values; (<b>c</b>) Permeability results from the Timur-Coates model versus actual values; (<b>d</b>) Permeability results from the SDR model versus actual values.</p> Full article ">
23 pages, 8542 KiB  
Article
Graphics Processing Unit-Accelerated Propeller Computational Fluid Dynamics Using AmgX: Performance Analysis Across Mesh Types and Hardware Configurations
by Yue Zhu, Jin Gan, Yongshui Lin and Weiguo Wu
J. Mar. Sci. Eng. 2024, 12(12), 2134; https://doi.org/10.3390/jmse12122134 - 22 Nov 2024
Viewed by 287
Abstract
Computational fluid dynamics (CFD) has become increasingly prevalent in marine and offshore engineering, with enhancing simulation efficiency emerging as a critical challenge. This study systematically evaluates the application of graphics processing unit (GPU) acceleration technology in CFD simulation of propeller open water performance. [...] Read more.
Computational fluid dynamics (CFD) has become increasingly prevalent in marine and offshore engineering, with enhancing simulation efficiency emerging as a critical challenge. This study systematically evaluates the application of graphics processing unit (GPU) acceleration technology in CFD simulation of propeller open water performance. Numerical simulations of the VP1304 propeller model were performed using OpenFOAM v2312 integrated with the NVIDIA AmgX library. The research compared GPU acceleration performance against conventional CPU methods across various hardware configurations and mesh types (tetrahedral, hexahedral-dominant, and polyhedral). Results demonstrate that GPU acceleration significantly improved computational efficiency, with tetrahedral meshes achieving over 400% speedup in a 4-GPU configuration, while polyhedral meshes reached over 500% speedup with a fixed mesh count. Among the mesh types, hexahedral-dominant meshes performed best in capturing flow field details. The study also found that GPU acceleration does not compromise simulation accuracy, but its effectiveness is closely related to mesh type and hardware configuration. Notably, GPUs demonstrate more significant advantages when handling large-scale problems. These findings have important practical implications for improving propeller design processes and shortening product development cycles. Full article
(This article belongs to the Section Ocean Engineering)
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<p>CFD simulation process accelerated through GPU in OpenFOAM.</p> Full article ">Figure 2
<p>Geometric model of propeller VP1304. (<b>a</b>) Front view; (<b>b</b>) side view.</p> Full article ">Figure 3
<p>Numerical simulation domain for open water performance of VP1304 propeller.</p> Full article ">Figure 4
<p>Details of CFD mesh refinement.</p> Full article ">Figure 5
<p>Comparative illustration of three computational domain dimensions (small, medium, and large).</p> Full article ">Figure 6
<p>Different mesh types for CFD simulations. (<b>a</b>) Tetrahedral mesh; (<b>b</b>) hex-dominant mesh; (<b>c</b>) polyhedral mesh.</p> Full article ">Figure 7
<p>Comparison of open water performance of propeller between simulation results on different hardware platforms and experimental data.</p> Full article ">Figure 8
<p>Comparison of open water performance of propeller between simulation results using different grid types and experimental data, with a base size of 4.5 mm.</p> Full article ">Figure 9
<p>Pressure distribution contour plots for different mesh types. (<b>a</b>) Tetrahedral mesh; (<b>b</b>) hex-dominant mesh; (<b>c</b>) polyhedral mesh.</p> Full article ">Figure 10
<p>Vorticity distribution for different mesh types. (<b>a</b>) Tetrahedral mesh; (<b>b</b>) hex-dominant mesh; (<b>c</b>) polyhedral mesh.</p> Full article ">Figure 11
<p>Velocity distribution for different mesh types. (<b>a</b>) Tetrahedral mesh; (<b>b</b>) hex-dominant mesh; (<b>c</b>) polyhedral mesh.</p> Full article ">Figure 12
<p>Simulation time versus number of CPU cores for different mesh types. (<b>a</b>) Fixed mesh size (4.5 mm). (<b>b</b>) Fixed mesh count (3.3 million elements).</p> Full article ">Figure 13
<p><a href="#jmse-12-02134-f012" class="html-fig">Figure 12</a> speedup factor versus number of CPU cores for different element types. (<b>a</b>) Fixed mesh size (4.5 mm). (<b>b</b>) Fixed mesh count (3.3 million elements).</p> Full article ">Figure 14
<p>Simulation time versus number of GPUs for different mesh types. (<b>a</b>) Fixed mesh size (4.5 mm). (<b>b</b>) Fixed mesh count (3.3 million elements).</p> Full article ">Figure 15
<p>Speedup factor versus number of GPUs for different mesh types. (<b>a</b>) Fixed mesh size (4.5 mm). (<b>b</b>) Fixed mesh count (3.3 million elements).</p> Full article ">Figure 16
<p>Speedup of different numbers of GPUs compared to 32-core CPU with consistent mesh size.</p> Full article ">Figure 17
<p>Speedup of different numbers of GPUs compared to 32-core CPU with consistent mesh number.</p> Full article ">
24 pages, 8531 KiB  
Article
Optimization of a Dual-Channel Water-Cooling Heat Dissipation System for PMSM in Underwater Unmanned Vehicles Using a Multi-Objective Genetic Algorithm
by Wenlong Tian, Chen Zhang, Zhaoyong Mao and Bo Cheng
J. Mar. Sci. Eng. 2024, 12(12), 2133; https://doi.org/10.3390/jmse12122133 - 22 Nov 2024
Viewed by 278
Abstract
To minimize the temperature of the propulsion motor and reduce flow loss in the water-cooling structure during the operation of an underwater unmanned vehicle, this paper employs a multi-objective genetic algorithm to optimize the dimensions of the inner and outer dual-channel water-cooling structure [...] Read more.
