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J. Mar. Sci. Eng., Volume 10, Issue 10 (October 2022) – 235 articles

Cover Story (view full-size image): The Belgium sandy coastline is very vulnerable to erosion; therefore, the development of sustainable and nature-based coastal protection solutions is a key feature. Enhancing the settlement of the ecosystem engineer Lanice conchilega (Pallas, 1766), which stabilizes the sediment bed, could be a solution. This study describes the development of innovative artificial substrate screening methodologies in laboratory conditions. This is with the goal of delivering technical recommendations for the development of an appropriate industrial artificial substrate with which to enhance the settlement of L.conchilega larvae on sandy coastlines. Through the use of an artificial substrate, L.conchilega aggregations could be restored and lead to sustainable as well as nature-based coastal protection. View this paper
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10 pages, 2036 KiB  
Communication
Research on the Delimitation of Marine Spatial Pattern Based on the Goal of “Carbon Peaking and Carbon Neutrality”
by Qiwei Zhao, Xin Teng, Panpan Zhang, Wanchao Kang, Xue Meng and Shuang Wang
J. Mar. Sci. Eng. 2022, 10(10), 1566; https://doi.org/10.3390/jmse10101566 - 21 Oct 2022
Cited by 3 | Viewed by 1911
Abstract
In the context of carbon peaking and carbon neutrality (“double carbon”), it is urgent to clarify the effect of marine spatial planning (MSP) on carbon sink increases and emission reductions, since such planning acts as a spatial governance tool for the earth’s largest [...] Read more.
In the context of carbon peaking and carbon neutrality (“double carbon”), it is urgent to clarify the effect of marine spatial planning (MSP) on carbon sink increases and emission reductions, since such planning acts as a spatial governance tool for the earth’s largest carbon pool. In this paper, a linkage model between marine spatial functional zones and carbon distribution is established. To explore the relationship between marine spatial functional zones and carbon, the study analyzed the carbon increase or reduction role of sea-use activities in each zone and considered the carbon sequestration function of the marine ecosystem itself. A marine spatial pattern of “Two Spaces and Four Carbon Areas” is proposed to present the linkage. A carbon distribution pattern in marine space is delimited using the linkage model and the current MSP in the case study of the city of Tangshan, Hebei, China. Some measures have been taken or planned to be taken in Tangshan to improve the carbon sink function of the ecosystem and the marine space. The supporting role of MSP in achieving the “double carbon” goal is studied, and the paths and suggestions for integrating the “double carbon” goal into MSP are explored. Full article
(This article belongs to the Special Issue Application of Advanced Technologies in Maritime Safety)
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<p>Marine spatial pattern for “Two Spaces and Four Carbon Areas” based on the “double carbon” goal.</p> Full article ">Figure 2
<p>The “Two Spaces and Four Carbon Areas” marine spatial pattern of Tangshan, Hebei, China.</p> Full article ">
24 pages, 3245 KiB  
Article
Combined LOFAR and DEMON Spectrums for Simultaneous Underwater Acoustic Object Counting and F0 Estimation
by Liming Li, Sanming Song and Xisheng Feng
J. Mar. Sci. Eng. 2022, 10(10), 1565; https://doi.org/10.3390/jmse10101565 - 21 Oct 2022
Cited by 10 | Viewed by 3924
Abstract
In a typical underwater acoustic target detection mission, we have to estimate the target number (N), perform source separation when N>1, and consequently predict the motion parameters such as fundamental frequency (F0) from separated noises [...] Read more.
In a typical underwater acoustic target detection mission, we have to estimate the target number (N), perform source separation when N>1, and consequently predict the motion parameters such as fundamental frequency (F0) from separated noises for each target. Although deep learning methods have been adopted in each task, their successes strongly depend on the feed-in features. In this paper, we evaluate several time-frequency features and propose a universal feature extraction strategy for object counting and F0 estimation simultaneously, with a convolutional recurrent neural network (CRNN) as the backbone. On one hand, LOFAR and DEMON are feasible for low-speed and high-speed analysis, respectively, and are combined (LOFAR + DEMON) to cope with full-condition estimation. On the other hand, a comb filter (COMB) is designed and applied to the combined spectrum for harmonicity enhancement, which will be further streamed into the CRNN for prediction. Experiments show that (1) in the F0 estimation task, feeding the filtered combined feature (LOFAR + DEMON + COMB) into the CRNN achieves an accuracy of 98% in the lake trial dataset, which is superior to LOFAR + COMB (83%) or DEMON + COMB (94%) alone, demonstrating that feature combination is plausible. (2) In a counting task, the prediction accuracy of the combined feature (LOFAR + DEMON, COMB included or excluded) is comparable to the state-of-the-art on simulation dataset and dominates the rest on the lake trial dataset, indicating that LOFAR + DEMON can be used as a common feature for both tasks. (3) The inclusion of COMB accelerates the convergence speed of the F0 estimation task, however, it penalizes the counting task by a depression of 13% on average, partly due to the merging effects brought in by the broadband filtering of COMB. Full article
(This article belongs to the Special Issue Application of Sensing and Machine Learning to Underwater Acoustic)
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<p>The pipeline of our proposed algorithm for underwater acoustic object counting and <span class="html-italic">F</span><sub>0</sub> estimation. Again, when the target number <math display="inline"><semantics> <mrow> <mi>N</mi> <mo>&gt;</mo> <mn>1</mn> </mrow> </semantics></math>, mixed spectra must be separated into mono spectrums before feeding into the CRNN, and related works will be present in a future report, which is not a concern in this paper.</p> Full article ">Figure 2
<p>An example for LOFAR and DEMON spectrum in low-speed rotation (“Hailangdao" ship, 600 r·min<math display="inline"><semantics> <msup> <mrow/> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> </semantics></math>), with upper for LOFAR and lower for DEMON. Letter annotations are used to illustrate the harmonic relationship between spectral lines. The vertical axis represents time and the horizontal axis frequency.</p> Full article ">Figure 3
<p>An example of the LOFAR and DEMON spectrums in high-speed rotation (“Hailangdao” ship, 1200 r·min<math display="inline"><semantics> <msup> <mrow/> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> </semantics></math>), with upper for LOFAR and lower for DEMON. Letter and digital annotations are used to illustrate the harmonic relationship between spectral lines. The vertical axis represents time and the horizontal axis frequency.</p> Full article ">Figure 4
<p>The enhanced version of the combined LOFAR and DEMON spectrum in <a href="#jmse-10-01565-f003" class="html-fig">Figure 3</a> with comb filtering, where the frequency (horizontal) axis is shown logarithmically for better visualization.</p> Full article ">Figure 5
<p>Structure of our CRNN.</p> Full article ">Figure 6
<p>Eigenray trace of sound propagation during simulation with the Bellhop toolkit. The rays are plotted using different colors depending on whether the ray hits one or both boundaries, red for direct, green for the surface, blue for the bottom, and black for both. The multi-path effect is clearly shown above.</p> Full article ">Figure 7
<p>Spectrum of 1 s radiated noise before and after the Bellhop simulation.</p> Full article ">Figure 8
<p>Environment and settings for lake trial, see the text for details.</p> Full article ">Figure 9
<p>Training process with different features on simulation dataset for the <span class="html-italic">F</span><sub>0</sub> estimation task, L + D is short for LOFAR + DEMON. The upper panel shows the loss and accuracy on the training dataset while the lower panel for validation dataset.</p> Full article ">Figure 10
<p>Feature spectrum of noises with the same blade frequency leading to classification errors. The confusion matrix for all fundamental frequencies is presented in (<b>a</b>). The red circle marks the largest classification errors, and the corresponding DEMON spectrum of one category is displayed in (<b>b</b>).</p> Full article ">Figure 11
<p>Training process with different features, L + D + COMB is an abbreviation for LOFAR + DEMON + COMB. The upper panel shows the loss and accuracy on the training dataset while the lower panel for validation dataset.</p> Full article ">Figure 12
<p>Confusion between spectral lines and electrical noises-induced pseudo-lines. (<b>a</b>,<b>c</b>) correspond to the STFT and GST feature of background noise (target absent); (<b>b</b>,<b>d</b>) give the confusion matrix of STFT and GST.</p> Full article ">Figure 13
<p>The confusion matrix of different features for <span class="html-italic">F</span><sub>0</sub> estimation on the lake trial dataset (COMB included). The GST is neglected since it has the same overall accuracy as STFT (<b>c</b>). (<b>a</b>,<b>b</b>,<b>d</b>) represent the cofusion matrix when LOFAR, DEMON and LOFAR + DEMON are employed for classification, respectively.</p> Full article ">
19 pages, 1990 KiB  
Article
Multi-Faceted Analysis of Airborne Noise Impact in the Port of Split (I)
by Luka Vukić, Ivan Peronja and Roko Glavinović
J. Mar. Sci. Eng. 2022, 10(10), 1564; https://doi.org/10.3390/jmse10101564 - 21 Oct 2022
Cited by 14 | Viewed by 1902
Abstract
This multi-faceted study deals with the analysis of the impact of noise emissions from the cargo terminals in the port of Split, especially in view of the proximity to inhabited areas and the growing number of registered issues and concerns due to its [...] Read more.
This multi-faceted study deals with the analysis of the impact of noise emissions from the cargo terminals in the port of Split, especially in view of the proximity to inhabited areas and the growing number of registered issues and concerns due to its particular location. Three objectives are pursued: the identification of noise sources in the port area, an overview of strategic noise maps and simulations of noise propagation from ships at berth, and the calculation of external costs of noise pollution. In the first, preliminary part of the research project, by conducting a monitoring campaign and analyzing the data on strategic noise maps of the studied area, road and rail traffic were estimated as the main noise sources causing excessive noise emissions for all assessment periods: day (Lday), evening (Levening), night (Lnight), and day-evening-night (Lden) period. Industrial resources, including ports, were identified as having marginal noise emission levels. The calculation of the total external noise costs results in a damage value of €190,166/year, considering the number of affected inhabitants and the assumed noise levels. As an added value of the study, the simulation results of two scenarios have determined the noise propagation of a ship at berth and highlighted the zone of excessive noise under certain conditions. The results of this study should encourage the relevant institutions to strengthen noise management plans and introduce effective and continuous monitoring of noise emissions in critical areas. Full article
(This article belongs to the Section Ocean Engineering)
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<p>Flowchart of the study.</p> Full article ">Figure 2
<p>Overview of the cargo basin and infrastructure with indication of its functions ([<a href="#B46-jmse-10-01564" class="html-bibr">46</a>], modified).</p> Full article ">Figure 3
<p>Aerial view of the cargo terminal in the port of Split [<a href="#B49-jmse-10-01564" class="html-bibr">49</a>].</p> Full article ">Figure 4
<p>Strategic maps of road noise sources in the evaluation periods (L<sub>day</sub>, L<sub>evening</sub>, L<sub>night</sub>, L<sub>den</sub>) * [<a href="#B55-jmse-10-01564" class="html-bibr">55</a>]. * noise indicator class (dB).</p> Full article ">Figure 5
<p>Strategic maps of rail noise sources in the evaluation periods (L<sub>day</sub>, L<sub>evening</sub>, L<sub>night</sub>, L<sub>den</sub>) * [<a href="#B55-jmse-10-01564" class="html-bibr">55</a>]. * noise indicator class (dB) as in <a href="#jmse-10-01564-f004" class="html-fig">Figure 4</a>.</p> Full article ">Figure 6
<p>Strategic maps of industrial noise sources in the evaluation periods (L<sub>day</sub>, L<sub>evening</sub>, L<sub>night</sub>, L<sub>den</sub>) * [<a href="#B55-jmse-10-01564" class="html-bibr">55</a>]. * noise indicator class (dB) as in <a href="#jmse-10-01564-f004" class="html-fig">Figure 4</a>.</p> Full article ">Figure 7
<p>Main noise categories in the research area of Split port ([<a href="#B56-jmse-10-01564" class="html-bibr">56</a>], modified).</p> Full article ">Figure 8
<p>Simulation of Scenario 1 ([<a href="#B46-jmse-10-01564" class="html-bibr">46</a>], modified).</p> Full article ">Figure 9
<p>Simulation of Scenario 2 ([<a href="#B46-jmse-10-01564" class="html-bibr">46</a>], modified).</p> Full article ">
20 pages, 2631 KiB  
Article
A Variational Bayesian-Based Simultaneous Localization and Mapping Method for Autonomous Underwater Vehicle Navigation
by Pengcheng Mu, Xin Zhang, Ping Qin and Bo He
J. Mar. Sci. Eng. 2022, 10(10), 1563; https://doi.org/10.3390/jmse10101563 - 21 Oct 2022
Cited by 4 | Viewed by 2368
Abstract
Simultaneous Localization and Mapping (SLAM) is a well-known solution for mapping and realizing autonomous navigation of an Autonomous Underwater Vehicle (AUV) in unknown underwater environments. However, the inaccurate time-varying observation noise will cause filtering divergence and reduce the accuracy of localization and feature [...] Read more.
Simultaneous Localization and Mapping (SLAM) is a well-known solution for mapping and realizing autonomous navigation of an Autonomous Underwater Vehicle (AUV) in unknown underwater environments. However, the inaccurate time-varying observation noise will cause filtering divergence and reduce the accuracy of localization and feature estimation. In this paper, VB-AUFastSLAM based on the unscented-FastSLAM (UFastSLAM) and the Variational Bayesian (VB) is proposed. The UFastSLAM combines unscented particle filter (UPF) and unscented Kalman filter (UKF) to estimate the AUV poses and features. In addition, to resist the unknown time-varying observation noise, the method of Variational Bayesian learning is introduced into the SLAM framework. Firstly, the VB method is used to estimate the joint posterior probability of the AUV path and observation noise. The Inverse-Gamma distribution is used to model the observation noise and real-time noise parameters estimation is performed to improve the AUV localization accuracy. Secondly, VB is reused to estimate the noise parameters in the feature update stage to enhance the performance of the feature estimation. The proposed algorithms are first validated in an open-source simulation environment. Then, an AUV SLAM system based on the Inertial Navigation System (INS), Doppler Velocity Log (DVL), and single-beam Sonar are also built to verify the effectiveness of the proposed algorithms in the marine environment. The accuracy of the proposed methods can reach 0.742% and 0.776% of the range, respectively, which is much better than 1.825% and 1.397% of the traditional methods. Full article
(This article belongs to the Special Issue Earth System Modeling, Data Assimilation, Artificial Intelligence, Deep Learning and Ocean Information Engineering)
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<p>The Sailfish-210 AUV.</p> Full article ">Figure 2
<p>Navigation coordinate system of Sailfish AUV.</p> Full article ">Figure 3
<p>Simulation Environment.</p> Full article ">Figure 4
<p>Dynamic observation noise is set to n = 3; <span class="html-italic">p</span> = 0.2.</p> Full article ">Figure 5
<p><span class="html-italic">n</span> = 2; <span class="html-italic">p</span> = 0.3, algorithms error comparison.</p> Full article ">Figure 6
<p>Comparison of Neff with different observation noise.</p> Full article ">Figure 7
<p>AUV trajectory.</p> Full article ">Figure 8
<p>Sailfish-210 during the mission.</p> Full article ">Figure 9
<p>Comparison chart of sea trial trajectory and error comparison of four algorithms.</p> Full article ">Figure 10
<p>The results of the proposed two algorithms for building the map.</p> Full article ">
20 pages, 5880 KiB  
Article
An Improved Underwater Recognition Algorithm for Subsea X-Tree Key Components Based on Deep Transfer Learning
by Wangyuan Zhao, Fenglei Han, Zhihao Su, Xinjie Qiu, Jiawei Zhang and Yiming Zhao
J. Mar. Sci. Eng. 2022, 10(10), 1562; https://doi.org/10.3390/jmse10101562 - 21 Oct 2022
Cited by 1 | Viewed by 1875
Abstract
It is promising to detect or maintain subsea X-trees using a remote operated vehicle (ROV). In this article, an efficient recognition model for the subsea X-tree component is proposed to assist in the autonomous operation of unmanned underwater maintenance vehicles: an efficient network [...] Read more.
It is promising to detect or maintain subsea X-trees using a remote operated vehicle (ROV). In this article, an efficient recognition model for the subsea X-tree component is proposed to assist in the autonomous operation of unmanned underwater maintenance vehicles: an efficient network module, SX(subsea X-tree)-DCANet, is designed to replace the CSPBlock of YOLOv4-tiny with ResBlock-D and combine with the ECANet attention module. In addition, two-stage transform learning is used for the insufficiency of underwater target recognition samples as well as the overfitting caused by the subsea target recognition model, thereby providing an effective learning strategy for traditional subsea target recognition. A mosaic data augment algorithm and cosine annealing algorithm are also utilized for better accuracy of network training. The results of ablation studies show that the mean Average Precision (mAP) and speed of the improved algorithm are increased by 1.58% and 10.62%, respectively. Multiple field experiments on the laboratory, experimental pool, and the hydro-electric station prove that the recognition algorithm and training strategy present in this article can be well applied in subsea X-tree component recognition, and can effectively promote the development of intelligent subsea oil extraction projects. Full article
(This article belongs to the Section Ocean Engineering)
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<p>VR simulation of X-tree operation and introduction of key parts. (<b>a</b>) The main body of the X-tree, which is the control channel and monitoring equipment for the production fluid, contains a large number of connectors or buttons. (<b>b</b>) Subsea flexible pipeline component.</p> Full article ">Figure 2
<p>Structure diagram of YOLOv4-tiny.</p> Full article ">Figure 3
<p>Structure diagram of Efficient Channel Attention Module.</p> Full article ">Figure 4
<p>Structure diagram of CSPBlock and ResBlock-D.</p> Full article ">Figure 5
<p>Schematic diagram of algorithm network structure proposed in this paper.</p> Full article ">Figure 6
<p>Flowchart of prediction result decoding.</p> Full article ">Figure 7
<p>Model diagram of two-stage deep transfer learning.</p> Full article ">Figure 8
<p>Flowchart of mosaic image data enhancement algorithm. (Four randomly selected images 1, 2, 3 and 4 were cropped and then spliced into a training sample with set side length).</p> Full article ">Figure 9
<p>Introduction to the robot structure used in the pool test.</p> Full article ">Figure 10
<p>Recognition task application background: used to assist localization in virtual simulation of underwater VR systems.</p> Full article ">Figure 11
<p>The corresponding values of different evaluation functions in continuously increasing training epoch.</p> Full article ">Figure 12
<p>Pre-training model dataset recognition results: simple identification and testing in the office.</p> Full article ">Figure 13
<p>Pool test carried out at different depths in different pools to test the reliability of the algorithms presented in this paper. (<b>a</b>) Depth within 0–1 m, color has changed dramatically and some images have deteriorated. (<b>b</b>) Depth within 1.5–4 m, severe color and pixel attenuation makes it difficult to distinguish the type of part.</p> Full article ">Figure 14
<p>Columns 1–3: Different scoring thresholds correspond to different evaluation function values. Column 4: Recall corresponds to Precision. Identification target for each row (from top to bottom): bend limiter, electric connector, ROV control panel knob, set down position, terminal connector, umbilical cable terminal connector, and valve operating handle.</p> Full article ">Figure 15
<p>Field test: verify the feasibility and generalization of the proposed scheme through turbid water area of hydropower station.</p> Full article ">
21 pages, 11617 KiB  
Article
Study on Fatigue Spectrum Analysis and Reliability Analysis of Multilayer Flexible Riser
by Jianxing Yu, Fucheng Wang, Yang Yu, Haoda Li, Xin Liu and Ruoke Sun
J. Mar. Sci. Eng. 2022, 10(10), 1561; https://doi.org/10.3390/jmse10101561 - 21 Oct 2022
Cited by 4 | Viewed by 2264
Abstract
Multilayer composite flexible risers have been widely used in engineering. However, this type of structure is complex, as there are influences between layers. Moreover, a range of uncertain factors need to be considered in fatigue analysis. Therefore, it is difficult to perform the [...] Read more.
Multilayer composite flexible risers have been widely used in engineering. However, this type of structure is complex, as there are influences between layers. Moreover, a range of uncertain factors need to be considered in fatigue analysis. Therefore, it is difficult to perform the fatigue analysis research of multilayer flexible risers. In this paper, the fatigue spectrum analysis and reliability analysis method of a nine-layer flexible riser structure are proposed, and a complete fatigue and reliability analysis process for multilayer structures is developed. The theoretical basis of the fatigue spectrum analysis method is introduced, and the calculation program is described. Finite element software is used to analyze the stress of the multilayer flexible riser under the influence of the upper platform structure movement and the ocean current. Moreover, the stress response of the riser structure of each layer is obtained. According to this, the irregular wave load is simulated by the method of random number simulation, and the stress response spectrum is formed. Then, the appropriate S-N curve is selected to calculate the fatigue damage degree of each layer, and the fatigue damage nephogram is displayed, so as to analyze the structural fatigue damage. Finally, the uncertainty in the process of fatigue damage calculation is analyzed. According to the results, the methods of multilayer riser analysis are summarized and the future research directions are put forward. Full article
(This article belongs to the Special Issue Fatigue and Fracture Mechanics of Marine Structures)
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<p>The selection of a typical section of rain-flow counting.</p> Full article ">Figure 2
<p>The fatigue spectrum analysis process.</p> Full article ">Figure 3
<p>A schematic diagram of the multilayer flexible riser structure.</p> Full article ">Figure 4
<p>A finite element model of a multilayer flexible riser.</p> Full article ">Figure 5
<p>The sectional view of a multi-storey structure.</p> Full article ">Figure 6
<p>The boundary conditions of a multi-story structure.</p> Full article ">Figure 7
<p>A stress nephogram under axial pressure. (<b>a</b>) The riser; (<b>b</b>) Skeleton layer; (<b>c</b>) Compressive armor layer; (<b>d</b>) Internal tensile armor layer; (<b>e</b>) External tensile armor layer.</p> Full article ">Figure 8
<p>A stress nephogram under bending moment. (<b>a</b>) The riser; (<b>b</b>) Skeleton layer; (<b>c</b>) Compressive armor layer; (<b>d</b>) Internal tensile armor layer; (<b>e</b>) External tensile armor layer.</p> Full article ">Figure 9
<p>A stress nephogram under torsion. (<b>a</b>) The riser; (<b>b</b>) Skeleton layer; (<b>c</b>) Compressive armor layer; (<b>d</b>) Internal tensile armor layer; (<b>e</b>) External tensile armor layer.</p> Full article ">Figure 9 Cont.
<p>A stress nephogram under torsion. (<b>a</b>) The riser; (<b>b</b>) Skeleton layer; (<b>c</b>) Compressive armor layer; (<b>d</b>) Internal tensile armor layer; (<b>e</b>) External tensile armor layer.</p> Full article ">Figure 10
<p>The stress response spectrum for the riser.</p> Full article ">Figure 11
<p>Fatigue damage of the skeleton layer. (<b>a</b>) The fatigue damage nephogram under axial compressive load; (<b>b</b>) The fatigue damage nephogram under bending moment; (<b>c</b>) The fatigue damage nephogram under torsion.</p> Full article ">Figure 11 Cont.
<p>Fatigue damage of the skeleton layer. (<b>a</b>) The fatigue damage nephogram under axial compressive load; (<b>b</b>) The fatigue damage nephogram under bending moment; (<b>c</b>) The fatigue damage nephogram under torsion.</p> Full article ">Figure 12
<p>Fatigue damage of a compressive armor layer. (<b>a</b>) The fatigue damage nephogram under axial compressive load; (<b>b</b>) The fatigue damage nephogram under bending moment; (<b>c</b>) The fatigue damage nephogram under torsion.</p> Full article ">Figure 12 Cont.
<p>Fatigue damage of a compressive armor layer. (<b>a</b>) The fatigue damage nephogram under axial compressive load; (<b>b</b>) The fatigue damage nephogram under bending moment; (<b>c</b>) The fatigue damage nephogram under torsion.</p> Full article ">Figure 13
<p>Fatigue damage of an internal tensile armor layer. (<b>a</b>) The fatigue damage nephogram under axial compressive load; (<b>b</b>) The fatigue damage nephogram under bending moment; (<b>c</b>) The fatigue damage nephogram under torsion.</p> Full article ">Figure 13 Cont.
<p>Fatigue damage of an internal tensile armor layer. (<b>a</b>) The fatigue damage nephogram under axial compressive load; (<b>b</b>) The fatigue damage nephogram under bending moment; (<b>c</b>) The fatigue damage nephogram under torsion.</p> Full article ">Figure 14
<p>Fatigue damage of an external tensile armor layer. (<b>a</b>) The fatigue damage nephogram under axial compressive load; (<b>b</b>) The fatigue damage nephogram under bending moment; (<b>c</b>) The fatigue damage nephogram under torsion.</p> Full article ">Figure 14 Cont.
<p>Fatigue damage of an external tensile armor layer. (<b>a</b>) The fatigue damage nephogram under axial compressive load; (<b>b</b>) The fatigue damage nephogram under bending moment; (<b>c</b>) The fatigue damage nephogram under torsion.</p> Full article ">Figure 15
<p>The reliability of metal layer changes with time.</p> Full article ">
1 pages, 164 KiB  
Correction
Correction: Liu et al. Advancing Three-Dimensional Coupled Water Quality Model of Marine Ranches: Model Development, Global Sensitivity Analysis, and Optimization Based on Observation System. J. Mar. Sci. Eng. 2022, 10, 1028
by Yongzhi Liu, Fan Jiang, Zihan Zhao, Tana and Xianqing Lv
J. Mar. Sci. Eng. 2022, 10(10), 1560; https://doi.org/10.3390/jmse10101560 - 21 Oct 2022
Viewed by 1081
Abstract
In the original publication [...] Full article
(This article belongs to the Section Physical Oceanography)
17 pages, 7460 KiB  
Article
The Impact of the Crude Oil Price on Tankers’ Port-Call Features: Mining the Information in Automatic Identification System
by Jackson Jinhong Mi, Xiangyan Meng, Yanhui Chen and Yicheng Wang
J. Mar. Sci. Eng. 2022, 10(10), 1559; https://doi.org/10.3390/jmse10101559 - 20 Oct 2022
Cited by 1 | Viewed by 4944
Abstract
Ship navigation technical data contains a lot of information. In this paper, we explore a relationship between the crude oil price and tankers’ port-call features by mining the information recorded in Automatic Identification System (AIS), which extends the application field of ship navigation [...] Read more.
Ship navigation technical data contains a lot of information. In this paper, we explore a relationship between the crude oil price and tankers’ port-call features by mining the information recorded in Automatic Identification System (AIS), which extends the application field of ship navigation technical data and aims to help oil shipping enterprises and port enterprises to arrange operation plans in advance. We generate a monthly panel data over the period from 2010 to 2020 of major global ports located in main crude oil exporting countries from AIS data. By using the panel fixed-effect model and binary logit model, our empirical results innovatively present the tanker’s monthly port-call features are influenced by crude oil price fluctuation through four dimensions, that is the tankers’ port-call numbers, the average docking time, total gross tonnage of the docking tankers and the number of different docking tankers. With these variables, we attempt to analyze the relationship between crude oil price fluctuation on tankers’ port-call features. The results of the study are helpful to comprehensively understand the impact mechanism of the crude oil price on the tankers’ port-call features. Full article
(This article belongs to the Special Issue Contemporary Shipping Logistics and Port Management)
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<p>The first 20 largest oil exporting countries in 2020.</p> Full article ">Figure 2
<p>The frequency of port-calls, the average time of docking on ports and the sum of total gross tonnage of the tankers, in 2010–2020.</p> Full article ">Figure 3
<p>(<b>a</b>) The oil future price and the frequency of tankers’ port-calls per month. (<b>b</b>) The oil future price and total docking time of all the tankers per month. (<b>c</b>) The oil future price and the sum of all the tankers’ gross tonnage per month.</p> Full article ">Figure 3 Cont.
<p>(<b>a</b>) The oil future price and the frequency of tankers’ port-calls per month. (<b>b</b>) The oil future price and total docking time of all the tankers per month. (<b>c</b>) The oil future price and the sum of all the tankers’ gross tonnage per month.</p> Full article ">
15 pages, 532 KiB  
Article
Configuration Analysis of Factors Influencing Port Competitiveness of Hinterland Cities under TOE Framework: Evidence from China
by Zhenyu Huang, Ying Yang and Fengmei Zhang
J. Mar. Sci. Eng. 2022, 10(10), 1558; https://doi.org/10.3390/jmse10101558 - 20 Oct 2022
Cited by 7 | Viewed by 2933
Abstract
Attention is increasingly being paid to the influence of hinterland cities on port competitiveness, but in-depth research is lacking on the formation conditions and mechanism of hinterland cities’ influence on port competitiveness. Based on the technology–organization–environment (TOE) framework and the characteristics of Chinese [...] Read more.
Attention is increasingly being paid to the influence of hinterland cities on port competitiveness, but in-depth research is lacking on the formation conditions and mechanism of hinterland cities’ influence on port competitiveness. Based on the technology–organization–environment (TOE) framework and the characteristics of Chinese government organizational behavior, in this study, we used fuzzy-set qualitative comparative analysis (fsQCA) to conduct a condition configuration analysis of 21 coastal ports and their hinterland cities in China. The findings showed the following: (1) The technology, organization, and environment conditions of hinterland cities cannot provide the necessary conditions for high or low port competitiveness alone: different combinations of these conditions have produced three high and four low port competitiveness configurations. (2) The three configurations of high port competitiveness are the organization–environment, economy–balance, and finance–balance types. Adequate government financial supply, high tertiary industry proportion, good economic development, and market openness are the core conditions required for achieving high port competitiveness. (3) The four configurations of low port competitiveness are finance–facilities–environment, capability–finance–environment, technology–finance–economy, and capability–industry–economy restrictions. Here, low-level innovation capability, inadequate government financial supply, and low tertiary industry proportion are the core conditions leading to low port competitiveness. We revealed the concurrent synergistic effect of the three conditions of technology, organization, and environment in hinterland cities and demonstrated the causal complexity and asymmetry of the impact of hinterland cities on port competitiveness. Our conclusions provide empirical evidence that will aid hinterland cities in formulating differentiated port competitiveness promotion policies according to their own conditions and endowments. Full article
(This article belongs to the Special Issue Sustainable Operations in Maritime Industry)
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<p>Configuration model based on TOE framework.</p> Full article ">
12 pages, 3729 KiB  
Article
Study of Crack Closure Effect of Hull Plate under Low Cycle Fatigue
by Qin Dong, Mengyuan Rong and Geng Xu
J. Mar. Sci. Eng. 2022, 10(10), 1557; https://doi.org/10.3390/jmse10101557 - 20 Oct 2022
Cited by 3 | Viewed by 1698
Abstract
The crack closure phenomenon significantly influences low cycle fatigue (LCF) crack growth. The crack closure theory deems that a crack can grow only when the applied load is greater than the fatigue crack opening and closing loads. The revised crack closure theory proposed [...] Read more.
The crack closure phenomenon significantly influences low cycle fatigue (LCF) crack growth. The crack closure theory deems that a crack can grow only when the applied load is greater than the fatigue crack opening and closing loads. The revised crack closure theory proposed in this paper provides a new understanding of crack growth: It is no longer the range of stress intensity factor ΔK that controls the crack growth rate, but the effective stress intensity factor ΔKeff. Therefore, it is of great importance to study the crack closure phenomenon of LCF. A combination of experiments and the finite element method (FEM) was used to study the effect of overload on the crack closure effect, and the study was carried out using compact tensile (CT) specimens made of AH32 steel. The FEM was used to obtain the stress changes near the crack tip and the opening displacement changes in the crack trailing area after a single tensile overload, to study the intrinsic mechanism of overload on crack closure, and to obtain the LCF crack opening and closing loads by the nodal displacement method. The effect of overload on crack morphology was observed by using high-magnification electron microscopy in combination with testing. Full article
(This article belongs to the Special Issue Modeling and Simulation of Moored Floating Structures)
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<p>CT specimen geometry (mm).</p> Full article ">Figure 2
<p>Finite element model and refined mesh.</p> Full article ">Figure 3
<p>Schematic diagram of the preliminary analysis of the crack closure process. (<b>a</b>) Crack closure process in a block cycle; (<b>b</b>) crack closure increasing process; (<b>c</b>) crack closure weakening process.</p> Full article ">Figure 4
<p>Crack tip stress distribution after overload. (<b>a</b>) a = 3.55 mm; (<b>b</b>) a = 3.6 mm; (<b>c</b>) a= 4.55 mm; (<b>d</b>) a = 5.0 mm.</p> Full article ">Figure 5
<p>Crack opening displacement change after overload. (<b>a</b>) a = 3.55 mm; (<b>b</b>) a = 3.6 mm; (<b>c</b>) a = 4.55 mm; (<b>d</b>) a = 5 mm.</p> Full article ">Figure 6
<p>Crack opening and closing loads under different overload ratios. (<b>a</b>) LCF crack opening load; (<b>b</b>) LCF crack closure load.</p> Full article ">Figure 7
<p>Schematic diagram of LCF crack closure test device for CT specimens.</p> Full article ">Figure 8
<p>Schematic diagram of opening load determination.</p> Full article ">Figure 9
<p>Fatigue crack opening curves under different overload ratios.</p> Full article ">Figure 10
<p>LCF crack closure parameters for different overload ratios U.</p> Full article ">Figure 11
<p>Overload crack tip morphology. (<b>a</b>) Before overload; (<b>b</b>) at overload; (<b>c</b>) unloading minimum load after overload; (<b>d</b>) some distance after overload.</p> Full article ">
24 pages, 5593 KiB  
Article
Research on Multi-Energy Integrated Ship Energy Management System Based on Hierarchical Control Collaborative Optimization Strategy
by Yuanjie Ren, Lanyong Zhang, Peng Shi and Ziqi Zhang
J. Mar. Sci. Eng. 2022, 10(10), 1556; https://doi.org/10.3390/jmse10101556 - 20 Oct 2022
Cited by 8 | Viewed by 3134
Abstract
The propulsion systems of hybrid electric ship output and load demand have substantial volatility and uncertainty, so a hierarchical collaborative control energy management scheme of the ship propulsion system is proposed in this paper. In a layer of control scheme, the traditional perturbation [...] Read more.