To minimize the temperature of the propulsion motor and reduce flow loss in the water-cooling structure during the operation of an underwater unmanned vehicle, this paper employs a multi-objective genetic algorithm to optimize the dimensions of the inner and outer dual-channel water-cooling structure as well as the flow rate of the cooling water. Firstly, the influence of design variables on response variables was examined through sensitivity analysis. Subsequently, a model sample library for simulating the coupled temperature and flow fields of the motor was constructed, and a response surface model between the variables was developed. Finally, appropriate sample points were selected from the Pareto solution set to verify the validity of the optimization results through CFD simulation and error analysis. The sensitivity analysis results indicate that the cooling water flow rate had the greatest impact on both the maximum motor temperature and the flow losses of the water-cooling structure, with values of 77.79% and 99.84%, respectively. On the other hand, the optimal design parameters for the four dimensions of the channel and the cooling water flow rate were obtained. Compared with the initial dimensions of the water-cooling structure, the maximum temperature of the motor decreased from 332.86 K to 331.46 K. Simultaneously, the flow loss of the water-cooling structure decreased from 100.02 kPa to 59.58 kPa, with a maximum reduction rate of 40.43%. The optimization effect of the motor cooling system is significant, which provides valuable insights for system design under the premise of ignoring multi-objective interactions. Full article
(This article belongs to the Section Ocean Engineering)
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<p>Flow chart of multi-objective genetic algorithm optimization of the cooling system.</p> Full article ">Figure 2
<p>Schematic diagram of the cooling structure of the UUV propulsion motor: (<b>a</b>) overall structure of the UUV; (<b>b</b>) the structure of the dual-water-channel system; (<b>c</b>) the structure of the inner and outer water channel; (<b>d</b>) rectangular cross-section of the dual water channel.</p> Full article ">Figure 3
<p>Parameters involved in the water-cooling system: (<b>a</b>) cross-sectional and dimensional parameters of the dual-channel water-cooling structure; (<b>b</b>) range of values of the design variables for multi-objective optimization.</p> Full article ">Figure 4
<p>Mesh division: (<b>a</b>) mesh of the internal cooling structure of the motor; (<b>b</b>) mesh of the entire motor.</p> Full article ">Figure 5
<p>Validation of the numerical model: (<b>a</b>) validation result graph; (<b>b</b>) photograph of the tested motor.</p> Full article ">Figure 6
<p>Temperature field distribution of the water-cooling motor system: (<b>a</b>) the contour of the motor system’s temperature field; (<b>b</b>) the contour of the temperature field for the heated parts of the motor system.</p> Full article ">Figure 7
<p>Results of the cooling water in the dual channel: (<b>a</b>) the contour of the temperature field for the cooling water; (<b>b</b>) the contour of the cooling water pressure.</p> Full article ">Figure 8
<p>Sensitivity analysis of design variables to response variables.</p> Full article ">Figure 9
<p>Correlation fitting results of cooling water flow rate with response variables: (<b>a</b>) the water-cooling structure flow loss <span class="html-italic">P<sub>w</sub></span>; (<b>b</b>) the maximum temperature of motor <span class="html-italic">T<sub>max</sub></span>.</p> Full article ">Figure 10
<p>Sample point distribution of design variables.</p> Full article ">Figure 11
<p>The fitting results of response variables in the RSM: (<b>a</b>) comparison of fits for <span class="html-italic">T<sub>max</sub></span>; (<b>b</b>) comparison of fits for <span class="html-italic">P<sub>w</sub></span>.</p> Full article ">Figure 12
<p>The case of <span class="html-italic">T<sub>max</sub></span> at the validation point: (<b>a</b>) the simulation results versus the predicted results of the RSM; (<b>b</b>) the absolute error of the simulated value versus the predicted results of the RSM.</p> Full article ">Figure 13
<p>The case of <span class="html-italic">P<sub>w</sub></span> at the validation point: (<b>a</b>) The simulation results versus the predicted results of the RSM; (<b>b</b>) the absolute error of the simulated value versus the predicted results of the RSM.</p> Full article ">Figure 14
<p>Minimization optimization of <span class="html-italic">T<sub>max</sub></span> and <span class="html-italic">P<sub>w</sub></span>.</p> Full article ">Figure 15
<p>Minimized design variable optimization for <span class="html-italic">T<sub>max</sub></span> and <span class="html-italic">P<sub>w</sub></span>: (<b>a</b>) <span class="html-italic">Wa</span> sample point iterative process; (<b>b</b>) <span class="html-italic">Wb</span> sample point iterative process (<b>c</b>) <span class="html-italic">Na</span> sample point iterative process; (<b>d</b>) <span class="html-italic">Nb</span> sample point iterative process; (<b>e</b>) <span class="html-italic">Qw</span> sample point iterative process; (<b>f</b>) <span class="html-italic">d</span> sample point iterative process.</p> Full article ">Figure 16
<p>Pareto solution set for <span class="html-italic">T<sub>max</sub></span> and <span class="html-italic">P<sub>w</sub></span>.</p> Full article ">Figure 17
<p>CFD simulation results of the optimized scheme: (<b>a</b>) the contour of the temperature field for motor system; (<b>b</b>) the contour of the cooling water pressure.</p> Full article ">
26 pages, 28817 KiB  
Article
Hydrodynamic Performance of Toroidal Propeller Based on Detached Eddy Simulation Method
by Pei Xu, Yingchun Guo, Liyu Ye and Kewei Song
J. Mar. Sci. Eng. 2024, 12(12), 2132; https://doi.org/10.3390/jmse12122132 - 22 Nov 2024
Viewed by 230
Abstract
Toroidal propellers hold significant potential as underwater propulsion systems compared to traditional propellers, primarily due to their unique shape, which effectively reduces and minimizes hydrodynamic noise and enhances structural stability and overall strength. To investigate hydrodynamic loads, flow fields, and vortex characteristics of [...] Read more.
Toroidal propellers hold significant potential as underwater propulsion systems compared to traditional propellers, primarily due to their unique shape, which effectively reduces and minimizes hydrodynamic noise and enhances structural stability and overall strength. To investigate hydrodynamic loads, flow fields, and vortex characteristics of toroidal propellers, numerical simulations were conducted on both toroidal and conventional propellers using the detached eddy simulation (DES) method in Star CCM+ computational fluid dynamics software. Results show that at low advance coefficients, the primary thrust generated by toroidal blades comes from pressure difference in the front section, whereas at high advance coefficients, it originates in the back section. A high-velocity region exists between the front and back sections of the toroidal propeller, with the range and intensity of this region gradually increasing from front to back. The wake vortex of the toroidal propeller comprises two parts: the tip vortex, where the front section tip vortex, back section tip vortex, and transition section leakage vortex merge, and the trailing edge vortex, which forms from the fusion of the front and back section leakage vortices. The fusion of these vortices is influenced by the advance coefficient. Compared to conventional propellers, the toroidal propellers exhibit a more extensive and intense trailing edge vortex in the wake flow field. These findings provide guidance for the optimization design research of toroidal propellers. Full article
(This article belongs to the Section Ocean Engineering)
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<p>Geometric model of propeller.</p> Full article ">Figure 2
<p>Computational domain and boundary condition settings.</p> Full article ">Figure 3
<p>Calculation domain grid of toroidal propeller.</p> Full article ">Figure 4
<p>PC456 propeller.</p> Full article ">Figure 5
<p>Wake vortex structure of toroidal propeller with different numbers of grids.</p> Full article ">Figure 5 Cont.