The propulsion systems of hybrid electric ship output and load demand have substantial volatility and uncertainty, so a hierarchical collaborative control energy management scheme of the ship propulsion system is proposed in this paper. In a layer of control scheme, the traditional perturbation algorithm is improved. Increasing the oscillation detection mechanism and establishing the dynamic disturbance step length realizes the real-time stability of maximum power point tracking control. In the second-layer control scheme, the power sensitivity factor and voltage and current double closed-loop controller is introduced. By designing a two-layer coordinated control strategy based on the dynamic droop coefficient, the problem of voltage and frequency deviation caused by load switching is solved. In the third-layer control scheme, due to the need of the optimal scheduling function, the multi-objective particle swarm optimization algorithm was improved through three aspects: introducing the mutation factor, improving the speed formula, and re-initializing the strategy. Compared with other algorithms, this algorithm proves its validity in day-ahead optimal scheduling strategy. The superiority of the hierarchical collaborative optimization control schemes proposed was verified, in which power loss was reduced by 39.3%, the overall tracking time was prolonged by 15.4%, and the environmental cost of the diesel generator was reduced by 8.4%. The control strategy solves the problems of the steady-state oscillation stage and deviation from the tracking direction, which can effectively suppress voltage and frequency fluctuations. Full article
(This article belongs to the Special Issue Smart Control of Ship Propulsion System)
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<p>Typical system structure diagram.</p> Full article ">Figure 2
<p>Schematic diagram of the diesel engine system.</p> Full article ">Figure 3
<p>Block diagram of diesel engine speed regulation system.</p> Full article ">Figure 4
<p>Diesel engine speed control system model.</p> Full article ">Figure 5
<p>Simulation diagram of synchronous generator and excitation system.</p> Full article ">Figure 6
<p>Equivalent circuit diagram of single diode model.</p> Full article ">Figure 7
<p>Simulation curves of <math display="inline"><semantics> <mrow> <mi>I</mi> <mo>–</mo> <mi>V</mi> </mrow> </semantics></math> (<b>a</b>) and <math display="inline"><semantics> <mrow> <mi>P</mi> <mo>–</mo> <mi>V</mi> </mrow> </semantics></math> (<b>b</b>) for PV power generation system.</p> Full article ">Figure 8
<p>Photovoltaic power generation system simulation diagram.</p> Full article ">Figure 9
<p>Schematic diagram of the doubly-fed wind power generation system.</p> Full article ">Figure 10
<p>Model of a doubly-fed wind power generation system.</p> Full article ">Figure 11
<p>Simulation model of energy storage system.</p> Full article ">Figure 12
<p>Design diagram of the hierarchical control scheme.</p> Full article ">Figure 13
<p>Flowchart of the improved P&amp;O algorithm.</p> Full article ">Figure 14
<p>Block diagram of the droop controller.</p> Full article ">Figure 15
<p>Photovoltaic power generation system simulation diagram. (<b>a</b>) Irradiance variation diagram; (<b>b</b>) output power curve diagram.</p> Full article ">Figure 16
<p>Simulation diagram of wind power generation system. (<b>a</b>) Wind power system output power; (<b>b</b>) <math display="inline"><semantics> <mrow> <msub> <mi>C</mi> <mi>p</mi> </msub> </mrow> </semantics></math> curve.</p> Full article ">Figure 17
<p>Simulation result of control system. (<b>a</b>) Current waveform; (<b>b</b>) Voltage waveform; (<b>c</b>) System frequency.</p> Full article ">Figure 18
<p>Load day-ahead power forecast (<b>a</b>) and renewable energy day-ahead power forecast (<b>b</b>).</p> Full article ">Figure 19
<p>Power curve of each power generation (<b>a</b>) and SOC curve of energy storage system (<b>b</b>).</p> Full article ">Figure 20
<p>Algorithm comparison graph.</p> Full article ">
22 pages, 5480 KiB  
Article
Longitudinal Vibration Transmission Control of Marine Propulsion Shafting with Friction Damper Integrated into the Thrust Bearing
by Ganbo Zhang, Yao Zhao and Wei Chu
J. Mar. Sci. Eng. 2022, 10(10), 1555; https://doi.org/10.3390/jmse10101555 - 20 Oct 2022
Cited by 8 | Viewed by 2232
Abstract
Propeller-induced longitudinal vibration resonance in marine propulsion shafting systems causes great harm to the hull structure and is the primary source of shipboard noise. Integrating a friction damper with designed parameters into thrust bearings can prevent these issues. To investigate the performance of [...] Read more.
Propeller-induced longitudinal vibration resonance in marine propulsion shafting systems causes great harm to the hull structure and is the primary source of shipboard noise. Integrating a friction damper with designed parameters into thrust bearings can prevent these issues. To investigate the performance of the damper-integrated thrust bearing in longitudinal vibration transmission control, an experimental and theoretical study is carried out in a laboratory-assembled test rig, which consists of components similar to the existing marine propulsion system. We developed a prototype of a thrust bearing designed with a friction-damping generation that allows switching from two supporting states, i.e., damper-connected and damper-disconnected states. Furthermore, a nonlinear analysis method for friction dampers is proposed. By this method, the way in which the friction damper changes the dynamic characteristics of the shafting system is analyzed. Based on the test rig, the acceleration frequency response function (AFRF) of the thrust bearing with and without a friction damper is measured. By comparison, the effectiveness of the friction damper is proved. The experimental results show that the friction damper suppresses the shafting longitudinal vibration response in a broadband frequency range and also confirms the stability of the damping effect, which does not change with the shafting rotational speed or static thrust from the propeller. Full article
(This article belongs to the Special Issue Ship Dynamics and Hydrodynamics)
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<p>Support structure of the Kingsbury thrust bearing.</p> Full article ">Figure 2
<p>Friction damper integrated into the thrust bearing.</p> Full article ">Figure 3
<p>The state of friction damping: (<b>a</b>) rigid support state; (<b>b</b>) hydraulic support state.</p> Full article ">Figure 4
<p>Test device of frictional force: (<b>a</b>) schematic diagram; (<b>b</b>) photograph.</p> Full article ">Figure 5
<p>Test results of single piston frictional force with respect to hydraulic pressure.</p> Full article ">Figure 6
<p>Two-degree-of-freedom model of friction damper.</p> Full article ">Figure 7
<p>Computational flowchart for the nonlinear analysis method of frictional force.</p> Full article ">Figure 8
<p>The designed test rig with a reduced scale in the laboratory: (<b>a</b>) schematic diagram; (<b>b</b>) photograph.</p> Full article ">Figure 9
<p>Mathematical model of test rig without control from friction damper.</p> Full article ">Figure 10
<p>Measured AFRF of thrust bearing without friction damper.</p> Full article ">Figure 11
<p>Deformations under the action of different thrusts.</p> Full article ">Figure 12
<p>Comparison of theoretical and experimental AFRF of the thrust bearing.</p> Full article ">Figure 13
<p>Mathematical model of test rig with friction damper.</p> Full article ">Figure 14
<p>Theoretical AFRF of thrust bearing with friction damper for various values of <math display="inline"><semantics> <mrow> <mi>ξ</mi> </mrow> </semantics></math>.</p> Full article ">Figure 15
<p>Hysteresis loops of friction damper for <math display="inline"><semantics> <mrow> <mi mathvariant="sans-serif">ξ</mi> <mo>=</mo> <mn>0.7</mn> <mo>,</mo> <mo> </mo> <mn>1.4</mn> <mo>,</mo> <mo> </mo> <mn>3.5</mn> </mrow> </semantics></math>.</p> Full article ">Figure 16
<p>Effects of different dynamic parameters: (<b>a</b>) <math display="inline"><semantics> <mrow> <msub> <mi mathvariant="bold-italic">M</mi> <mi mathvariant="bold-italic">a</mi> </msub> <mo>;</mo> <mo> </mo> </mrow> </semantics></math>(<b>b</b>) <math display="inline"><semantics> <mrow> <msub> <mi mathvariant="bold-italic">η</mi> <mi mathvariant="bold-italic">a</mi> </msub> </mrow> </semantics></math>; (<b>c</b>) <math display="inline"><semantics> <mrow> <msub> <mi mathvariant="bold-italic">K</mi> <mi mathvariant="bold-italic">o</mi> </msub> </mrow> </semantics></math>; (<b>d</b>) <math display="inline"><semantics> <mrow> <msub> <mi mathvariant="bold-italic">C</mi> <mi mathvariant="bold-italic">o</mi> </msub> </mrow> </semantics></math>; (<b>e</b>)<math display="inline"><semantics> <mrow> <mo> </mo> <msub> <mi mathvariant="bold-italic">M</mi> <mi mathvariant="bold-italic">t</mi> </msub> </mrow> </semantics></math>; (<b>f</b>) <math display="inline"><semantics> <mrow> <msub> <mi mathvariant="bold-italic">η</mi> <mi mathvariant="bold-italic">t</mi> </msub> </mrow> </semantics></math>; (<b>g</b>) <math display="inline"><semantics> <mrow> <msub> <mi mathvariant="bold-italic">M</mi> <mi mathvariant="bold-italic">f</mi> </msub> </mrow> </semantics></math>; (<b>h</b>) <math display="inline"><semantics> <mrow> <msub> <mi mathvariant="bold-italic">η</mi> <mi mathvariant="bold-italic">f</mi> </msub> </mrow> </semantics></math>.</p> Full article ">Figure 16 Cont.
<p>Effects of different dynamic parameters: (<b>a</b>) <math display="inline"><semantics> <mrow> <msub> <mi mathvariant="bold-italic">M</mi> <mi mathvariant="bold-italic">a</mi> </msub> <mo>;</mo> <mo> </mo> </mrow> </semantics></math>(<b>b</b>) <math display="inline"><semantics> <mrow> <msub> <mi mathvariant="bold-italic">η</mi> <mi mathvariant="bold-italic">a</mi> </msub> </mrow> </semantics></math>; (<b>c</b>) <math display="inline"><semantics> <mrow> <msub> <mi mathvariant="bold-italic">K</mi> <mi mathvariant="bold-italic">o</mi> </msub> </mrow> </semantics></math>; (<b>d</b>) <math display="inline"><semantics> <mrow> <msub> <mi mathvariant="bold-italic">C</mi> <mi mathvariant="bold-italic">o</mi> </msub> </mrow> </semantics></math>; (<b>e</b>)<math display="inline"><semantics> <mrow> <mo> </mo> <msub> <mi mathvariant="bold-italic">M</mi> <mi mathvariant="bold-italic">t</mi> </msub> </mrow> </semantics></math>; (<b>f</b>) <math display="inline"><semantics> <mrow> <msub> <mi mathvariant="bold-italic">η</mi> <mi mathvariant="bold-italic">t</mi> </msub> </mrow> </semantics></math>; (<b>g</b>) <math display="inline"><semantics> <mrow> <msub> <mi mathvariant="bold-italic">M</mi> <mi mathvariant="bold-italic">f</mi> </msub> </mrow> </semantics></math>; (<b>h</b>) <math display="inline"><semantics> <mrow> <msub> <mi mathvariant="bold-italic">η</mi> <mi mathvariant="bold-italic">f</mi> </msub> </mrow> </semantics></math>.</p> Full article ">Figure 16 Cont.
<p>Effects of different dynamic parameters: (<b>a</b>) <math display="inline"><semantics> <mrow> <msub> <mi mathvariant="bold-italic">M</mi> <mi mathvariant="bold-italic">a</mi> </msub> <mo>;</mo> <mo> </mo> </mrow> </semantics></math>(<b>b</b>) <math display="inline"><semantics> <mrow> <msub> <mi mathvariant="bold-italic">η</mi> <mi mathvariant="bold-italic">a</mi> </msub> </mrow> </semantics></math>; (<b>c</b>) <math display="inline"><semantics> <mrow> <msub> <mi mathvariant="bold-italic">K</mi> <mi mathvariant="bold-italic">o</mi> </msub> </mrow> </semantics></math>; (<b>d</b>) <math display="inline"><semantics> <mrow> <msub> <mi mathvariant="bold-italic">C</mi> <mi mathvariant="bold-italic">o</mi> </msub> </mrow> </semantics></math>; (<b>e</b>)<math display="inline"><semantics> <mrow> <mo> </mo> <msub> <mi mathvariant="bold-italic">M</mi> <mi mathvariant="bold-italic">t</mi> </msub> </mrow> </semantics></math>; (<b>f</b>) <math display="inline"><semantics> <mrow> <msub> <mi mathvariant="bold-italic">η</mi> <mi mathvariant="bold-italic">t</mi> </msub> </mrow> </semantics></math>; (<b>g</b>) <math display="inline"><semantics> <mrow> <msub> <mi mathvariant="bold-italic">M</mi> <mi mathvariant="bold-italic">f</mi> </msub> </mrow> </semantics></math>; (<b>h</b>) <math display="inline"><semantics> <mrow> <msub> <mi mathvariant="bold-italic">η</mi> <mi mathvariant="bold-italic">f</mi> </msub> </mrow> </semantics></math>.</p> Full article ">Figure 17
<p>Comparison of experimental AFRF of thrust bearing with and without friction damper.</p> Full article ">Figure 18
<p>Comparisons of the AFRF of thrust bearing with and without friction damper at different rotational speeds: (<b>a</b>) rotational speed at 60 rpm; (<b>b</b>) rotational speed at 160 rpm.</p> Full article ">Figure 18 Cont.
<p>Comparisons of the AFRF of thrust bearing with and without friction damper at different rotational speeds: (<b>a</b>) rotational speed at 60 rpm; (<b>b</b>) rotational speed at 160 rpm.</p> Full article ">Figure 19
<p>Comparisons of AFRF of thrust bearing under different static thrusts: (<b>a</b>) static thrust is10 kN; (<b>b</b>) static thrust is 30 kN; (<b>c</b>) static thrust is 50 kN.</p> Full article ">Figure 19 Cont.
<p>Comparisons of AFRF of thrust bearing under different static thrusts: (<b>a</b>) static thrust is10 kN; (<b>b</b>) static thrust is 30 kN; (<b>c</b>) static thrust is 50 kN.</p> Full article ">
18 pages, 14851 KiB  
Article
A Simplified Panel Method (sPM) for Hydrodynamics of Air Cushion Assisted Platforms
by Fengmei Jing, Song Wang, Zhiqun Guo and Yurui Ni
J. Mar. Sci. Eng. 2022, 10(10), 1554; https://doi.org/10.3390/jmse10101554 - 20 Oct 2022
Cited by 1 | Viewed by 2023
Abstract
Air-cushion-assisted platforms (ACAPs) are floating platforms supported by both buoyancy pontoon and air cushion, which have merits of wave bending moment reduction, better stability, and hydrodynamic performance. However, there is barely a concise method that can quickly predict the motion response of ACAPs. [...] Read more.
Air-cushion-assisted platforms (ACAPs) are floating platforms supported by both buoyancy pontoon and air cushion, which have merits of wave bending moment reduction, better stability, and hydrodynamic performance. However, there is barely a concise method that can quickly predict the motion response of ACAPs. In this paper, a simplified panel method (sPM) was presented for evaluating the hydrodynamics of ACAPs. The sPM extends the conventional boundary integral equation (BIE) to include the radiation solutions of pulsating air pressure but ignores some unimportant air-water cross terms in motion equations whose coefficients cannot be directly derived from conventional Green’s function methods. The effectiveness of the sPM was validated by experimental data from an ACAP model with one air chamber and analytical results from an oscillating water column (OWC). The numerical results demonstrate that the sPM can give desirable predictions for motion responses of the ACAP and inner pressure of the OWC as compared with results from the literature, which suggests the sPM could be approximately applied to evaluation of hydrodynamic performance of ACAPs and OWCs. Full article
(This article belongs to the Topic Wind, Wave and Tidal Energy Technologies in China)
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<p>Schematic diagram of a single-cabin rectangular ACAP.</p> Full article ">Figure 2
<p>Dimensional parameters of the ACAP [<a href="#B23-jmse-10-01554" class="html-bibr">23</a>]. (<b>a</b>) 3D model, (<b>b</b>) Longitudinal section.</p> Full article ">Figure 3
<p>3D model of the ACAP [<a href="#B23-jmse-10-01554" class="html-bibr">23</a>].</p> Full article ">Figure 4
<p>Panels for the ACAP.</p> Full article ">Figure 5
<p>Heave RAO obtained by the sPM as compared with WAMIT results [<a href="#B23-jmse-10-01554" class="html-bibr">23</a>] and experimental data [<a href="#B21-jmse-10-01554" class="html-bibr">21</a>].</p> Full article ">Figure 6
<p>Pitch RAO obtained by the sPM as compared with WAMIT results [<a href="#B23-jmse-10-01554" class="html-bibr">23</a>] and experimental data [<a href="#B21-jmse-10-01554" class="html-bibr">21</a>].</p> Full article ">Figure 7
<p>A restrained vertical symmetric OWC with finite wall thickness [<a href="#B26-jmse-10-01554" class="html-bibr">26</a>].</p> Full article ">Figure 8
<p>Mesh generation for the OWC model.</p> Full article ">Figure 9
<p>The wave amplitude distribution around the OWC for different <math display="inline"><semantics> <mrow> <mi>k</mi> <mi>a</mi> </mrow> </semantics></math>. (<b>a</b>) <math display="inline"><semantics> <mrow> <mi>k</mi> <mi>a</mi> <mo>=</mo> <mn>0.4</mn> </mrow> </semantics></math>, (<b>b</b>) <math display="inline"><semantics> <mrow> <mi>k</mi> <mi>a</mi> <mo>=</mo> <mn>1.0</mn> </mrow> </semantics></math>, (<b>c</b>) <math display="inline"><semantics> <mrow> <mi>k</mi> <mi>a</mi> <mo>=</mo> <mn>1.5</mn> </mrow> </semantics></math>, (<b>d</b>) <math display="inline"><semantics> <mrow> <mi>k</mi> <mi>a</mi> <mo>=</mo> <mn>2.0</mn> </mrow> </semantics></math>.</p> Full article ">Figure 9 Cont.
<p>The wave amplitude distribution around the OWC for different <math display="inline"><semantics> <mrow> <mi>k</mi> <mi>a</mi> </mrow> </semantics></math>. (<b>a</b>) <math display="inline"><semantics> <mrow> <mi>k</mi> <mi>a</mi> <mo>=</mo> <mn>0.4</mn> </mrow> </semantics></math>, (<b>b</b>) <math display="inline"><semantics> <mrow> <mi>k</mi> <mi>a</mi> <mo>=</mo> <mn>1.0</mn> </mrow> </semantics></math>, (<b>c</b>) <math display="inline"><semantics> <mrow> <mi>k</mi> <mi>a</mi> <mo>=</mo> <mn>1.5</mn> </mrow> </semantics></math>, (<b>d</b>) <math display="inline"><semantics> <mrow> <mi>k</mi> <mi>a</mi> <mo>=</mo> <mn>2.0</mn> </mrow> </semantics></math>.</p> Full article ">Figure 10
<p>Inner air pressure of the OWC obtained by the sPM as compared with analytical results [<a href="#B26-jmse-10-01554" class="html-bibr">26</a>]. (<b>a</b>) <math display="inline"><semantics> <mrow> <msub> <mi>g</mi> <mi>T</mi> </msub> <mo>=</mo> <mn>6</mn> </mrow> </semantics></math>, (<b>b</b>) <math display="inline"><semantics> <mrow> <msub> <mi>g</mi> <mi>T</mi> </msub> <mo>=</mo> <mn>10</mn> </mrow> </semantics></math>.</p> Full article ">
15 pages, 10694 KiB  
Article
Arrhenius Equation-Based Model to Predict Lithium-Ions Batteries’ Performance
by Liteng Zeng, Yuli Hu, Chengyi Lu, Guang Pan and Mengjie Li
J. Mar. Sci. Eng. 2022, 10(10), 1553; https://doi.org/10.3390/jmse10101553 - 20 Oct 2022
Cited by 4 | Viewed by 3897
Abstract
The accuracy of Peukert’s battery capacity equation may decrease under the conditions of variable current and variable temperatures. Some researchers have previously tried to overcome the lack of C-rate change. However, the dependence of battery capacity on temperature is still not included. In [...] Read more.
The accuracy of Peukert’s battery capacity equation may decrease under the conditions of variable current and variable temperatures. Some researchers have previously tried to overcome the lack of C-rate change. However, the dependence of battery capacity on temperature is still not included. In this paper, we mainly studied the capacity reduction effect of batteries under variable temperatures. The proposed method can calculate the battery’s available capacity according to the specific discharge conditions. The experimental method proposed in this paper provides a reasonable test method to generate the required coefficients in order to establish a state of charge prediction model with high accuracy. After establishing the method, we can make a real-time prediction of the available energy of battery including the remaining energy of battery. From the result, we can see that the result is of great precision and the method is valuable. Full article
(This article belongs to the Special Issue Energy Efficiency in Marine Vehicles)
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<p>The model of circuit.</p> Full article ">Figure 2
<p>The flow chart of the proposed method to predict the output energy.</p> Full article ">Figure 3
<p>(<b>a</b>) The experimental subject LF105 battery. (<b>b</b>) The batteries in experiment. (<b>c</b>) The thermostat used in the experiment.</p> Full article ">Figure 4
<p>Schematic diagram of discharge experiment flow.</p> Full article ">Figure 5
<p>Variation of capacity and energy of battery with ambient temperature and charging rate.</p> Full article ">Figure 6
<p>The capacity efficiency of the battery varies with the average temperature of the battery shell.</p> Full article ">Figure 7
<p>The equivalent electric discharged during the discharging process in various cases.</p> Full article ">Figure 8
<p>The temperature of the battery changes with time under the rate of (<b>a</b>) 1/3C, (<b>b</b>) 1/2C, (<b>c</b>)1C, (<b>d</b>) 2C.</p> Full article ">Figure 9
<p>The energy efficiency of the battery varies with the average temperature of the battery shell.</p> Full article ">Figure 10
<p>The output voltage of the battery varies with the ambient temperature and C-rate.</p> Full article ">Figure 11
<p>(<b>a</b>) Practical curve of battery OCV with battery SOC at different temperature. (<b>b</b>) Theoretical curve of battery OCV with battery SOC at different temperature.</p> Full article ">Figure 12
<p>Prediction of output energy.</p> Full article ">
20 pages, 1737 KiB  
Article
Designing Subsidy Scheme for Marine Disaster Index Insurance in China
by Yuemei Xue, Lili Ding and Kee-hung Lai
J. Mar. Sci. Eng. 2022, 10(10), 1552; https://doi.org/10.3390/jmse10101552 - 20 Oct 2022
Cited by 1 | Viewed by 2030
Abstract
Designing an optimal subsidy scheme for marine disaster index insurance (MDII) for households in coastal areas of China remains a managerial challenge. The issue of subsidies for disaster insurance has received extensive research attention, but extant studies are confined to the issue of [...] Read more.
Designing an optimal subsidy scheme for marine disaster index insurance (MDII) for households in coastal areas of China remains a managerial challenge. The issue of subsidies for disaster insurance has received extensive research attention, but extant studies are confined to the issue of whether to subsidize, lacking focus on how and how much to subsidize. In the existing marine disaster index insurance pilots in China, there are varying levels and scales of subsidies in spite of premium subsidies. To design an optimal subsidy scheme for marine disaster index insurance in China, this paper proposes an optimal insurance model of marine disaster index insurance with government subsidy. Excluding the behaviors of the policyholders and insurance firms, the model captures the behaviors of the subsidy scheme from the government. Furthermore, employing the storm surge disasters, the optimal trigger scheme and subsidy scheme are designed and estimated. The results recommend that the optimal subsidy ratio for MDII in China needs to be at least 92.54%. Moreover, this value increases when there are more potential victims of marine disasters who choose to insure MDII, while the total subsidy decreases. Evidently, the subsidies for pilots of MDII in China are inadequate to meet the conditions for operation currently, which explains the dilemma of the MDII in China’s pilots. These findings provide theoretical evidence for the optimization of the MDII in China. Full article
(This article belongs to the Special Issue Risk Analysis of Maritime Accidents)
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<p>The absolute risk aversion coefficient of the policyholder and the risk premium.</p> Full article ">Figure 2
<p>Scatter and non-parametric regression of direct economic loss with respect to wind forces.</p> Full article ">Figure 3
<p>Probability density plot and its GAMMA distribution probability density fitting curve.</p> Full article ">Figure 4
<p>The optimal trigger scheme.</p> Full article ">
16 pages, 4223 KiB  
Article
Hydrodynamic Performance of a Floating Offshore Oscillating Water Column Wave Energy Converter
by Mohammad Rashed Mia, Ming Zhao, Helen Wu, Vatsal Dhamelia and Pan Hu
J. Mar. Sci. Eng. 2022, 10(10), 1551; https://doi.org/10.3390/jmse10101551 - 20 Oct 2022
Cited by 4 | Viewed by 3311
Abstract
A floating oscillating water column (OWC) wave energy converter (WEC) supported by mooring lines can be modelled as an elastically supported OWC. The main objective of this paper is to investigate the effects of the frequency ratio on the performance of floating OWC [...] Read more.
A floating oscillating water column (OWC) wave energy converter (WEC) supported by mooring lines can be modelled as an elastically supported OWC. The main objective of this paper is to investigate the effects of the frequency ratio on the performance of floating OWC (oscillating water column) devices that oscillate either vertically or horizontally at two different mass ratios (m = 2 and 3) through two-dimensional computational fluid dynamics simulations. The frequency ratio is the ratio of the natural frequency of the system to the wave frequency. Simulations are conducted for nine frequency ratios in the range between 1 and 10. The hydrodynamic efficiency achieves its maximum at the smallest frequency ratio of 1 if the OWC oscillates horizontally and at the largest frequency ratio of 10 if the OWC oscillates vertically. The frequency ratio affects the hydraulic efficiency of the vertical oscillating OWC significantly stronger than that of the horizontal oscillating OWC, especially when it is small. The air pressure and the volume oscillation in OWC is not affected much by the horizontal motion of the OWC but is significantly affected by the vertical motion, especially at small frequency ratios. Full article
(This article belongs to the Special Issue CFD Analysis in Ocean Engineering)
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<p>Schematic diagram of a two-dimensional wave tank developed to simulate the hydrodynamic performance of (<b>a</b>) sway-only floating (<b>b</b>) heave-only floating OWC in a wave flume; (<b>c</b>) computational mesh near the OWC.</p> Full article ">Figure 1 Cont.
<p>Schematic diagram of a two-dimensional wave tank developed to simulate the hydrodynamic performance of (<b>a</b>) sway-only floating (<b>b</b>) heave-only floating OWC in a wave flume; (<b>c</b>) computational mesh near the OWC.</p> Full article ">Figure 2
<p>Effects of the turbine coefficient for fixed OWC device at (<span class="html-italic">d</span>/<span class="html-italic">h</span> = 0.25) on efficiency with <span class="html-italic">B</span>/<span class="html-italic">L</span> for different turbine coefficient.</p> Full article ">Figure 3
<p>Variation of the floating OWC device efficiency <span class="html-italic">ε</span> with <math display="inline"><semantics> <mrow> <mi>B</mi> <mo>/</mo> <mi>L</mi> </mrow> </semantics></math> under nine different frequency ratios (<math display="inline"><semantics> <mrow> <msub> <mi>R</mi> <mi>f</mi> </msub> </mrow> </semantics></math> ) at <span class="html-italic">d</span>/<span class="html-italic">h</span>= 0.25, <span class="html-italic">K</span><sub>t</sub> = 3000 Pa·m<sup>−3</sup>·s.</p> Full article ">Figure 4
<p>Variation of the amplitude of air pressures oscillatory of the floating OWC chamber with <math display="inline"><semantics> <mrow> <mi>B</mi> <mo>/</mo> <mi>L</mi> </mrow> </semantics></math> for <span class="html-italic">K</span><sub>t</sub> = 3000 Pa·m<sup>−3</sup>·s and various frequency ratios at <span class="html-italic">d</span>/<span class="html-italic">h</span> = 0.25.</p> Full article ">Figure 5
<p>Variation of the amplitude of oscillatory air volume of the floating OWC chamber with <math display="inline"><semantics> <mrow> <mi>B</mi> <mo>/</mo> <mi>L</mi> </mrow> </semantics></math> for <span class="html-italic">K</span><sub>t</sub> = 3000 Pa·m<sup>−3</sup>·s and various frequency ratios at <span class="html-italic">d</span>/<span class="html-italic">h</span> = 0.25.</p> Full article ">Figure 6
<p>Variation of the amplitude of the oscillatory horizontal and vertical motion of the floating OWC chamber for <span class="html-italic">K</span><sub>t</sub> = 3000 Pa·m<sup>−3</sup>·s and <span class="html-italic">d/h</span> = 0.25.</p> Full article ">Figure 7
<p>Variation of the amplitude of the waver surface elevation of the floating OWC chamber <math display="inline"><semantics> <mrow> <mi>B</mi> <mo>/</mo> <mi>L</mi> </mrow> </semantics></math> for <span class="html-italic">K</span><sub>t</sub> = 3000 Pa·m<sup>−3</sup>·s and <span class="html-italic">d</span>/<span class="html-italic">h</span> = 0.25.</p> Full article ">Figure 8
<p>The phase between the floating OWC displacement and surface elevation at the centre of the OWC for <span class="html-italic">K</span><sub>t</sub> = 3000 Pa·m<sup>−3</sup>·s and vertical motion at <span class="html-italic">m</span> = 2.</p> Full article ">Figure 9
<p>Flow near the OWC represented by streamlines and vorticity contours at horizontal motion, <span class="html-italic">m</span> = 2, <span class="html-italic">B/L</span> = 0.159.</p> Full article ">Figure 10
<p>Flow near the OWC represented by streamlines and vorticity contours at vertical motion, <span class="html-italic">m</span> = 2, <span class="html-italic">B/L</span> = 0.159.</p> Full article ">
11 pages, 9414 KiB  
Article
Some Peculiarities of Low-Frequency Hydroacoustic Signals Behavior in Tomographic Studies of “Sea-Land-Sea” System
by Sergey Budrin, Grigory Dolgikh, Vladimir Chupin and Stanislav Dolgikh
J. Mar. Sci. Eng. 2022, 10(10), 1550; https://doi.org/10.3390/jmse10101550 - 20 Oct 2022
Cited by 2 | Viewed by 1586
Abstract
In this paper, we analyzed the results of experimental data processing in the study of regularities of propagation and transformation of low-frequency harmonic signals at the boundary of the “sea−land−sea” system. Harmonic signals at a carrier frequency of 33 Hz were generated by [...] Read more.
In this paper, we analyzed the results of experimental data processing in the study of regularities of propagation and transformation of low-frequency harmonic signals at the boundary of the “sea−land−sea” system. Harmonic signals at a carrier frequency of 33 Hz were generated by a low-frequency hydroacoustic radiator in Vityaz Bay. Then, they passed along the shelf of decreasing depth, transformed into seismoacoustic signals of the upper layer of the Earth’s crust and the bedrocks of Shultz Cape and excited hydroacoustic signals at the corresponding frequency in the shelf waters in the open part of the Sea of Japan. When processing the experiment results, we obtained the vertical distributions of the pressure field, caused by an acoustic low-frequency signal passing through the upper layer of the Earth’s crust. We presented the distributions of hydroacoustic and seismoacoustic energies. The obtained experimental data were compared with the simulations by the model, developed strictly according to the experiment scheme and the geological structure of the area. In the discussion of the obtained results, we explained a probable mechanism of acoustic energy propagation and the nature of the vertical distributions of the pressure field formation. Full article
(This article belongs to the Special Issue Sound Scattering in the Ocean)
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<p>Scheme map of the experiment. The “radiator” was the transmitting point; P1−P5 were measurement points.</p> Full article ">Figure 2
<p>Simulation parameters.</p> Full article ">Figure 3
<p>Vertical distributions of the pressure field, measured and calculated by the model at points: (<b>a</b>) P1; (<b>b</b>) P2; (<b>c</b>) P3; (<b>d</b>) P4.</p> Full article ">Figure 4
<p>(<b>a</b>) Comparison of the energy densities of oscillations recorded by the laser strainmeter and the energy densities of the experimentally measured hydroacoustic signals; (<b>b</b>) comparison of energy density values of elastic oscillations: energy density of the experimentally measured hydroacoustic signals and energy densities calculated according to the model.</p> Full article ">Figure 5
<p>Changes in hydroacoustic and seismoacoustic energy densities from point to point.</p> Full article ">Figure 6
<p>Vertical distributions of the sound velocity (<b>a</b>) and the temperature (<b>b</b>) at measurement points P2 and P3.</p> Full article ">
18 pages, 9489 KiB  
Article
Land Subsidence Evolution and Simulation in the Western Coastal Area of Bohai Bay, China
by Can Lu, Lin Zhu, Xiaojuan Li, Huili Gong, Dong Du, Haigang Wang and Pietro Teatini
J. Mar. Sci. Eng. 2022, 10(10), 1549; https://doi.org/10.3390/jmse10101549 - 20 Oct 2022
Cited by 7 | Viewed by 2412
Abstract
Groundwater overexploitation and loading of buildings have been the main factors triggering land subsidence along the west coast of Bohai Bay, China, since the 2000s. Uneven subsidence has been causing damage to buildings and civil facilities, loss of elevation, increasing the risk of [...] Read more.