<p>Wake vortex structure of toroidal propeller with different numbers of grids.</p> Full article ">Figure 6
<p>Hydrodynamic performance curve of toroidal propeller.</p> Full article ">Figure 7
<p>Hydrodynamic performance curves of front and rear propellers.</p> Full article ">Figure 8
<p>Pressure distribution on toroidal propeller blade back at different advance coefficients.</p> Full article ">Figure 8 Cont.
<p>Pressure distribution on toroidal propeller blade back at different advance coefficients.</p> Full article ">Figure 9
<p>Pressure distribution on toroidal propeller blade face at different advance coefficients.</p> Full article ">Figure 10
<p>Vertical view of pressure distribution of toroidal propeller at different advance coefficients.</p> Full article ">Figure 11
<p>Distribution of pressure coefficients at different cross-sections on the toroidal propeller blades.</p> Full article ">Figure 11 Cont.
<p>Distribution of pressure coefficients at different cross-sections on the toroidal propeller blades.</p> Full article ">Figure 12
<p>Pressure distribution of front and rear propellers.</p> Full article ">Figure 13
<p>Distribution of pressure coefficients at different cross-sections on front and rear propeller blade (<span class="html-italic">J</span> = 0.8).</p> Full article ">Figure 14
<p>Axial velocity contours at <span class="html-italic">Y</span> = 0 with different advance coefficients—toroidal propeller.</p> Full article ">Figure 15
<p>Axial velocity contours at <span class="html-italic">Y</span> = 0 for front and rear propellers (<span class="html-italic">J</span> = 0.8).</p> Full article ">Figure 16
<p>Different cross-sectional positions in toroidal propeller.</p> Full article ">Figure 17
<p>Axial velocity distribution at different cross-sections of toroidal propeller.</p> Full article ">Figure 18
<p>Axial velocity distribution at different advance coefficients—toroidal propeller.</p> Full article ">Figure 19
<p>Different cross-sectional positions in front and rear propellers.</p> Full article ">Figure 20
<p>Axial velocity distribution at different cross-sections of the front and rear propellers (<span class="html-italic">J</span> = 0.8).</p> Full article ">Figure 21
<p>Iso-surface wake vortex structure at different advance coefficients—toroidal propeller.</p> Full article ">Figure 22
<p>Iso-surface wake vortex structure at different advance coefficients—front propeller.</p> Full article ">Figure 23
<p>Iso-surface wake vortex structure at different advance coefficients—rear propeller.</p> Full article ">Figure 24
<p>Vorticity distribution at <span class="html-italic">Y</span> = 0 with different advance coefficients—toroidal propeller.</p> Full article ">Figure 25
<p>Vorticity distribution at <span class="html-italic">Y</span> = 0 with different advance coefficients—front and rear propellers.</p> Full article ">
26 pages, 11337 KiB  
Article
Comparative Study on Hydrodynamic Characteristics of Under-Water Vehicles Near Free Surface and Near Ice Surface
by Pei Xu, Jixiang Chen, Yingchun Guo and Wanzhen Luo
J. Mar. Sci. Eng. 2024, 12(12), 2131; https://doi.org/10.3390/jmse12122131 - 22 Nov 2024
Viewed by 267
Abstract
In this paper, the commercial computational fluid dynamics software STAR-CCM+ (18.04.008-R8) is utilized to analyze the hydrodynamic performance of BB2 underwater vehicles under various navigation conditions, as well as the flow field disturbances caused by the free surface and ice surface during navigation. [...] Read more.
In this paper, the commercial computational fluid dynamics software STAR-CCM+ (18.04.008-R8) is utilized to analyze the hydrodynamic performance of BB2 underwater vehicles under various navigation conditions, as well as the flow field disturbances caused by the free surface and ice surface during navigation. After dividing the computational domains based on different navigation scenarios, numerical simulations are conducted for BB2 underwater vehicles (without a propeller) at infinite depth, near the free surface, and near the ice surface under various operating conditions. The analysis focuses on changes in resistance, velocity fields, and pressure fields of the BB2 at different velocities and navigation depths, followed by a comparison of the navigation differences of BB2 vehicles under varying operating conditions. Furthermore, to simulate realistic navigation conditions for underwater vehicles, numerical simulations are performed for BB2 underwater vehicles equipped with a propeller under different operating conditions. The results indicate that both the free surface and ice surface significantly influence the resistance, velocity field, and pressure field of the BB2. When the navigation depth exceeds 2D, the impact of ice on the vehicle can be nearly disregarded, and when the navigation depth exceeds 3D, the influence of the free surface on the vehicle can also be considered negligible. Full article
(This article belongs to the Section Ocean Engineering)
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<p>BB2 3D model.</p> Full article ">Figure 2
<p>Definition of submarine’s non-dimensional depth.</p> Full article ">Figure 3
<p>Computational domain for different conditions. (<b>a</b>) infinite depth, (<b>b</b>) near free surface, and (<b>c</b>) near ice surface.</p> Full article ">Figure 4
<p>Grid division under different conditions. (<b>a</b>) refinement of the grids around the hull at infinite depth, (<b>b</b>) refinement of the grids around the hull and the free surface, (<b>c</b>) refinement of the ice surface grid, (<b>d</b>) refinement of the grids of the head appendages, (<b>e</b>) refinement of the grids of the hull, and (<b>f</b>) refinement of the grids of the tail.</p> Full article ">Figure 5
<p>Comparison of calculation results with and without appendages: (<b>a</b>) Comparison of resistance coefficients; (<b>b</b>) Comparison of lift coefficients.</p> Full article ">Figure 6