Groundwater overexploitation and loading of buildings have been the main factors triggering land subsidence along the west coast of Bohai Bay, China, since the 2000s. Uneven subsidence has been causing damage to buildings and civil facilities, loss of elevation, increasing the risk of flood and seawater intrusion, and threatening the safety of people’s lives and property. This paper analyzed the spatial and temporal features of land subsidence along the coastal area from 2003 to 2010 and from 2015 to 2020, respectively. The relations between the initiating factors and land subsidence were explored. Then, the simulation model of land subsidence was constructed through a deep learning method. During the process, multiple data were collected, including land satellite (Landsat), environmental satellite advanced synthetic aperture radar (ENVISAT ASAR) and Sentinel-1 images, leveling data, lithological data, and groundwater level data. The area occupied by buildings and vertical displacement were extracted by using supervised classification, small baseline subset (SBAS), and persistent scatterer interferometry (PSI) technologies. The gated recurrent unit (GRU) neural network was adopted to simulate the evolution of land subsidence. Results showed that the maximum annual vertical displacement rate decreased from −94 mm/yr during 2003–2010 to −87 mm/yr during 2015–2020. The correlation efficiency between the groundwater level of the third confined aquifer group and land subsidence was larger than the area occupied by buildings and the compressible layer thickness with subsidence. The constructed GRU neural network model can simulate subsidence from September 2019 to December 2019, with the overall RMSE and MAE being 3.16 mm and 2.19 mm, respectively. This work can facilitate an understanding of the evolution and prevention of land subsidence along the west coast of Bohai Bay, which will provide information for policy decisions and flood-fighting plans of the worldwide coastal cities. Full article
(This article belongs to the Special Issue Remote Sensing and Geographic Information System (GIS) for Coastal Environment)
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<p>Distribution of the study area. The yellow box indicates the boundary of the ENVISAT ASAR images (track 175, frame 2817), and the green box indicates the boundary of the Sentinel-1 images (path 149, frame 463) used to map land subsidence. The positions of the leveling benchmarks and wells monitoring station are provided. The black dashed line shows the hydrogeological cross-section. The background is a Sentinel-2 image acquired on 16 September 2020.</p> Full article ">Figure 2
<p>Hydrogeological cross-section in the study area. The black dashed line represents the aquifer group boundary (modified from [<a href="#B29-jmse-10-01549" class="html-bibr">29</a>]).</p> Full article ">Figure 3
<p>The flowchart of the processing.</p> Full article ">Figure 4
<p>Illustration of gated recurrent units (modified from [<a href="#B22-jmse-10-01549" class="html-bibr">22</a>]).</p> Full article ">Figure 5
<p>Comparison of subsidence from the leveling benchmarks data and InSAR results.</p> Full article ">Figure 6
<p>Average vertical displacement rates obtained by the InSAR techniques in the Binhai New Area: (<b>a</b>) SBAS from 2003 to 2010 and (<b>b</b>) PSI from 2015 to 2020. Negative values indicate subsidence, and positive values indicate uplift.</p> Full article ">Figure 7
<p>Distribution of buildings in the study area from 2003 to 2010 and 2015 to 2020.</p> Full article ">Figure 8
<p>Area occupied by buildings in the Binhai New Area extracted with the GEE from 2003 to 2010 and 2015 to 2020.</p> Full article ">Figure 9
<p>The contour lines of groundwater level depth of the second (<b>a</b>) and third (<b>b</b>) confined aquifer group in 2013, 2016, and 2019.</p> Full article ">Figure 10
<p>Cumulative land subsidence and groundwater level at typical groundwater monitoring wells: (<b>a</b>) well #1, (<b>b</b>) well #2 and (<b>c</b>) well #3.</p> Full article ">Figure 11
<p>Cumulative land subsidence and area occupied by buildings in the 200 m buffer zone around each well: (<b>a</b>) well #1, (<b>b</b>) well #2 and (<b>c</b>) well #3.</p> Full article ">Figure 12
<p>Land subsidence rate and compressed layer thickness along the H-H′ profile at the two time periods.</p> Full article ">Figure 13
<p>Schematic description of Well #1 and Well #3 lithologies.</p> Full article ">Figure 14
<p>InSAR and simulated values for each monitoring well: (<b>a</b>) well #1, (<b>b</b>) well #2 and (<b>c</b>) well #3.</p> Full article ">
24 pages, 7301 KiB  
Article
Predicting Acoustic Transmission Loss Uncertainty in Ocean Environments with Neural Networks
by Brandon M. Lee, Jay R. Johnson and David R. Dowling
J. Mar. Sci. Eng. 2022, 10(10), 1548; https://doi.org/10.3390/jmse10101548 - 20 Oct 2022
Cited by 5 | Viewed by 2428
Abstract
Computational predictions of acoustic transmission loss (TL) in ocean environments depend on the relevant environmental characteristics, such as the sound speed field, bathymetry, and seabed properties. When databases are used to obtain estimates of these properties, the resulting predictions of TL are uncertain, [...] Read more.
Computational predictions of acoustic transmission loss (TL) in ocean environments depend on the relevant environmental characteristics, such as the sound speed field, bathymetry, and seabed properties. When databases are used to obtain estimates of these properties, the resulting predictions of TL are uncertain, and this uncertainty can be quantified via the probability density function (PDF) of TL. A machine learning technique for quickly estimating the PDF of TL using only a single, baseline TL calculation is presented here. The technique shifts the computational burden from present-time Monte-Carlo (MC) TL simulations in the environment of interest to ahead-of-time training of a neural network using equivalent MC TL simulations in hundreds of ocean environments. An environmental uncertainty approach which draws information from global databases is also described and is used to create hundreds of thousands of TL-field examples across 300 unique ocean environments at ranges up to 100 km for source frequencies between 50 and 600 Hz. A subset of the total dataset is used to train and compare neural networks with various architectures and TL-PDF-generation methods. Finally, the remaining dataset examples are used to compare the machine-learning technique’s accuracy and computational effort to that of prior TL-uncertainty-estimation techniques. Full article
(This article belongs to the Special Issue Application of Sensing and Machine Learning to Underwater Acoustic)
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<p>Elements of the proposed machine learning technique for estimating the probability density function (PDF) of transmission loss (TL). A simple, feedforward neural network (NN) diagram is shown between the model’s inputs on the left and outputs on the right. The inputs are values from a single baseline TL solution surrounding the point of interest (POI), and the output is a predicted estimate of the Monte Carlo (MC) PDF of TL at that POI resulting from environmental uncertainty.</p> Full article ">Figure 2
<p>Sample TL-field. In this environment, the acoustic source is located at a range of 0 km and a depth of 72.3 m. The acoustic source frequency is 528.5 Hz. The sound speed profile corresponding to the water column properties at the source location is shown in the left panel. The environment’s bathymetry is shown as the dashed red line. The sediment type for this environment is clay, and the sediment thickness for this environment is 350.2 m. The six red points shown in the environment near 1000 m in depth are the example locations for the PDFs of TL shown in <a href="#jmse-10-01548-f003" class="html-fig">Figure 3</a>.</p> Full article ">Figure 3
<p>MC PDFs of TL for example receiver locations indicated in <a href="#jmse-10-01548-f002" class="html-fig">Figure 2</a>. The example receiver locations corresponding to the MC PDFs of TL labeled (<b>a</b>–<b>f</b>) are located at ranges of 8.0, 23.3, 38.6, 53.8, 69.0, and 84.3 km and at depths near 1 km.</p> Full article ">Figure 4
<p>Summary of cases used to create the training and testing datasets. (<b>a</b>) The distributions of mean baseline environment depth for all 10,000 and the 300 selected cases. (<b>b</b>) The distributions of environment sediment type for all 10,000 and the 300 selected cases. The sediment types are—1: cobble or gravel or pebble, 2: muddy sandy gravel, 3: medium sand or sand, 4: fine sand or silty sand, 5: muddy sand, 6: gravelly mud or sandy silt, 7: medium silt or sand-silt-clay, 8: sandy mud or silt, 9: fine silt or clayey silt, 10: sandy clay, 11: silty clay, and 12: clay. Without careful selection of the final 300 cases, around 90% of the cases would have the clay sediment type. (<b>c</b>) Source locations for all 10,000 and the 300 selected cases.</p> Full article ">Figure 5
<p>Algorithm block diagram for a NN prediction of a PDF of TL. When presented with a new example, the NN first gathers its inputs (as described in <a href="#sec2dot2dot1-jmse-10-01548" class="html-sec">Section 2.2.1</a>). Those inputs are passed to the input layer of the neural network. The NN shown in this figure is a fully connected feedforward NN. The final values of the NN computation appear in the output layer. Depending on the choice of output type (as discussed in <a href="#sec2dot2dot2-jmse-10-01548" class="html-sec">Section 2.2.2</a>), a final computation is performed to produce the predicted PDF of TL. The generic architecture (number of hidden layers, nodes, etc.) of the NN pictured here is not representative of the NNs trained in this study (as detailed in <a href="#sec2dot2dot3-jmse-10-01548" class="html-sec">Section 2.2.3</a>).</p> Full article ">Figure 6
<p>Example MC PDF of TL (solid black line) plotted with its best fit three-parameter log normal (LN3) PDF (solid blue line) and as a histogram (dashed red line). The <math display="inline"><semantics> <mrow> <msub> <mi>L</mi> <mn>1</mn> </msub> </mrow> </semantics></math> error between the MC PDF of TL and its best fit LN3 PDF is visualized as the shaded region and is 0.105 in this example.</p> Full article ">Figure 7
<p>Final histogram NN performance for a sample test case. (<b>a</b>) The <math display="inline"><semantics> <mrow> <msub> <mi>L</mi> <mn>1</mn> </msub> </mrow> </semantics></math> errors of the final histogram NN’s predictions on the examples in case 101 (a testing case) at each example’s receiver location. Results from the three labeled receiver locations are shown in (<b>b</b>). The NN’s predicted PDF of TL (dashed red line) is compared to the MC PDF of TL (solid black line) at three receiver locations and the <math display="inline"><semantics> <mrow> <msub> <mi>L</mi> <mn>1</mn> </msub> </mrow> </semantics></math> error is visualized as the shaded area for each. The <math display="inline"><semantics> <mrow> <msub> <mi>L</mi> <mn>1</mn> </msub> </mrow> </semantics></math> errors for examples A, B, and C are 1.246, 0.507, and 0.117 respectively.</p> Full article ">Figure 8
<p>Distributions of testing errors on 1,844,995 examples for the final histogram NN (solid black line) and the LN3 NN (dotted red line) on testing dataset 1. The two distributions are very similar, with most of their differences occurring at <math display="inline"><semantics> <mrow> <msub> <mi>L</mi> <mn>1</mn> </msub> </mrow> </semantics></math> errors less than 0.5 (vertical dashed line).</p> Full article ">Figure 9
<p>Distributions of testing errors for the final histogram NN (solid black line), the LN3 NN (dotted red line), and AS (thin gray line) on testing dataset 2. A greater proportion of the Area Statistics (AS) method <math display="inline"><semantics> <mrow> <msub> <mi>L</mi> <mn>1</mn> </msub> </mrow> </semantics></math> errors are greater than 0.5 (vertical dashed line) than the NN methods.</p> Full article ">Figure 10
<p>Cumulative distributions of the <math display="inline"><semantics> <mrow> <msub> <mi>L</mi> <mn>1</mn> </msub> </mrow> </semantics></math> errors for the final histogram NN (thick solid black line) and LN3 NN (thick dashed red line) on testing dataset 2 in comparison to the cumulative distribution of the <math display="inline"><semantics> <mrow> <msub> <mi>L</mi> <mn>1</mn> </msub> </mrow> </semantics></math> errors for AS (thick dash-dotted gray line) and the cumulative distributions of <math display="inline"><semantics> <mrow> <msub> <mi>L</mi> <mn>1</mn> </msub> </mrow> </semantics></math> differences for the alternative MC PDFs of TL generated with 2000 trials (thin solid orange line), 500 trials (thin dashed blue line), 200 trials (thin dash-dotted green line), and 100 trials (thin solid purple line).</p> Full article ">Figure 11
<p>Comparison of the mean testing errors and TL PDF prediction times for each method. The mean prediction time is the average of the 100 testing case prediction times—the time it took to predict the PDF of TL for every example in that case after the baseline TL computation was available.</p> Full article ">Figure A1
<p>Maximum-likelihood estimated model of bathymetry uncertainty. (<b>a</b>) The model for the standard deviation of the error of the bathymetry database’s predicted value at a grid point given the predicted value (solid line), and the model for the standard deviation of the error of a measured value of water column depth given the measured value (dashed red line). (<b>b</b>) The model for the correlation between the errors in predicted values at two grid points given the distance between the two grid points. The inferred bathymetry correlation length is 16.7 km.</p> Full article ">Figure A2
<p>Neural network improvement of cross-validation (CV) performance with tuning effort. The best current CV-scores at any time during the tuning for the histogram output type (solid black line) and the LN3 output type (dotted red line). The first configuration for the LN3 output type NN provided a CV-score of 0.83; this point is omitted from the plot for clarity.</p> Full article ">
10 pages, 2360 KiB  
Article
Genetic Diversity Analysis of Different Populations of Lutjanus kasmira Based on SNP Markers
by Fangcao Zhao, Liang Guo, Nan Zhang, Jingwen Yang, Kecheng Zhu, Huayang Guo, Baosuo Liu, Bo Liu, Dianchang Zhang and Shigui Jiang
J. Mar. Sci. Eng. 2022, 10(10), 1547; https://doi.org/10.3390/jmse10101547 - 20 Oct 2022
Cited by 1 | Viewed by 2005
Abstract
Lutjanus kasmira belongs to the family Lutjanidae. Over the past 20 years, the L. kasmira population in the South China Sea has been shrinking due to climate change, pressure from human activities, and inadequate food supplies. In this study, single nucleotide polymorphism (SNP) [...] Read more.
Lutjanus kasmira belongs to the family Lutjanidae. Over the past 20 years, the L. kasmira population in the South China Sea has been shrinking due to climate change, pressure from human activities, and inadequate food supplies. In this study, single nucleotide polymorphism (SNP) data obtained from restriction site-associated DNA sequencing (RAD-seq) were used to assess the genetic diversity of L. kasmira in Zhubi Dao (ZB) and Meiji Dao (MJ). The genome-wide nucleotide diversity (π) of the ZB population and MJ population was 0.02478 and 0.02154, respectively. The inbreeding coefficient (Fis) of the ZB population and MJ population was −0.18729 and 0.03256, respectively. The genetic differentiation (Fst) between the ZB and MJ subpopulations was 0.00255102. The expected heterozygosity (He) of individuals from ZB and MJ was 0.33585 and 0.22098, respectively. The observed heterozygosity (Ho) of individuals from the ZB population and MJ population was 0.46834 and 0.23103, respectively. Although the ZB and MJ populations did not have significant genetic differences, the genetic differentiation between them was confirmed using population structure, phylogenetic, and principal component analyses. These results indicated that the genetic diversity of the ZB and MJ populations was relatively low at the genome level, and that their genetic differences were small. Full article
(This article belongs to the Special Issue New Techniques in Marine Aquaculture)
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<p>Locations of the 30 collected <span class="html-italic">Lutjanus kasmira</span>.</p> Full article ">Figure 2
<p>The distance matrix heatmap of 30 individuals of <span class="html-italic">L. kasmira</span> created using the R package poppr.</p> Full article ">Figure 3
<p>The minimum spanning network of 30 <span class="html-italic">L. kasmira</span> individuals.</p> Full article ">Figure 4
<p>Phylogenetic tree of 30 <span class="html-italic">L. kasmira</span> individuals based on SNP loci created using the neighbor-joining method.</p> Full article ">Figure 5
<p>The group structure diagrams. (<b>a</b>) The cross-validation error for <span class="html-italic">L. kasmira</span> according to the admixture value K; (<b>b</b>) results of Bayesian cluster analysis of <span class="html-italic">L. kasmira</span> based on SNP loci using ADMIXTURE software for K = 1–10 clusters.</p> Full article ">Figure 6
<p>Population relationships of the collected <span class="html-italic">L. kasmira</span> individuals. (<b>a</b>) Principal component analysis (PCA) plot of 30 <span class="html-italic">L. kasmira</span> individuals based on all SNP loci between the ZB and MJ populations. A and B represent the MJ population and ZB population, respectively; (<b>b</b>) three−dimensional PCA clustering map of 30 <span class="html-italic">L. kasmira</span> individuals based on all SNP loci between the ZB and MJ populations. A and B represent the MJ population and ZB population, respectively.</p> Full article ">
17 pages, 2614 KiB  
Article
Antimicrobial Effect of Carbon Nanodots–ZnO Nanocomposite Synthesized Using Sargassum horneri
by Kyung Woo Kim, Dawoon Chung, Seung-Hyun Jung, Yong Min Kwon, Jawoon Young Hwan Kim and Kyunghwa Baek
J. Mar. Sci. Eng. 2022, 10(10), 1546; https://doi.org/10.3390/jmse10101546 - 20 Oct 2022
Cited by 5 | Viewed by 2021
Abstract
For several years, industrial damages caused by massive blooming and drifting of Sargassum horneri (S. horneri) called “golden tides” seaweeds have been continuously reported in Korea. National efforts have been made to produce useful cases of application by using the troublesome [...] Read more.