<p>Dimensionless velocity field of the tail of the hulls with and without appendages.</p> Full article ">Figure 7
<p>Variation in resistance coefficients with <span class="html-italic">Fr</span> at the same velocity.</p> Full article ">Figure 8
<p>Variation in resistance coefficients with <span class="html-italic">H*</span> at the same velocity.</p> Full article ">Figure 9
<p>Dimensionless velocity field under different operating conditions. (<b>a</b>) Navigation at infinite depth, (<b>b</b>) Navigation at 0.6 <span class="html-italic">D</span> near the free surface, and (<b>c</b>) Navigation at 0.6<span class="html-italic">D</span> near the ice surface.</p> Full article ">Figure 10
<p>The distribution of dimensionless velocity field at different velocities near the ice surface: (<b>a</b>) <span class="html-italic">V</span> = 0.609 m/s; (<b>b</b>) <span class="html-italic">V</span> = 0.913 m/s; (<b>c</b>) <span class="html-italic">V</span> = 1.61 m/s; (<b>d</b>) <span class="html-italic">V</span> = 1.82 m/s and (<b>e</b>) <span class="html-italic">V</span> = 2.61 m/s.</p> Full article ">Figure 11
<p>The location of the intercept cross-section.</p> Full article ">Figure 12
<p><span class="html-italic">X</span>-speed at different speeds: (<b>a</b>) <span class="html-italic">V</span> = 0.609 m/s; (<b>b</b>) <span class="html-italic">V</span> = 0.913 m/s; (<b>c</b>) <span class="html-italic">V</span> = 1.61 m/s; (<b>d</b>) <span class="html-italic">V</span> = 1.82 m/s and (<b>e</b>) <span class="html-italic">V</span> =2.61 m/s.</p> Full article ">Figure 13
<p>Distribution of dimensionless velocity field under different diving depths near the ice surface: (<b>a</b>) 0.6 <span class="html-italic">D</span>; (<b>b</b>) 1 <span class="html-italic">D</span>; (<b>c</b>) 1.5 <span class="html-italic">D</span>; (<b>d</b>) 2 <span class="html-italic">D</span>; (<b>e</b>) 3 <span class="html-italic">D</span>; (<b>f</b>) Infinite depth.</p> Full article ">Figure 14
<p>Distribution of dimensionless velocity field near the free surface at different diving depths: (<b>a</b>) 0.6 <span class="html-italic">D</span>; (<b>b</b>) 1 <span class="html-italic">D</span>; (<b>c</b>) 1.5 <span class="html-italic">D</span>; (<b>d</b>) 2 <span class="html-italic">D</span>; (<b>e</b>) 3 <span class="html-italic">D</span>; (<b>f</b>) 5 <span class="html-italic">D</span>; and (<b>g</b>) Infinite depth.</p> Full article ">Figure 15
<p>Wave height variation in the free liquid surface at different depths: (<b>a</b>) 0.6 <span class="html-italic">D</span>; (<b>b</b>) 1 <span class="html-italic">D</span>; (<b>c</b>) 1.5 <span class="html-italic">D</span>; (<b>d</b>) 2 <span class="html-italic">D</span>; (<b>e</b>) 3 <span class="html-italic">D</span>; and (<b>f</b>) Infinite depth.</p> Full article ">Figure 16
<p>Dimensionless pressure field of the BB2 hull under the condition of near ice surface with different diving depths: (<b>a</b>) 0.6 <span class="html-italic">D</span>; (<b>b</b>) 0.8 <span class="html-italic">D</span>; (<b>c</b>) 1 <span class="html-italic">D</span>; (<b>d</b>) 1.5 <span class="html-italic">D</span>; (<b>e</b>) 2 <span class="html-italic">D</span>; (<b>f</b>) 2 <span class="html-italic">D</span>; (<b>g</b>) Infinite depth.</p> Full article ">Figure 17
<p>Dimensionless pressure field distribution of the BB2 hull under the condition of near free surface with different diving depths: (<b>a</b>) 0.6 <span class="html-italic">D</span>; (<b>b</b>) 0.8 <span class="html-italic">D</span>; (<b>c</b>) 1 <span class="html-italic">D</span>; (<b>d</b>) 1.5 <span class="html-italic">D</span> (<b>e</b>) 2 <span class="html-italic">D</span>; (<b>f</b>) 3 <span class="html-italic">D</span>; (<b>g</b>) 5 <span class="html-italic">D</span>; (<b>h</b>) Infinite depth.</p> Full article ">Figure 18
<p>Distribution of dimensionless pressure along <span class="html-italic">X</span> direction on the BB2 hull at different depths: (<b>a</b>) Ice surface; (<b>b</b>) Free surface.</p> Full article ">Figure 19
<p>BB2 self-propulsion 3D model. (<b>a</b>) self-propulsion model; (<b>b</b>) BB2 model; and (<b>c</b>) 6–blade propeller model.</p> Full article ">Figure 20
<p>Calculation domain division under self-propulsion conditions near the ice surface.</p> Full article ">Figure 21
<p>Grid division under different self-propulsion conditions. (<b>a</b>) Grid around the vehicle; (<b>b</b>) Freesurface refinement; (<b>c</b>) Boundary layer mesh around bow; (<b>d</b>) Ice-surface refinement; (<b>e</b>) Grid around propeller; (<b>f</b>) Grid around the blade; and (<b>g</b>) Boundary layer mesh around blade.</p> Full article ">Figure 22
<p>Calculation of self-propulsion point at a flow velocity of 1.22 m/s at infinite depth.</p> Full article ">Figure 23
<p>Distribution of dimensionless velocity field under different self-propulsion conditions: (<b>a</b>) Free surface, (<b>b</b>) Ice surface, and (<b>c</b>) Infinite depth.</p> Full article ">Figure 24
<p>Dimensionless pressure field distribution under different self-propulsion conditions: (<b>a</b>) Free surface, (<b>b</b>) Ice surface, and (<b>c</b>) Infinite depth.</p> Full article ">
18 pages, 2646 KiB  
Article
Improved RT-DETR for Infrared Ship Detection Based on Multi-Attention and Feature Fusion
by Chun Liu, Yuanliang Zhang, Jingfu Shen and Feiyue Liu
J. Mar. Sci. Eng. 2024, 12(12), 2130; https://doi.org/10.3390/jmse12122130 - 22 Nov 2024
Viewed by 305
Abstract
Infrared cameras form images by capturing the thermal radiation emitted by objects in the infrared spectrum, making them complex sensors widely used in maritime surveillance. However, the broad spectral range of the infrared band makes it susceptible to environmental interference, which can reduce [...] Read more.