For several years, industrial damages caused by massive blooming and drifting of Sargassum horneri (S. horneri) called “golden tides” seaweeds have been continuously reported in Korea. National efforts have been made to produce useful cases of application by using the troublesome S. horneri. As a part of that, a CNDs–ZnO nanocomposite with antibacterial and antifungal properties was synthesized through a simple hydrothermal reaction using S. horneri, and the results were verified in this paper. The antibacterial and antifungal activities were mainly determined by the disk diffusion test against five bacterial and fungal strains, respectively. Of note, the inhibitory effect of the CNDs–ZnO on the growth of both Gram-positive (Bacillus cereus and Staphylococcus aureus) and Gram-negative (Escherichia coli, Salmonella typhimurium, and Vibrio alginolyticus) bacteria was highly effective. Moreover, the nanocomposite showed low toxicity compared to chlorine bleach. In addition, the CNDs–ZnO showed antifungal activities against both yeast (Saccharomyces cerevisiae and Rhodotorula mucilaginosa) and mold (Aspergillus flavus, Aspergillus niger, and Aspergillus terreus). This work showed the potential usability in antimicrobial application based on poor marine brown alga considered as useless in Korea. Through this paper, it seems that sufficient utility and possibility can be expected upon various unappreciated and uninterested marine species. Full article
(This article belongs to the Special Issue New Insights in the Study of Harmful Algal Bloom)
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<p>(<b>a</b>) Photo image of brown-colored CNDs–ZnO aqueous solution and fluorescent photo-images of both CNDs alone and CNDs–ZnO at specific excitation wavelengths; (<b>b</b>) UV–vis absorption spectra of CNDs alone and CNDs–ZnO; (<b>c</b>) PL emission spectra of CNDs alone and CNDs–ZnO under excitation wavelength change with 20 nm increments from 250 nm to 550 nm.</p> Full article ">Figure 2
<p>FT-IR spectra comparison of CNDs alone and CNDs–ZnO.</p> Full article ">Figure 3
<p>XPS survey spectra comparison of CNDs alone and CNDs–ZnO and their percentage of atomic concentrations.</p> Full article ">Figure 4
<p>High-resolution XPS spectra of (<b>a</b>,<b>b</b>) C 1<span class="html-italic">s</span>, (<b>c</b>,<b>d</b>) N 1<span class="html-italic">s</span>, (<b>e</b>,<b>f</b>) O 1<span class="html-italic">s</span>, and (<b>g</b>) Zn 2<span class="html-italic">p</span> between CNDs alone and CNDs–ZnO.</p> Full article ">Figure 5
<p>(<b>a</b>) TEM image of CNDs alone and (<b>b</b>) CNDs–ZnO nanocomposites; (<b>c</b>) size distribution of the as-fabricated CNDs–ZnO.</p> Full article ">Figure 6
<p>Fitting curves of bacterial cell growth for (<b>a</b>) <span class="html-italic">E. coli</span> with pure ZnO and CNDs–ZnO; (<b>b</b>) <span class="html-italic">B. cereus</span> with pure ZnO and CNDs–ZnO; (<b>c</b>) <span class="html-italic">S. typhimurium</span> with pure ZnO and CNDs–ZnO; (<b>d</b>) <span class="html-italic">S. aureus</span> with pure ZnO and CNDs–ZnO; (<b>e</b>) <span class="html-italic">V. alginolyticus</span> with pure ZnO and CNDs–ZnO.</p> Full article ">
21 pages, 6527 KiB  
Article
Effects of Tidal Stream Energy Exploitation on Estuarine Circulation and Its Seasonal Variability
by Marcos Sánchez, David Mateo Fouz, Iván López, Rodrigo Carballo and Gregorio Iglesias
J. Mar. Sci. Eng. 2022, 10(10), 1545; https://doi.org/10.3390/jmse10101545 - 20 Oct 2022
Cited by 2 | Viewed by 1960
Abstract
Residual flows are of major importance in coastal areas, driving environmental processes such as sediment transport or nutrient dispersion. Consequently, in those areas where a large tidal stream energy resource is available, prior to the installation of a tidal farm, it is imperative [...] Read more.
Residual flows are of major importance in coastal areas, driving environmental processes such as sediment transport or nutrient dispersion. Consequently, in those areas where a large tidal stream energy resource is available, prior to the installation of a tidal farm, it is imperative to assess how energy extraction affects the residual flows and, in particular, upwelling events. In this paper, the potential effects of different configurations of tidal stream farms on the residual circulation and its seasonality are analysed by means of a case study: Ria de Ortigueira, the westernmost of the Galician Rias Altas (NW Spain). For this purpose, a 3D numerical model was implemented and validated against field measurements. Next, a total of eight case studies, including the operation of bottom-fixed and floating converters under typical summer and winter scenarios, considering upwelling favourable winds, were studied. Overall, when a tidal farm operates, regardless of its configuration and the forcings considered, the resulting general residual flow pattern does not experience significant modifications. This pattern is characterized by a 2D circulation in the inner ria and a positive estuarine circulation in the middle and outer ria. The largest modifications of the residual flow are apparent in the vicinities of the plant, with maximum values of about 0.05 ms−1. Outside this area, the alteration is lower than 0.01 ms−1 and virtually negligible at some distance from the farm where upwelling events develop. Full article
(This article belongs to the Special Issue Coastal Engineering: Sustainability and New Technologies)
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<p>The Ria de Ortigueira (<b>b</b>) and its location in Galicia (NW Spain) (<b>a</b>).</p> Full article ">Figure 2
<p>Numerical model grid.</p> Full article ">Figure 3
<p>Location of the considered tidal stream plant, indicating the locations (S1, S2, and S3) for velocity and salinity profiles (<b>a</b>). Layout in plan view of the plants (<b>b</b>).</p> Full article ">Figure 4
<p>Residual flow pattern in Ria de Ortigueira under case study CS1 conditions. (<b>a</b>) Surface layer; (<b>b</b>) bottom layer.</p> Full article ">Figure 5
<p>Residual flow pattern in Ria de Ortigueira under case study CS2 conditions. (<b>a</b>) Surface layer; (<b>b</b>) bottom layer.</p> Full article ">Figure 6
<p>Salinity vertical profiles at S1, S2, and S3 during CS1 (spring–summer season) (red line) and CS2 (autumn–winter season) (black line).</p> Full article ">Figure 7
<p>Vertical profiles of the magnitude of the residual circulation at S1, S2, and S3 during CS1 (spring–summer season) (red line) and CS2 (autumn–winter season) (black line).</p> Full article ">Figure 8
<p>Residual flow pattern in Ria de Ortigueira under case study CS3 conditions. (<b>a</b>) Surface layer; (<b>b</b>) bottom layer.</p> Full article ">Figure 9
<p>Residual flow pattern in Ria de Ortigueira under case study CS5 conditions. (<b>a</b>) Surface layer; (<b>b</b>) bottom layer.</p> Full article ">Figure 10
<p>Residual flow pattern in Ria de Ortigueira under case study CS5 conditions (vectors); differences in the residual flow magnitude between altered flow and reference situation (CS5–CS2) (colour map). (<b>a</b>) Surface layer; (<b>b</b>) bottom layer.</p> Full article ">Figure 11
<p>Residual flow pattern in Ria de Ortigueira under case study CS6 conditions (vectors); differences in the residual flow magnitude between altered flow and reference situation (CS6–CS2) (colour map). (<b>a</b>) Surface layer; (<b>b</b>) bottom layer.</p> Full article ">Figure 12
<p>Residual flow pattern in Ria de Ortigueira under case study CS7 conditions (vectors); differences in the residual flow magnitude between altered flow and reference situation (CS7–CS4) (colour map). (<b>a</b>) Surface layer; (<b>b</b>) bottom layer.</p> Full article ">Figure 13
<p>Residual flow pattern in Ria de Ortigueira under case study CS8 conditions (vectors); differences in the residual flow magnitude between altered flow and reference situation (CS8–CS4) (colour map). (<b>a</b>) Surface layer; (<b>b</b>) bottom layer.</p> Full article ">
15 pages, 2127 KiB  
Article
Impact of Physically and Chemically Dispersed Crude Oil on the Antioxidant Defense Capacities and Non-Specific Immune Responses in Sea Cucumber (Apostichopus japonicus)
by Xishan Li, Yuhang Zou, Hao Xuan, Wei Yang, Guoxiang Liao, Chengyan Wang and Deqi Xiong
J. Mar. Sci. Eng. 2022, 10(10), 1544; https://doi.org/10.3390/jmse10101544 - 20 Oct 2022
Cited by 3 | Viewed by 2024
Abstract
Currently, oil spill pollution is one of the major environmental concerns for sea cucumber (Apostichopus japonicus) aquaculture. During oil spills, spraying chemical dispersants is generally considered an efficient oil spill response. However, the impact of chemical dispersant deployment during oil spills [...] Read more.
Currently, oil spill pollution is one of the major environmental concerns for sea cucumber (Apostichopus japonicus) aquaculture. During oil spills, spraying chemical dispersants is generally considered an efficient oil spill response. However, the impact of chemical dispersant deployment during oil spills on sea cucumbers is still less known. In this study, we treated sea cucumbers with physically and chemically (by GM-2 chemical dispersant) dispersed Oman crude oil for 24 h. For antioxidant defense capacities, our results showed that physically dispersed crude oil caused a significant elevation on superoxide dismutase (SOD) and catalase (CAT) activities, and glutathione (GSH) content, while chemically dispersed crude oil caused a significant decrease in SOD activity and GSH content with no apparent change in CAT activity. As for non-specific immune responses, our results indicated that physically dispersed crude oil up-regulated acid phosphatase (ACP) and lysozyme (LZM) activities but had no obvious impact on alkaline phosphatase (ALP) activity. Differently, chemically dispersed crude oil down-regulated ACP and LZM activities while up-regulating ALP activity. Based on the integrated biomarker response analysis, the overall impact of chemically dispersed crude oil on antioxidant defense capacities and non-specific immune responses of sea cucumbers was more severe than physically dispersed crude oil. Full article
(This article belongs to the Section Marine Environmental Science)
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<p>Superoxide dismutase (SOD) activity (<b>A</b>) and catalase (CAT) activity (<b>B</b>) in the respiratory tree of sea cucumbers following acute exposure to GM-2 chemical dispersant alone (DISP, red-filled up-triangle), physically (LEWAF, blue-filled square) and chemically (CEWAF, green-filled down-triangle) dispersed Oman crude oil (<span class="html-italic">n</span> = 9). The control was sea cucumbers exposed to pre-filtered natural seawater only (control, black-filled circle). Error bars represent standard error (SEM), and intermediate lines represent mean of each treatment. Asterisks (* or ***) denote the significant differences between the treatments and the control (<span class="html-italic">p</span> &lt; 0.05 or 0.001, respectively). Dark traits denote the significant differences among different treatments (one-way analysis of variance (ANOVA)).</p> Full article ">Figure 2
<p>Glutathione (GSH) content in the respiratory tree of sea cucumbers following acute exposure to GM-2 chemical dispersant alone (DISP, red-filled up-triangle), physically (LEWAF, blue-filled square) and chemically (CEWAF, green-filled down-triangle) dispersed Oman crude oil (<span class="html-italic">n</span> = 9). The control was sea cucumbers exposed to pre-filtered natural seawater only (control, black-filled circle). Error bars represent standard error (SEM), and intermediate lines represent the mean of each treatment. Asterisks (* or ***) denote the significant differences between the treatments and the control (<span class="html-italic">p</span> &lt; 0.05 or 0.001, respectively). Dark traits denote the significant differences among different treatments (one-way analysis of variance (ANOVA)).</p> Full article ">Figure 3
<p>Lysozyme (LZM) relative activity in the respiratory tree of sea cucumbers following acute exposure to GM-2 chemical dispersant alone (DISP, red-filled up-triangle), physically (LEWAF, blue-filled square) and chemically (CEWAF, green-filled down-triangle) dispersed Oman crude oil (<span class="html-italic">n</span> = 9). The control was sea cucumbers exposed to pre-filtered natural seawater only (control, black-filled circle). Error bars represent standard error (SEM), and intermediate lines represent mean of each treatment. Asterisks (* or **) denote the significant differences between the treatments and the control (<span class="html-italic">p</span> &lt; 0.05 or 0.01, respectively). Dark traits denote the significant differences among different treatments (one-way analysis of variance (ANOVA)).</p> Full article ">Figure 4
<p>Acid phosphatase activity ACP (<b>A</b>) and alkaline phosphatase ALP (<b>B</b>) activities in the respiratory tree of sea cucumbers following acute exposure to GM-2 chemical dispersant alone (DISP, red-filled up-triangle), physically (LEWAF, blue-filled square) and chemically (CEWAF, green-filled down-triangle) dispersed Oman crude oil (<span class="html-italic">n</span> = 9). The control was sea cucumbers exposed to pre-filtered natural seawater only (control, black-filled circle). Error bars represent standard error (SEM), and intermediate lines represent the mean of each treatment. Asterisks (* or ***) denote the significant differences between the treatments and the control (<span class="html-italic">p</span> &lt; 0.05 or 0.001, respectively). Dark traits denote the significant differences among different treatments (one-way analysis of variance (ANOVA)).</p> Full article ">Figure 5
<p>Integrated biomarker response version 2 (IBRv2) index for sea cucumbers following acute exposure to GM-2 chemical dispersant alone (DISP, red-filled, (<b>A</b>)), physically (LEWAF, blue-filled, (<b>B</b>)) and chemically (CEWAF, green-filled, (<b>C</b>)) dispersed Oman crude oil. The control was sea cucumbers exposed to pre-filtered natural seawater only (control, black dash line). SOD: superoxide dismutase activity, CAT: catalase activity, GSH: glutathione content, LZM: lysozyme relative activity, ACP: acid phosphatase activity, ALP: alkaline phosphatase activity. The results of all biomarkers are represented in the reference treatment (control). The area above 0 or below 0 indicate positive or negative regulation, respectively.</p> Full article ">
17 pages, 9459 KiB  
Article
Integrating AIS, GIS and E-Chart to Analyze the Shipping Traffic and Marine Accidents at the Kaohsiung Port
by Chien-Chang Chou, Chia-Nan Wang, Hsien-Pin Hsu, Ji-Feng Ding, Wen-Jui Tseng and Chien-Yi Yeh
J. Mar. Sci. Eng. 2022, 10(10), 1543; https://doi.org/10.3390/jmse10101543 - 20 Oct 2022
Cited by 2 | Viewed by 3135
Abstract
In the past, case study and questionnaire survey methodologies have often been used to analyze the causes of marine accidents. One of the disadvantages of these two methods is that they can only interpret the specific causes of one particular marine accident at [...] Read more.
In the past, case study and questionnaire survey methodologies have often been used to analyze the causes of marine accidents. One of the disadvantages of these two methods is that they can only interpret the specific causes of one particular marine accident at a time. They cannot analyze and find the common causes of most marine accidents. Therefore, this study integrates the Automatic Identification System, Geographic Information System, and an e-chart to explore the relationship between environmental factors (wind, wave, tide, and current), locations, and significant common causes of marine accidents. Firstly, an Automatic Identification System is used to collect the traffic flows of vessels entering/exiting the port. The locations of maritime accidents were then plotted on an e-chart, after which we can quickly analyze the locations of marine accidents on the e-chart. Furthermore, environmental data are displayed using Geographic Information System. Subsequently, all data, including traffic flows of vessels, locations of marine accidents, and environmental data, are integrated into the e-chart simultaneously. As a result, the information related to factors affecting the probability of marine accidents could be displayed clearly on the e-chart. Finally, findings and conclusions are given to port authorities to help manage the ship traffic flow and reduce the probability of the occurrence of marine accidents around the port efficiently. Full article
(This article belongs to the Special Issue Contemporary Shipping Logistics and Port Management)
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<p>Diagram of research framework.</p> Full article ">Figure 2
<p>The traffic flows of vessels entering/exiting the Kaohsiung port. Note: the red line means ship track (enter); the green line means ship track (exit); the yellow area means land; the light blue area means coastal line.</p> Full article ">Figure 3
<p>The daily traffic flow of vessels entering/exiting the Kaohsiung port.</p> Full article ">Figure 4
<p>The levels of wind speed.</p> Full article ">Figure 5
<p>The levels of wave height.</p> Full article ">Figure 6
<p>The levels of tide height.</p> Full article ">Figure 7
<p>The levels of current speed.</p> Full article ">Figure 8
<p>The locations of marine accidents and the levels of wind, wave, tide, and current in the port of Kaohsiung. Note: a blue circle is wind speed and the number inside a blue circle is level of wind speed; a red circle is wave height and the number inside a red circle is level of wave height; a green circle is tide height and the number inside a green circle is level of tide height; a purple circle is current speed and the number inside a purple circle is level of current speed; a set of data (four circles including blue, red, green, and purple circles) means one marine accident, and the position of the blue circle is the location of that marine accident.</p> Full article ">Figure 9
<p>All data for the port of Kaohsiung are integrated on the e-chart. Note: lines A, A<sub>1</sub>, B, and B<sub>1</sub> are traffic flows; points C<sub>1</sub>, C<sub>2</sub>, …, C<sub>n</sub> are marine accidents within the port; points D<sub>1</sub> and D<sub>2</sub> are marine accidents in the channel; point E is the marine accident in the anchorage; a set of data (four circles including blue, red, green, and purple circles) means one marine accident, and the position of the blue circle is the location of that marine accident.</p> Full article ">Figure 10
<p>The locations of marine accidents.</p> Full article ">
14 pages, 10589 KiB  
Article
CFD Study on the Influence of Exostructure Elements on the Resistance of a Submarine
by Inno Gatin, Juvel Čokić, Darjan Romić and Joško Parunov
J. Mar. Sci. Eng. 2022, 10(10), 1542; https://doi.org/10.3390/jmse10101542 - 20 Oct 2022
Cited by 7 | Viewed by 2806
Abstract
Submersible vessels designed to operate at low speeds are often designed with an intricate exostructure, as well as other elements that are located outside of the main pressure hull. Exostructure elements are often of cylindrical or rectangular shape, positioned perpendicularly to the flow [...] Read more.