Infrared cameras form images by capturing the thermal radiation emitted by objects in the infrared spectrum, making them complex sensors widely used in maritime surveillance. However, the broad spectral range of the infrared band makes it susceptible to environmental interference, which can reduce the contrast between the target and the background. As a result, detecting infrared targets in complex marine environments remains challenging. This paper presents a novel and enhanced detection model developed from the real-time detection transformer (RT-DETR), which is designated as MAFF-DETR. The model incorporates a novel backbone by integrating CSP and parallelized patch-aware attention to enhance sensitivity to infrared imagery. Additionally, a channel attention module is employed during feature selection, leveraging high-level features to filter low-level information and enabling efficient multi-level fusion. The model’s target detection performance on resource-constrained devices is further enhanced by incorporating advanced techniques such as group convolution and ShuffleNetV2. The experimental results show that, although the enhanced RT-DETR algorithm still experiences missed detections under severe object occlusion, it has significantly improved overall performance, including a 1.7% increase in mAP, a reduction in 4.3 M parameters, and a 5.8 GFLOPs decrease in computational complexity. It can be widely applied to tasks such as coastline monitoring and maritime search and rescue. Full article
(This article belongs to the Special Issue AI-Empowered Marine Energy)
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<p>Architecture of the MAFF-DETR Network.</p> Full article ">Figure 2
<p>Comparison between the Original C2f Module and the C2f-PPA Module in MAFF-DETR: (<b>a</b>) Architecture of the C2f module. (<b>b</b>) Architecture of the C2f-PPA module.</p> Full article ">Figure 3
<p>Structure overview of the parallelized patch-aware attention module.</p> Full article ">Figure 4
<p>Detailed structure of the SFF and CA modules.</p> Full article ">Figure 5
<p>Detailed structure of the multi-layer dynamic shuffle transformer module.</p> Full article ">Figure 6
<p>Dataset analysis: (<b>a</b>) Bar graph of the number of ship types. (<b>b</b>) Bounding box overlay.</p> Full article ">Figure 7
<p>Examples of bounding box visualizations of infrared ship detections for different scenarios: (<b>a</b>) Detection results of adjacent fishing boats. (<b>b</b>) Detection results of extremely small targets. (<b>c</b>) Detection results at the image edges. (<b>d</b>) Detection results in complex nearshore scenarios. The red box indicates the false negatives and false positives issues of RT-DETR.</p> Full article ">Figure 8
<p>Examples of heat maps Illustrating infrared ship detection for different scenarios: (<b>a</b>) Heat map of adjacent fishing boats. (<b>b</b>) Heat map of extremely small targets. (<b>c</b>) Heat map at the image edges. (<b>d</b>) Heat map in complex nearshore scenarios.</p> Full article ">
29 pages, 18764 KiB  
Article
Analytical Modeling of the Lazy-Wave Hydrogen Production Riser (HPR) with Incorporation of Seabed Interaction in the Touchdown Zone
by Mohammad Mahdi Hajitaheriha and Hodjat Shiri
J. Mar. Sci. Eng. 2024, 12(12), 2129; https://doi.org/10.3390/jmse12122129 - 22 Nov 2024
Viewed by 220
Abstract
Hydrogen production risers (HPRs) connected to floating offshore wind turbines (FOWTs) must be properly configured to minimize both the top-end tension at the hang-off point and the oscillation amplitude in the touchdown zone (TDZ) under environmental loads. One of the best riser configurations [...] Read more.
Hydrogen production risers (HPRs) connected to floating offshore wind turbines (FOWTs) must be properly configured to minimize both the top-end tension at the hang-off point and the oscillation amplitude in the touchdown zone (TDZ) under environmental loads. One of the best riser configurations to meet these requirements is the lazy-wave configuration, where the riser is lifted midway by buoyancy tanks to create a negative curvature, mitigating the motion dependency of the catenary part and the TDZ. Analytical solutions can be effectively used in riser optimization and configuration studies, where a large number of analyses need to be conducted iteratively. In this paper, an analytical model for HPRs has been developed by combining different approaches for the hanging and touchdown zones to improve the accuracy and continuity of shear force, bending moment, and axial tension distribution along the riser, which are the key parameters governing fatigue damage accumulation in the TDZ. Modified catenary equations were used for the hanging part, and a boundary layer model was implemented in the touchdown zone to model the seabed interaction, preventing stress discontinuity between the two sections. The model was used to assess a case study and compared with numerical simulations to ensure accuracy and viability. The proposed model can be used in daily engineering practice for preliminary investigations and optimization studies of HPRs. Full article
(This article belongs to the Special Issue Sustainable Offshore Pipeline Operations)
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<p>Comparison of static shear force distribution, bending moments, and Von Mises stress along an SCR for seabed stiffness, <math display="inline"><semantics> <mrow> <mi>k</mi> <mo>=</mo> <mn>100</mn> <mo> </mo> <mi>kPa</mi> </mrow> </semantics></math>, and peak shear force at the TDZ in different seabed stiffness [<a href="#B20-jmse-12-02129" class="html-bibr">20</a>].</p> Full article ">Figure 1 Cont.
<p>Comparison of static shear force distribution, bending moments, and Von Mises stress along an SCR for seabed stiffness, <math display="inline"><semantics> <mrow> <mi>k</mi> <mo>=</mo> <mn>100</mn> <mo> </mo> <mi>kPa</mi> </mrow> </semantics></math>, and peak shear force at the TDZ in different seabed stiffness [<a href="#B20-jmse-12-02129" class="html-bibr">20</a>].</p> Full article ">Figure 2
<p>Schematic of the steel lazy-wave riser segments.</p> Full article ">Figure 3
<p>Schematic diagram of the forces acting on the suspended riser and the riser with internal fluid and hydrostatic pressure.</p> Full article ">Figure 4
<p>TDP position on the elastic seabed obtained via BLM [<a href="#B19-jmse-12-02129" class="html-bibr">19</a>].</p> Full article ">Figure 5
<p>Algorithm for static deformation and internal force calculation in SLWR configurations.</p> Full article ">Figure 6
<p>Detailed section of the algorithm for SLWR configuration and internal force calculation.</p> Full article ">Figure 7
<p>Different types of risers and vessel positions in the mean, far, and near zones.</p> Full article ">Figure 8
<p>(<b>a</b>) Configuration, (<b>b</b>) curvature, and (<b>c</b>) effective tension of the SLWR along the SLWR.</p> Full article ">Figure 9
<p>Lazy-wave riser configuration, (<b>a</b>) SLWR 1, (<b>b</b>) SLWR 2, (<b>c</b>) SLWR 3, and (<b>d</b>) SLWR 4.</p> Full article ">Figure 10
<p>Curvature distribution along the SLWR, (<b>a</b>) SLWR 1, (<b>b</b>) SLWR 2, (<b>c</b>) SLWR 3, and (<b>d</b>) SLWR 4.</p> Full article ">Figure 11
<p>Effective tension distribution along the SLWR, (<b>a</b>) SLWR 1, (<b>b</b>) SLWR 2, (<b>c</b>) SLWR 3, and (<b>d</b>) SLWR 4.</p> Full article ">Figure 11 Cont.