Submersible vessels designed to operate at low speeds are often designed with an intricate exostructure, as well as other elements that are located outside of the main pressure hull. Exostructure elements are often of cylindrical or rectangular shape, positioned perpendicularly to the flow direction. For this reason, their resistance coefficient is relatively large compared to the pressure hull or appendages of a classical submarine. In some cases, the exostructure can significantly increase the wetted surface of the vessel and dominate its resistance. This paper presents a study on how different exostructure elements impact the overall resistance of a submarine relative to the resistance of the cylindrical, smooth, pressure hull. Additionally, the effect of depth is also considered. The study is conducted using the RANS-based CFD method. The subject of the study is a 25 m long tourist submarine designed for depths up to 40 m and a speed of up to 3 knots. Full article
(This article belongs to the Special Issue Design, Analysis and Maintenance of Green Innovative Marine Structures)
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<p>Visual representation of the submarine.</p> Full article ">Figure 2
<p>Geometry 1: Smooth cylindrical hull, representing a simplified geometry of the pressure hull.</p> Full article ">Figure 3
<p>Geometry 2: Cylindrical hull with ring protrusions, representing the geometry of the pressure hull with rings.</p> Full article ">Figure 4
<p>Geometry 3: Geometry of the pressure hull and exostructure used in the CFD simulation. The geometry is divided into a number of different patches denoted on the figure.</p> Full article ">Figure 5
<p>Geometry 3: Geometry of the pressure hull and exostructure used in the CFD simulation, bottom view.</p> Full article ">Figure 6
<p>Geometry 3: Sliced view of the geometry.</p> Full article ">Figure 7
<p>Geometry 3: Sliced view of the discretised surface geometry of the submarine.</p> Full article ">Figure 8
<p>Total resistance results for all cases.</p> Full article ">Figure 9
<p>Total resistance of the smooth and simplified pressure hull, Geometry 1 and 2.</p> Full article ">Figure 9 Cont.
<p>Total resistance of the smooth and simplified pressure hull, Geometry 1 and 2.</p> Full article ">Figure 10
<p>Resistance of individual patches of Geometry 3 when sailing at the free surface (0 m depth).</p> Full article ">Figure 11
<p>Resistance of individual patches of Geometry 3 when sailing at depth of 6 m.</p> Full article ">Figure 12
<p>Resistance of individual patches of Geometry 3 when sailing at depth of 40 m.</p> Full article ">Figure 13
<p>Free surface elevation at 1.5 and 6 knots for Geometry 3 sailing at the free surface.</p> Full article ">Figure 14
<p>Pressure distribution and free surface geometry at 1.5 and 6 knots for Geometry 3 sailing at the free surface.</p> Full article ">Figure 15
<p>Streamlines around the submarine when sailing at 3 knots speed at 40 m depth.</p> Full article ">Figure 16
<p>Pressure distribution for Geometry 1, 2, and 3, from left to bottom right. Pressure is shown at 4.5 knots for Geometry 1 and 2, and at 3.0 knots for Geometry 3.</p> Full article ">
14 pages, 5151 KiB  
Article
Distributions of Radiocesium and Plutonium in the Korean Seas and North Pacific after the Fukushima Accident, 2011–2014
by Jaeeun Lee, Suk Hyun Kim, Huisu Lee, Hyunmi Lee and Intae Kim
J. Mar. Sci. Eng. 2022, 10(10), 1541; https://doi.org/10.3390/jmse10101541 - 20 Oct 2022
Cited by 3 | Viewed by 2822
Abstract
The distributions of artificial radionuclides, radiocesium (134Cs and 137Cs) and plutonium isotopes (238Pu and 239+240Pu), in the surface water around the Korean seas (East/Japan Sea and Yellow Sea) in 2011–2012 and in three sections in the North [...] Read more.
The distributions of artificial radionuclides, radiocesium (134Cs and 137Cs) and plutonium isotopes (238Pu and 239+240Pu), in the surface water around the Korean seas (East/Japan Sea and Yellow Sea) in 2011–2012 and in three sections in the North Pacific between 2011 and 2014 were examined. The 137Cs activities in the surface water in the Korean seas in 2011 (immediately after the Fukushima nuclear power plant (NPP) accident on 17 March 2011) were comparable or not significantly different relative to those in 2010 and 2012. However, 134Cs, which had been not detected in the study area before the Fukushima accident (under the detection limit of 0.1 mBq kg−1 level), were detected rapidly in 2011 after the accident (in about 60% of the 72 samples) and gradually disappeared due to their short half-life (t1/2 = 2.06 years) in 2012 (detected in about 16% of the 24 samples). In addition, the highest activities of radiocesium and Pu isotopes appeared locally in some stations of the Korean Strait region (located between Korea and Japan) within 1–2 months immediately after the accident. This suggests that the radioactive nuclides released immediately after the Fukushima accident were significantly introduced through the atmosphere, based on recent studies conducted in neighboring areas. We also showed that the spatial distribution of radiocesium in the North Pacific moved eastward from 2012 to 2014, and we attempted to quantify the residence time of radiocesium (137Cs) in the Korean seas based on the long-term (tens of years scale) temporal trends of 137Cs activity data, which have been collected since the 1960s and 1970s. The estimated retention time of 137Cs in the East/Japan Sea and Yellow Sea were 25 ± 0.6 and 8.0 ± 0.1 years, respectively. These results are expected to be used as a preliminary study for a potential future event of a marine radioactive accident (which, of course, cannot be predicted) and as basic data for predicting the influences of radionuclide releases in the ocean. Full article
(This article belongs to the Special Issue Environmental Radioactivity in the Ocean)
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Figure 1
<p>Map showing the locations of the Korean seas (East/Japan Sea and Yellow Sea), the Korean Strait, and the Asian countries in the marginal region of the North Pacific with the major current directions.</p> Full article ">Figure 2
<p>The spatial distributions of the <sup>134</sup>Cs ((<b>a</b>,<b>b</b>), upper panel) and <sup>137</sup>Cs ((<b>c</b>,<b>d</b>), lower panel) activities in the Korean seas from 2011 ((<b>a</b>,<b>c</b>)) to 2012 ((<b>b</b>,<b>d</b>)). The red square indicates the region (Korean Strait) where the sample (data) was collected on 29–30 March 2011.</p> Full article ">Figure 3
<p>The comparisons of the annual mean activities of <sup>134</sup>Cs and <sup>137</sup>Cs in the Korean seas from 2006 to 2012. All the data were applied from the national report of Korea Institute of Nuclear Safety (KINS) [<a href="#B45-jmse-10-01541" class="html-bibr">45</a>]. We note that the data from 2011 and 2012 are in the same 22 locations, but the data before 2010 were only for eight stations per year before the expansion of the monitoring region due to the Fukushima accident.</p> Full article ">Figure 4
<p>The spatial distributions of: (<b>a</b>) <sup>238</sup>Pu; and (<b>b</b>) <sup>239+240</sup>Pu and activities. The red square indicates the region (Korean Strait) where the samples (data) were collected on 29–30 March 2011 (same region as in <a href="#jmse-10-01541-f003" class="html-fig">Figure 3</a>).</p> Full article ">Figure 5
<p>The sectional distributions of radiocesium, <sup>134</sup>Cs (upper panel, (<b>a</b>–<b>c</b>)); and <sup>137</sup>Cs (lower panel, (<b>d</b>–<b>f</b>)), from three (#1 to #3) sections in the North Pacific, 2012–2014.</p> Full article ">Figure 6
<p>The activity ratios: (<b>a</b>) <sup>134</sup>Cs/<sup>137</sup>Cs in the Korean seas (2011); (<b>b</b>) <sup>238</sup>Pu/<sup>239+240</sup>Pu (2011) in the Korean seas; and (<b>c</b>) <sup>134</sup>Cs/<sup>137</sup>Cs in the North Pacific (2012).</p> Full article ">Figure 7
<p>Distribution of the <sup>239+240</sup>Pu/<sup>137</sup>Cs ratio over time after the Fukushima NPP accident (D-day = 0) in the Korean seas’ surface water.</p> Full article ">Figure 8
<p>The long-term trends (1960s to present) of <sup>137</sup>Cs activity in the Korean seas. Past data other than those measured in this study were downloaded and applied from the IAEA MARIS (IAEA’s MARine Information System, <a href="https://maris.iaea.org/" target="_blank">https://maris.iaea.org/</a>, accessed on 1 September 2022) web database. The environmental removal constant (exponential decrease) to define the environmental half-life was obtained by the coefficient (k) of the best-fitting curve (of exponential decay, A = A<sub>0</sub> (exp(−kt))), (0.146 yr<sup>−1</sup> and 0.062 yr<sup>−1</sup> in Yellow Sea and East/Japan Sea, respectively) The fitting curve equation is calculated by with Sigma–Plot professional 10.0 software.</p> Full article ">
14 pages, 7108 KiB  
Article
Numerical Simulation Analysis on the Lateral Dynamic Characteristics of Deepwater Conductor Considering the Pile-Soil Contact Models
by Yanbin Wang, Deli Gao and Chenyu Meng
J. Mar. Sci. Eng. 2022, 10(10), 1540; https://doi.org/10.3390/jmse10101540 - 19 Oct 2022
Cited by 5 | Viewed by 1982
Abstract
It is important to accurately assess the interaction between the conductor and the soil to ensure the stability of the subsea wellheads during deepwater drilling. In this paper, numerical simulations were carried out to study the lateral dynamic bearing capacity of the conductor [...] Read more.
It is important to accurately assess the interaction between the conductor and the soil to ensure the stability of the subsea wellheads during deepwater drilling. In this paper, numerical simulations were carried out to study the lateral dynamic bearing capacity of the conductor considering different contact models between the conductor and the soil. In particular, the contact surface model and contact element model were selected to study the dynamic behavior of pile–soil under a transverse periodic load. On this basis, the influence of the bending moment, the wellhead stick-up, the outer diameter (O.D.) of the conductor and the wall thickness (W.T.) of the conductor, as well as the physical parameters of the soil on the dynamic bearing capacity are discussed in detail. Analysis results show that the lateral deformation, deflection angle and von Mises stress calculated by the contact element model are greater than those calculated by the contact surface model. The maximum value of the lateral deformation and bending moment of the conductor decrease with the O.D. and W.T. of the conductor, and the cohesion and internal friction angle of the soil. However, the maximum value of the lateral deformation and bending moment of the conductor increase with the wellhead stick-up. Both the vertical force and the soil density have a negligible effect on the lateral behavior of the conductor. This study has reference value for the design and stability assessment of subsea wellheads. Full article
(This article belongs to the Special Issue Theory, Method and Engineering Application of Computational Mechanics in Offshore Structures)
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<p>Schematic diagram of the conductor after installation of drilling riser and BOP.</p> Full article ">Figure 2
<p>Contact surface behavior of pile–soil interaction.</p> Full article ">Figure 3
<p>Mechanical behavior of different contact models, where (<b>a</b>) is the hard contact model in normal contact model, (<b>b</b>) is the soft contact model in normal contact model, and (<b>c</b>) is the tangential contact model.</p> Full article ">Figure 4
<p>Goodman contact element model.</p> Full article ">Figure 5
<p>The 3-D FE model after completion of boundary conditions (cross-section view).</p> Full article ">Figure 6
<p>The 3-D FE model after completion of loading.</p> Full article ">Figure 7
<p>The 3-D FE model after completion of meshing.</p> Full article ">Figure 8
<p>Simulation results of the lateral deformation, where (<b>a</b>) is the result calculated by the contact surface model and (<b>b</b>) is that calculated by the Goodman contact element model.</p> Full article ">Figure 9
<p>The lateral deformation along the conductor axial direction, where (<b>a</b>) is the result calculated by the contact surface model and (<b>b</b>) is that calculated by the Goodman contact element model.</p> Full article ">Figure 10
<p>The von Mises stress of the conductor, where (<b>a</b>) is calculated by the contact surface model and (<b>b</b>) is calculated by the Goodman contact element model.</p> Full article ">Figure 11
<p>Model verification results.</p> Full article ">Figure 12
<p>The influence of the bending moment on the mechanical response of the conductor, where (<b>a</b>) is the variation of the lateral deformation at the top of the conductor with time and (<b>b</b>) is the variation of the bending moment along the conductor axial direction when the maximum bending moment is 4000 kN·m.</p> Full article ">Figure 13
<p>The influence of wellhead stick-up on the mechanical response of the conductor, where (<b>a</b>) is the variation of lateral moment at the top of the conductor with time and (<b>b</b>) is the variation of bending moment along the conductor axial direction when the wellhead stick-up is 6 m.</p> Full article ">Figure 14
<p>The influence of O.D. on the mechanical response of the conductor, where (<b>a</b>) is the variation of the lateral moment at the top of the conductor with time and (<b>b</b>) is the variation of the bending moment along the conductor axial direction when the O.D. and W.T. are 36 in and 1.0 in, respectively.</p> Full article ">Figure 15
<p>The influence of the W.T. on the mechanical response of the conductor, where (<b>a</b>) is the variation of the lateral moment at the top of the conductor with time and (<b>b</b>) is the variation of the bending moment along the conductor axial direction when the O.D. and W.T. are 36 in and 1.5 in, respectively.</p> Full article ">Figure 16
<p>The influence of the internal friction angle on the mechanical response of the conductor, where (<b>a</b>) is the variation of the lateral moment at the top of the conductor with time and (<b>b</b>) is the variation of the bending moment along the conductor axial direction when the internal friction angle is 40°.</p> Full article ">Figure 17
<p>The influence of cohesion on the mechanical response of the conductor, where (<b>a</b>) is the variation of the lateral moment at the top of the conductor with time and (<b>b</b>) is the variation of the bending moment along the conductor axial direction when the cohesion is 20 kPa.</p> Full article ">
13 pages, 1598 KiB  
Article
Optimization of Process Parameters in Friction Stir Welding of Aluminum 5451 in Marine Applications
by Shoaib Ahmed, Rana Atta ur Rahman, Awais Awan, Sajjad Ahmad, Waseem Akram, Muhammad Amjad, Mohd Yazid Yahya and Seyed Saeid Rahimian Koloor
J. Mar. Sci. Eng. 2022, 10(10), 1539; https://doi.org/10.3390/jmse10101539 - 19 Oct 2022
Cited by 20 | Viewed by 3680
Abstract
Friction stir welding (FSW) is one of the primary fabrication techniques for joining different components, and it has become popular, especially in aluminum alloy structures for marine applications. The welded joint with the friction stir process greatly depends on the process parameters, i.e., [...] Read more.
Friction stir welding (FSW) is one of the primary fabrication techniques for joining different components, and it has become popular, especially in aluminum alloy structures for marine applications. The welded joint with the friction stir process greatly depends on the process parameters, i.e., feed rate, rotational speed, and pin profile of the tool. In the current study, plates of aluminum 5451 alloy were joined by the FSW technique, and the Taguchi method was used to find the process parameters at an optimal level. The maximum value of tensile strength, i.e., 160.6907 MPa, was achieved using optimum welding conditions of a tool rotation speed of 1400, a feed rate of 18 mm/min, and the tool pin with threads. The maximum value of hardness, i.e., 81.056 HV, was achieved using optimum conditions of 1200 tool rotational speed and a feed rate of 18 mm/min with a tool pin profile having threads. In addition, the contribution in terms of the percentage of each input parameter was found by the analysis of variance (ANOVA). The ANOVA results revealed that the pin profile of the tool has the maximum contribution of 67.77% and 62.42% in achieving the optimum value of tensile strength and hardness, respectively. The study also investigated the joint efficiency of the friction stir welded joint, hardness at the weld zone, and metallography on FSW samples at the optimized level. The effectiveness and reliability of FSW joints for shipping industry applications can be observed by joint efficiency. That was investigated at optimum conditions, and it comes out to be 80.5%. Full article
(This article belongs to the Special Issue Failure Analysis of Marine Structure)
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<p>Tool profiles in the FSW process.</p> Full article ">Figure 2
<p>The geometry of tensile test specimens is based on the standard.</p> Full article ">Figure 3
<p>Samples used in the tensile test; (<b>A</b>) before and (<b>B</b>) after the test.</p> Full article ">Figure 4
<p>Main effect plot of signal-to-noise ratio for tensile strength.</p> Full article ">Figure 5
<p>Main effect plot of signal-to-noise ratio for hardness (weld zone).</p> Full article ">Figure 6
<p>Vickers microhardness of the weld regions.</p> Full article ">Figure 7
<p>Optical micrographs of AA5451 (base metal) and FSW region at an optimum level.</p> Full article ">
18 pages, 920 KiB  
Article
Application of Fuzzy Delphi-AHP-TOPSIS for Selecting an International Crew Change Center in Taiwan
by Tien-Chun Ho and Hsuan-Shih Lee
J. Mar. Sci. Eng. 2022, 10(10), 1538; https://doi.org/10.3390/jmse10101538 - 19 Oct 2022
Cited by 2 | Viewed by 2494
Abstract
The COVID-19 crisis has brought disruption to the global economy and to international passenger and cargo transportation, and the unprecedented crew change crisis remains an issue for governments around the world to address. The selection of a port for international crew changes is [...] Read more.