<p>Effective tension distribution along the SLWR, (<b>a</b>) SLWR 1, (<b>b</b>) SLWR 2, (<b>c</b>) SLWR 3, and (<b>d</b>) SLWR 4.</p> Full article ">Figure 12
<p>Wall tension distribution along the SLWR, (<b>a</b>) SLWR 1, (<b>b</b>) SLWR 2, (<b>c</b>) SLWR 3, and (<b>d</b>) SLWR 4.</p> Full article ">Figure 13
<p>Wall tension stress distribution along the SLWR, (<b>a</b>) SLWR 1, (<b>b</b>) SLWR 2, (<b>c</b>) SLWR 3, and (<b>d</b>) SLWR 4.</p> Full article ">Figure 14
<p>Bending moment stress distribution along the SLWR, (<b>a</b>) SLWR 1, (<b>b</b>) SLWR 2, (<b>c</b>) SLWR 3, and (<b>d</b>) SLWR 4.</p> Full article ">Figure 15
<p>Total axial stress distribution along the SLWR, (<b>a</b>) SLWR 1, (<b>b</b>) SLWR 2, (<b>c</b>) SLWR 3, and (<b>d</b>) SLWR 4.</p> Full article ">Figure 15 Cont.
<p>Total axial stress distribution along the SLWR, (<b>a</b>) SLWR 1, (<b>b</b>) SLWR 2, (<b>c</b>) SLWR 3, and (<b>d</b>) SLWR 4.</p> Full article ">Figure 16
<p>Comparison of the wall tension stress range distribution along the SLWR, MATLAB: (<b>a</b>) SLWR 1, (<b>b</b>) SLWR 2, (<b>c</b>) SLWR 3, and (<b>d</b>) SLWR 4; OrcaFelx: (<b>e</b>) SLWR 1, (<b>f</b>) SLWR 2, (<b>g</b>) SLWR 3, and (<b>h</b>) SLWR 4.</p> Full article ">Figure 16 Cont.
<p>Comparison of the wall tension stress range distribution along the SLWR, MATLAB: (<b>a</b>) SLWR 1, (<b>b</b>) SLWR 2, (<b>c</b>) SLWR 3, and (<b>d</b>) SLWR 4; OrcaFelx: (<b>e</b>) SLWR 1, (<b>f</b>) SLWR 2, (<b>g</b>) SLWR 3, and (<b>h</b>) SLWR 4.</p> Full article ">Figure 17
<p>Wall tension stress range distribution along the different SLWRs: (<b>a</b>), constant tension in the boundary layer, (<b>b</b>) developed tension variation, (<b>c</b>) obtained numerical results.</p> Full article ">Figure 18
<p>Comparison of the bending stress range distribution along the SLWR, (<b>a</b>) SLWR 1, (<b>b</b>) SLWR 2, (<b>c</b>) SLWR 3, and (<b>d</b>) SLWR 4.</p> Full article ">Figure 19
<p>Comparison of the total axial stress range distribution along the SLWR, MATLAB: (<b>a</b>) SLWR 1, (<b>b</b>) SLWR 2, (<b>c</b>) SLWR 3, and (<b>d</b>) SLWR 4; OrcaFelx: (<b>e</b>) SLWR 1, (<b>f</b>) SLWR 2, (<b>g</b>) SLWR 3, and (<b>h</b>) SLWR 4.</p> Full article ">Figure 20
<p>Comparison of the total axial stress range distribution along the SLWR for different types of risers, (<b>a</b>) Near 3 to Far 3, (<b>b</b>) Near 2 to Far 3, (<b>c</b>) Mean to Far 3, and (<b>d</b>) Far 2 to Far 3.</p> Full article ">Figure 21
<p>Comparison of different SLWRs with SCRs.</p> Full article ">
19 pages, 623 KiB  
Article
Critical Success Factors for Green Port Transformation Using Digital Technology
by Zhenqing Su, Yanfeng Liu, Yunfan Gao, Keun-Sik Park and Miao Su
J. Mar. Sci. Eng. 2024, 12(12), 2128; https://doi.org/10.3390/jmse12122128 - 22 Nov 2024
Viewed by 286
Abstract
Ports are the main arteries of global trade, handling goods circulation and serving as hubs for information, capital, and technology. Integrating digital technology has become the key for green port development to achieve resource efficiency and ecological balance. The current literature overlooks how [...] Read more.
Ports are the main arteries of global trade, handling goods circulation and serving as hubs for information, capital, and technology. Integrating digital technology has become the key for green port development to achieve resource efficiency and ecological balance. The current literature overlooks how digital technology can facilitate greener port operations. This study integrates sustainable supply chain management and system dynamics theories based on an in-depth analysis of existing research results and expert interviews. The analysis focuses on three key dimensions: integrating digital technologies with infrastructure, optimizing digital management and operations, and improving environmental and safety management in a digitally driven setting. Using the fuzzy Decision Making Trial and Evaluation Laboratory (Fuzzy Dematel) methodology, we collaborated with domain experts in port logistics to identify and confirm 12 pivotal factors that support the green digital transformation of ports. The research shows that the most critical success factors for using digital technology to drive ports’ green transformation are green supply chain information platforms, intelligent vessel scheduling, traffic optimization, and digital carbon emission monitoring. This study significantly contributes to the literature on green port transformation, offering indispensable practical insights for port operators, government entities, and shipping firms in identifying and deploying these key success factors. The findings will help maritime supply chain stakeholders develop actionable digital strategies, improving port efficiency and ecological resilience. Full article
(This article belongs to the Section Ocean Engineering)
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<p>Centrality–cause degree scatter plot.</p> Full article ">
23 pages, 4802 KiB  
Review
Stromatolites and Their “Kin” as Living Microbialites in Contemporary Settings Linked to a Long Fossil Record
by Ed Landing and Markes E. Johnson
J. Mar. Sci. Eng. 2024, 12(12), 2127; https://doi.org/10.3390/jmse12122127 - 22 Nov 2024
Viewed by 458
Abstract
Organo-sedimentary deposits that result from fine-grained sediment trapping, binding, and likely precipitation (of carbonate) by microbes in flat-mat, branching, and dome-shaped constructions are termed microbialites. They were first identified as stromatolites by paleontologists well before the discovery of cyanobacteria that build the same [...] Read more.
Organo-sedimentary deposits that result from fine-grained sediment trapping, binding, and likely precipitation (of carbonate) by microbes in flat-mat, branching, and dome-shaped constructions are termed microbialites. They were first identified as stromatolites by paleontologists well before the discovery of cyanobacteria that build the same kinds of structures in contemporary settings around the world. Earth’s earliest life forms were prokaryotes (bacteria and bacteria-like forms) that reproduced under anaerobic conditions and later produced increasingly aerobic conditions. Stromatolites persisted through later Archean and Proterozoic times through the subsequent Phanerozoic to the present. At the start of the Cambrian Period 538 million years ago, stromatolites continued alongside rapidly diversifying plant and animal phyla during the Cambrian explosion of eukaryotic life, which have complex cells with internal structures and tissue-grade organization in multicellular taxa. The type locality exhibiting clear examples of stromatolite structures is conserved at Lester Park near Saratoga Springs in northeastern New York State. Paleontologist James Hall (1811–1898) was the first in 1884 to assign a Latin binomen (Cryptozoon proliferum) to stromatolite fossils from Lester Park. Thereafter, reports on formally named stromatolites proliferated, as did examples from virtually all subsequent geological time intervals including the Pleistocene Epoch. However, recognition that living cyanobacteria formed stromatolites identified as Cryptozoon took place much later in 1961 with the announcement by geologist Brian W. Logan (1933–2008) who described modern constructions in Hamlin Pool, Shark Bay, Western Australia. Initially, Shark Bay was regarded as a one-of-a-kind sanctuary for stromatolites living under restricted conditions with elevated levels of salinity that prohibited competition or grazing by eukaryotes. Most notably, among other settings with living stromatolites discovered and described since then are the Bahamas, East African rift lakes, Mexico’s Baja California, and saline lakes in Argentina. This report reviews the history of discoveries of modern-day stromatolites, more commonly called microbialites by biologists. All are predicated on the ground-breaking efforts of geologists and paleontologists who first described fossil stromatolites but were unaware of their living counterparts. The Lester Park locality is highlighted together with a master list of other North American localities that feature purported Cryptozoons. Full article
(This article belongs to the Special Issue Feature Review Papers in Geological Oceanography)
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Full article ">Figure 1
<p>Global representation of today’s continents and oceans on a Mollweide projection showing major ocean spreading zones. Numerals on black triangles denote the locations of oligotrophic stromatolites living today in (1) Shark Bay, Western Australia; (2) Bahamas; (3) Lake Tanganyika, Africa; (4) saline lakes in northwestern Argentina; and (5) saline ponds in Mexico’s Baja California. The numbered black dot marks the location of fossil stromatolites from present-day Upper New York State (1).</p> Full article ">Figure 2
<p>Maps showing the coast of Western Australia on the Indian Ocean: (<b>a</b>) Western Australia with a small arrow pointing to Shark Bay; and (<b>b</b>) enlargement showing the location of the Hamelin Pool Marine Nature Reserve (asterisk) within the UNESCO World Heritage zone protecting the greater Shark Bay.</p> Full article ">Figure 3
<p>Examples of stromatolites shaped like large bread loaves oriented perpendicular to the shore at the Hamelin Pool Marine Nature Reserve, Shark Bay: (<b>a</b>) stromatolites exposed during low tide (pocket knife 9 cm long for scale); (<b>b</b>) stromatolites barely awash at high tide with narrow, open galleries from 10 to 20 cm wide.</p> Full article ">Figure 4
<p>A field of living stromatolites in permanently subtidal seawater offshore Carbla Point near Hamelin Pool in Shark Bay. Junior author for scale.</p> Full article ">Figure 5
<p>Maps showing Mexico’s Isla Ángel de la Guarda in relation to the Baja California peninsula: (<b>a</b>) the full peninsula adjacent to the Gulf of California off the Mexican mainland with the island’s location (asterisk) near the head of the gulf; (<b>b</b>) map of Isla Ángel de la Guarda marking the four localities (blue) where stromatolites occur in closed lagoons; and (<b>c</b>) topographic map enlarged from box in (<b>b</b>) showing the island’s southeast end, where thrombolites and mat-forming stromatolites were discovered in 2007.</p> Full article ">Figure 6
<p>Photos showing living stromatolites from closed lagoons on the shores of southeast Isla Ángel de la Guarda (see <a href="#jmse-12-02127-f005" class="html-fig">Figure 5</a>c for location): (<b>a</b>) thrombolite assemblage of branched forms the size of small cauliflower heads from the small lagoon (coin 2.4 cm in diameter for scale); (<b>b</b>) matted microbialites dissected by desiccation polygons along the shore of the big lagoon (compass case 10 cm across for scale).</p> Full article ">Figure 7
<p>Location of Lester Park in the Upper Cambrian–Upper Ordovician lowlands southeast of the Mesoproterozoic Adirondack Mountains massif, northeast of the upper Middle Devonian Catskill Highlands, and west from the terminal Ediacaran–lower Upper Ordovician Taconic Allochthon.</p> Full article ">Figure 8
<p>Coalesced topotype specimens of the first-named stromatolite <span class="html-italic">Cryptozoon proliferum</span> Hall, 1884, at Lester Park, Saratoga County, eastern New York: (<b>a</b>) View of the top of a shoaling cycle abraded and truncated to show growth laminae by the movement of coarse quartz sand that weathers brownish; narrower, lower parts of domes (upper part of the figure) were exposed by glacial (Pleistocene) plucking of the upper part of domes. (<b>b</b>) Detail of clotted thrombolite structure surrounded by bedded limestone from Hoyt quarry, ca. 5 m above the <span class="html-italic">C</span>. <span class="html-italic">proliferum</span> surface at Lester Park. Hammer (30 cm) for scale in both pictures.</p> Full article ">Figure 9
<p><span class="html-italic">Cryptozoon proliferum</span> from older Galway Formation (lower Upper Cambrian <span class="html-italic">Elvina</span> Zone) NE of Lester Park shows two generations of fracturing: (1) brownish carbonate mud-filled cracks in the lower part of the specimen that separate and also run transverse to growth laminae are syndepositional fractures (ca. 490 Ma) and reflect continued extension of the rifted margin of NE Laurentia; (2) thin white calcite veins parallel to growth laminae produced during the Taconic orogeny (ca. 460 Ma) [<a href="#B74-jmse-12-02127" class="html-bibr">74</a>]. Hypotype NYSM 19512 from the middle of the Galway Formation railroad cut above U.S. Route 9 just N of the intersection of U.S. Route 9 with Daniels Road [<a href="#B74-jmse-12-02127" class="html-bibr">74</a>], with a USD 25 cent coin (23 mm) for scale.</p> Full article ">Figure 10
<p>Small <span class="html-italic">Crytozoon proliferum</span> dome at the north end of the Lester Park surface. This specimen was termed a “microatoll” [<a href="#B26-jmse-12-02127" class="html-bibr">26</a>], but it has an erosion-truncated top and lateral margins and is surrounded by a coarse-grained sandstone with light grey-colored <span class="html-italic">C. proliferum</span> clasts (yellow arrows).</p> Full article ">
18 pages, 4642 KiB  
Article
Experimental Investigation on Wave and Bed Profile Evolution in a Sandbar-Lagoon Coast with Submerged Vegetation
by Wei Xing, Xin Cong, Cuiping Kuang, Dan Wang, Zhenzhen An and Qingping Zou
J. Mar. Sci. Eng. 2024, 12(12), 2126; https://doi.org/10.3390/jmse12122126 - 21 Nov 2024
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Abstract
Better understanding of the hydro- and morphodynamic processes within vegetated sandbar-lagoon coasts is important for assessing the coastal protection capability of vegetation meadow for the coastal environments. Eighteen flume tests were conducted in a mobile-bed sandbar-lagoon with mimicked submerged vegetation under different water [...] Read more.
Better understanding of the hydro- and morphodynamic processes within vegetated sandbar-lagoon coasts is important for assessing the coastal protection capability of vegetation meadow for the coastal environments. Eighteen flume tests were conducted in a mobile-bed sandbar-lagoon with mimicked submerged vegetation under different water depths and wave conditions. It was found that wave attenuation by submerged vegetation near the breaking point is significant. An empirical linear expression for the total wave energy change ratio is proposed with a determination coefficient of 0.84. Moreover, the quantitative formulae for the erosion volume and maximum erosion thickness of sandbars and foredunes, as well as the total sediment transport volume, were proposed to demonstrate the implications of submerged vegetation meadows. These findings provide scientific references for coastal management and conservation planning, especially for sandbar-lagoon coasts. Nevertheless, additional physical experiments or field data are necessary to further validate those formulae. Full article
(This article belongs to the Section Coastal Engineering)
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Figure 1

Figure 1
<p>Experimental setup diagram.</p> Full article ">Figure 2
<p>Actual bed morphology and vegetation configuration within the flume.</p> Full article ">Figure 3
<p>Changes in short-wave height (<b>a</b>,<b>b</b>), long-wave height (<b>c</b>,<b>d</b>), and wave energy (<b>e</b>,<b>f</b>). <span class="html-italic">H</span><sub>SW</sub> is the short-wave height; <span class="html-italic">H</span><sub>LW</sub> is the long-wave height; <span class="html-italic">E</span><sub>T</sub> is the total wave energy. The light green indicates the vegetation zone. N indicates “without vegetation”, S signifies “with vegetation”, d1 denotes a water depth of 0.48 m, d2 denotes a water depth of 0.55 m, and the final numbers 1–5 represent incident wave heights ranging from low to high (<a href="#jmse-12-02126-t001" class="html-table">Table 1</a>).</p> Full article ">Figure 4
<p>Wave reflection coefficients at different water depths and incident wave heights.</p> Full article ">Figure 5
<p>Longitudinal profiles at different scenarios. (<b>a</b>) <span class="html-italic">d</span> = 0.48 m; (<b>b</b>) <span class="html-italic">d</span> = 0.55 m.</p> Full article ">Figure 6
<p>Comparison of shoreline receding distance <span class="html-italic">Dsr</span> (<b>a</b>) and maximum erosion (<span class="html-italic">Tes</span> for sandbar, <span class="html-italic">Ted</span> for foredune) /deposition (<span class="html-italic">Tss</span> for sandbar, <span class="html-italic">Tsl</span> for lagoon, <span class="html-italic">Tsd</span> for foredune) thickness (<b>b</b>,<b>c</b>) with and without submerged vegetation. <span class="html-italic">H</span><sub>0</sub> is the incident wave height.</p> Full article ">Figure 7
<p>Comparison of local erosion (<span class="html-italic">Evs</span> for sandbar, <span class="html-italic">Evd</span> for foredune) /deposition (<span class="html-italic">Dvs</span> for sandbar, <span class="html-italic">Dvd</span> for foredune) volume (<b>a</b>,<b>b</b>), local sediment transport volume (<span class="html-italic">Svs</span> for sandbar, <span class="html-italic">Svl</span> for lagoon, <span class="html-italic">Svd</span> for foredune), and total sediment transport volume <span class="html-italic">Tsv</span> (<b>c</b>,<b>d</b>) with and without submerged vegetation. <span class="html-italic">H</span><sub>0</sub> is the incident wave height. N indicates “without vegetation”, S signifies “with vegetation”, d1 denotes a water depth of 0.48 m, d2 denotes a water depth of 0.55 m, and the final numbers 1–5 represent incident wave heights ranging from low to high (<a href="#jmse-12-02126-t001" class="html-table">Table 1</a>).</p> Full article ">Figure 8
<p>Correlation between change ratio of wave energy and relative incident wave height. <span class="html-italic">H</span><sub>0</sub> is the incident wave height; <span class="html-italic">d</span> is the water depth. The dashed line represents the fitted curve after removing the outlier near the arrow.</p> Full article ">Figure 9
<p>Fitted curves for the maximum erosion thickness of the sandbar <span class="html-italic">Tes</span> (<b>a</b>) and the erosion volume of the sandbar <span class="html-italic">Evs</span> (<b>b</b>) with and without submerged vegetation. <span class="html-italic">H</span><sub>0</sub> is the incident wave height; <span class="html-italic">R</span><sub>c</sub> is the sandbar freeboard; <span class="html-italic">d</span> is the water depth. The red and black dashed lines represent the fitted curves for scenarios with and without vegetation, respectively.</p> Full article ">Figure 10
<p>Fitted curves for the maximum erosion thickness of the foredune (<b>a</b>) and the erosion volume of the foredune (<b>b</b>) with and without submerged vegetation. The red and black dashed lines represent the fitted curves for scenarios with and without vegetation, respectively.</p> Full article ">Figure 11
<p>Fitted curves for the total sediment transport volume <span class="html-italic">Tsv</span> with and without submerged vegetation. <span class="html-italic">H</span><sub>0</sub> is the incident wave height; <span class="html-italic">d</span> is the water depth. The red and black dashed lines represent the fitted curves for scenarios with and without vegetation, respectively.</p> Full article ">
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