The COVID-19 crisis has brought disruption to the global economy and to international passenger and cargo transportation, and the unprecedented crew change crisis remains an issue for governments around the world to address. The selection of a port for international crew changes is a major decision for a country, and this port selection can be considered as a multi-criteria decision-making (MCDM) issue. As with other facility-siting issues, the issue of selecting a port for international crew changes requires consideration of several criteria relative to cargo transshipment, and since this process involves uncertainty, fuzzy logic must be incorporated into the process to obtain more accurate results. This study proceeds from the standpoint of shipping companies and ship management companies, conducting a survey questionnaire on carriers calling at Taiwan ports using cargo structure, transit costs, transit time, environmental factors, geographic location, infrastructure, and crew safety certification facilities. Fuzzy Delphi and FAHP are used to obtain the subjective opinions of carriers and FTOPSIS is used to explore and prioritize the objective opinions of carriers on international crew change ports. This is then used to construct an evaluation model of the key factors influencing the selection of an international crew change location for the development of Taiwanese ports. The results of the study showed that the hinterland industry economy was the main key factor and Kaohsiung container terminal 5 was the most suitable place for crew replacement. Full article
(This article belongs to the Special Issue Recent Scientific Developments in Port Logistics)
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<p>Key influencing factors and best alternatives for developing international crew transit centers in Taiwanese ports.</p> Full article ">
31 pages, 12201 KiB  
Article
Wave Height Reduction Inside Pohang New Port, Korea, Due to the Construction of a Detached Breakwater
by Kyong Ho Ryu, Weon Mu Jeong, Jung-Eun Oh, Won-Dae Baek and Yeon S. Chang
J. Mar. Sci. Eng. 2022, 10(10), 1537; https://doi.org/10.3390/jmse10101537 - 19 Oct 2022
Cited by 2 | Viewed by 2393
Abstract
The effect of a detached breakwater, which was constructed to improve harbor tranquility inside Pohang New Port, was examined through the comparison of wave data measured before and after the construction of the breakwater. The observation data showed that the wave energy was [...] Read more.
The effect of a detached breakwater, which was constructed to improve harbor tranquility inside Pohang New Port, was examined through the comparison of wave data measured before and after the construction of the breakwater. The observation data showed that the wave energy was effectively reduced by the breakwater, although the wave height measured outside the breakwater was higher after its construction. The wave energy was reduced in all of the measured wave-propagating directions, but it was also observed that the breakwater became less effective in protecting against northeastwaves than in protecting against NNE waves. The BOUSS-2D Boussinesq-type wave model was employed to analyze the pattern of wave propagation, showing that, before the breakwater’s construction, NE waves could directly enter the port, increasing the wave energy inside the port. After the breakwater’s construction, simulations showed that the detached breakwater effectively blocked the waves approaching the port from both the NNE and NE directions, although the wave heights of the waves from the extreme NE direction inside the port increased. Considering that the estimated probability of failing to preserve the port tranquility was only 0.2–0.5% for these extreme NE waves, it was concluded that no secondary structures were necessary, and the existing breakwater was sufficient for the protection of the port. Full article
(This article belongs to the Special Issue Advanced Studies in Breakwaters and Coastal Protection)
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<p>(<b>a</b>) Map of the Korean Peninsula with the location of Yeongil Bay, (<b>b</b>) map of Yeongil Bay located in the southeast of the Korean Peninsula, and (<b>c</b>) magnified map of Yeongil Bay with the locations of wave measurements. W0 and W1 (red triangles): locations where AWAC instruments were installed on the sea bed at the entrance of Yeongil Bay (W0) and just offshore of the detached breakwater (W1); W2–W5 (white circles): locations of the pressure sensors inside Pohang New Port; W6 and W7 (white circles): locations of pressure sensors in the nearshore of Dogu Beach (W6) and Yeongildae Beach (W7).</p> Full article ">Figure 2
<p>Comparison of <math display="inline"><semantics> <mrow> <msub> <mi>H</mi> <mrow> <mi>m</mi> <mn>0</mn> </mrow> </msub> </mrow> </semantics></math> in W1 (blue) and W3 (red): (<b>a</b>) first period before detached breakwater construction (BC01) in 15 October 2008–12 July 2009, (<b>b</b>) before detached breakwater construction (BC02) in 21 May 2018–13 February 2099, and (<b>c</b>) after detached breakwater construction (AC).</p> Full article ">Figure 2 Cont.
<p>Comparison of <math display="inline"><semantics> <mrow> <msub> <mi>H</mi> <mrow> <mi>m</mi> <mn>0</mn> </mrow> </msub> </mrow> </semantics></math> in W1 (blue) and W3 (red): (<b>a</b>) first period before detached breakwater construction (BC01) in 15 October 2008–12 July 2009, (<b>b</b>) before detached breakwater construction (BC02) in 21 May 2018–13 February 2099, and (<b>c</b>) after detached breakwater construction (AC).</p> Full article ">Figure 3
<p>(<b>a</b>) <math display="inline"><semantics> <mrow> <msub> <mi>H</mi> <mrow> <mi>m</mi> <mn>0</mn> </mrow> </msub> </mrow> </semantics></math> (blue line) and <math display="inline"><semantics> <mrow> <msub> <mi>D</mi> <mi>p</mi> </msub> </mrow> </semantics></math> (black circles) in W1 and <math display="inline"><semantics> <mrow> <msub> <mi>H</mi> <mrow> <mi>m</mi> <mn>0</mn> </mrow> </msub> </mrow> </semantics></math> in W3 (red line) for a selected wave event in BC; (<b>b</b>) same as (<b>a</b>) but an event in AC. These two wave events were also selected for the numerical experiments, as described in <a href="#sec2dot3-jmse-10-01537" class="html-sec">Section 2.3</a> and <a href="#sec3dot2-jmse-10-01537" class="html-sec">Section 3.2</a>.</p> Full article ">Figure 4
<p>(<b>a</b>) Map of Yeongil Bay around Pohang New Port, in which the computational domain for the BOUSS-2D wave model is marked in a red rectangle, (<b>b</b>) model domain with the bathymetry map and the locations of wave stations. It is noted that the domain is rotated 40° in the NE direction for the outer open boundary to face <math display="inline"><semantics> <mrow> <mi mathvariant="normal">N</mi> <mn>40</mn> <mo>°</mo> <mi mathvariant="normal">E</mi> </mrow> </semantics></math>.</p> Full article ">Figure 5
<p>(<b>a</b>) Maximum values of <math display="inline"><semantics> <mrow> <msub> <mi>H</mi> <mrow> <mi>m</mi> <mn>0</mn> </mrow> </msub> </mrow> </semantics></math> observed in the 21 events before construction in W1 (black circles), W2 (blue squares), W3 (red stars), W4 (green crosses), and W5 (pink triangles) for BC01 (15 October 2008~12 July 2009); (<b>b</b>) maximum <math display="inline"><semantics> <mrow> <msub> <mi>H</mi> <mrow> <mi>m</mi> <mn>0</mn> </mrow> </msub> </mrow> </semantics></math> values for the 17 events before construction in W1 (black circles), W3 (red stars), W4 (green crosses), and W5 (pink triangles) for BC02 (21 May 2018~13 February 2019) (it is noted that the <math display="inline"><semantics> <mrow> <msub> <mi>H</mi> <mrow> <mi>m</mi> <mn>0</mn> </mrow> </msub> </mrow> </semantics></math> in W2 was not included due to poor data quality); (<b>c</b>) maximum <math display="inline"><semantics> <mrow> <msub> <mi>H</mi> <mrow> <mi>m</mi> <mn>0</mn> </mrow> </msub> </mrow> </semantics></math> values for the 27 events after the construction in W1 (black circles), W2 (blue squares), W3 (red stars), W4 (green crosses), and W5 (pink triangles). The pattern of <math display="inline"><semantics> <mrow> <msub> <mi>H</mi> <mrow> <mi>m</mi> <mn>0</mn> </mrow> </msub> </mrow> </semantics></math> variation is similar between the wave stations outside (W1) and inside (W2, W3, W3, and W4) the port.</p> Full article ">Figure 6
<p>Same as <a href="#jmse-10-01537-f005" class="html-fig">Figure 5</a> but for <math display="inline"><semantics> <mrow> <msub> <mi>D</mi> <mi>p</mi> </msub> </mrow> </semantics></math> values that were observed at the same time as the <math display="inline"><semantics> <mrow> <msub> <mi>H</mi> <mrow> <mi>m</mi> <mn>0</mn> </mrow> </msub> </mrow> </semantics></math> in <a href="#jmse-10-01537-f005" class="html-fig">Figure 5</a> (i.e., <math display="inline"><semantics> <mrow> <msub> <mi>D</mi> <mi>p</mi> </msub> </mrow> </semantics></math> in <a href="#jmse-10-01537-f006" class="html-fig">Figure 6</a> was measured at the time when the <math display="inline"><semantics> <mrow> <msub> <mi>H</mi> <mrow> <mi>m</mi> <mn>0</mn> </mrow> </msub> </mrow> </semantics></math> value was maximum for each wave event). Compared to the results in <a href="#jmse-10-01537-f005" class="html-fig">Figure 5</a>, it shows a lower correlation between <math display="inline"><semantics> <mrow> <msub> <mi>D</mi> <mi>p</mi> </msub> </mrow> </semantics></math> outside the port (W1) and <math display="inline"><semantics> <mrow> <msub> <mi>H</mi> <mrow> <mi>m</mi> <mn>0</mn> </mrow> </msub> </mrow> </semantics></math> inside the port (W2, W3, W3, and W4).</p> Full article ">Figure 7
<p>Comparison of the distributions of (<b>a</b>) significant wave height (<math display="inline"><semantics> <mrow> <msub> <mi>H</mi> <mrow> <mi>m</mi> <mn>0</mn> </mrow> </msub> </mrow> </semantics></math>) and (<b>b</b>) peak wave direction (<math display="inline"><semantics> <mrow> <msub> <mi>D</mi> <mi>p</mi> </msub> </mrow> </semantics></math>) between W0 and W1 for the selected events for both BC (only for the second period from 2018 to 2019) and AC. The coefficient of determination (<math display="inline"><semantics> <mrow> <msup> <mi>r</mi> <mn>2</mn> </msup> </mrow> </semantics></math> ) was 0.83 for <math display="inline"><semantics> <mrow> <msub> <mi>H</mi> <mrow> <mi>m</mi> <mn>0</mn> </mrow> </msub> </mrow> </semantics></math> and 0.16 for <math display="inline"><semantics> <mrow> <msub> <mi>D</mi> <mi>p</mi> </msub> </mrow> </semantics></math>. Wave measurements show a high correlation in <math display="inline"><semantics> <mrow> <msub> <mi>H</mi> <mrow> <mi>m</mi> <mn>0</mn> </mrow> </msub> </mrow> </semantics></math> between the two wave stations of W0 and W1, whereas they show a lower correlation in the case of <math display="inline"><semantics> <mrow> <msub> <mi>D</mi> <mi>p</mi> </msub> </mrow> </semantics></math>.</p> Full article ">Figure 8
<p>Scattered plots of <math display="inline"><semantics> <mrow> <msub> <mi>R</mi> <mrow> <mi>W</mi> <mo>#</mo> </mrow> </msub> </mrow> </semantics></math> <math display="inline"><semantics> <mrow> <mrow> <mo>[</mo> <mrow> <msub> <mi>R</mi> <mrow> <mi>W</mi> <mn>2</mn> </mrow> </msub> <mrow> <mo>(</mo> <mrow> <mo>=</mo> <mfrac> <mrow> <msub> <mi>H</mi> <mrow> <mi>m</mi> <mn>0</mn> </mrow> </msub> <mrow> <mo>[</mo> <mrow> <mi>W</mi> <mn>2</mn> </mrow> <mo>]</mo> </mrow> </mrow> <mrow> <msub> <mi>H</mi> <mrow> <mi>m</mi> <mn>0</mn> </mrow> </msub> <mrow> <mo>[</mo> <mrow> <mi>W</mi> <mn>1</mn> </mrow> <mo>]</mo> </mrow> </mrow> </mfrac> </mrow> <mo>)</mo> </mrow> <mo>,</mo> <msub> <mi>R</mi> <mrow> <mi>W</mi> <mn>3</mn> </mrow> </msub> <mrow> <mo>(</mo> <mrow> <mo>=</mo> <mfrac> <mrow> <msub> <mi>H</mi> <mrow> <mi>m</mi> <mn>0</mn> </mrow> </msub> <mrow> <mo>[</mo> <mrow> <mi>W</mi> <mn>3</mn> </mrow> <mo>]</mo> </mrow> </mrow> <mrow> <msub> <mi>H</mi> <mrow> <mi>m</mi> <mn>0</mn> </mrow> </msub> <mrow> <mo>[</mo> <mrow> <mi>W</mi> <mn>1</mn> </mrow> <mo>]</mo> </mrow> </mrow> </mfrac> </mrow> <mo>)</mo> </mrow> <mo>,</mo> <msub> <mi>R</mi> <mrow> <mi>W</mi> <mn>4</mn> </mrow> </msub> <mrow> <mo>(</mo> <mrow> <mo>=</mo> <mfrac> <mrow> <msub> <mi>H</mi> <mrow> <mi>m</mi> <mn>0</mn> </mrow> </msub> <mrow> <mo>[</mo> <mrow> <mi>W</mi> <mn>4</mn> </mrow> <mo>]</mo> </mrow> </mrow> <mrow> <msub> <mi>H</mi> <mrow> <mi>m</mi> <mn>0</mn> </mrow> </msub> <mrow> <mo>[</mo> <mrow> <mi>W</mi> <mn>1</mn> </mrow> <mo>]</mo> </mrow> </mrow> </mfrac> </mrow> <mo>)</mo> </mrow> <mo>,</mo> <msub> <mi>R</mi> <mrow> <mi>W</mi> <mn>5</mn> </mrow> </msub> <mrow> <mo>(</mo> <mrow> <mo>=</mo> <mfrac> <mrow> <msub> <mi>H</mi> <mrow> <mi>m</mi> <mn>0</mn> </mrow> </msub> <mrow> <mo>[</mo> <mrow> <mi>W</mi> <mn>5</mn> </mrow> <mo>]</mo> </mrow> </mrow> <mrow> <msub> <mi>H</mi> <mrow> <mi>m</mi> <mn>0</mn> </mrow> </msub> <mrow> <mo>[</mo> <mrow> <mi>W</mi> <mn>1</mn> </mrow> <mo>]</mo> </mrow> </mrow> </mfrac> </mrow> <mo>)</mo> </mrow> </mrow> <mo>]</mo> </mrow> </mrow> </semantics></math> versus <math display="inline"><semantics> <mrow> <msub> <mi>D</mi> <mi>p</mi> </msub> </mrow> </semantics></math> measured in W1 for the data measured during the 38 wave events in BC. (<b>a</b>) <math display="inline"><semantics> <mrow> <msub> <mi>R</mi> <mrow> <mi>W</mi> <mn>2</mn> </mrow> </msub> </mrow> </semantics></math> vs. <math display="inline"><semantics> <mrow> <msub> <mi>D</mi> <mi>p</mi> </msub> </mrow> </semantics></math>, (<b>b</b>) <math display="inline"><semantics> <mrow> <msub> <mi>R</mi> <mrow> <mi>W</mi> <mn>3</mn> </mrow> </msub> </mrow> </semantics></math> vs. <math display="inline"><semantics> <mrow> <msub> <mi>D</mi> <mi>p</mi> </msub> </mrow> </semantics></math>, (<b>c</b>) <math display="inline"><semantics> <mrow> <msub> <mi>R</mi> <mrow> <mi>W</mi> <mn>4</mn> </mrow> </msub> </mrow> </semantics></math> vs. <math display="inline"><semantics> <mrow> <msub> <mi>D</mi> <mi>p</mi> </msub> </mrow> </semantics></math>, (<b>d</b>) <math display="inline"><semantics> <mrow> <msub> <mi>R</mi> <mrow> <mi>W</mi> <mn>5</mn> </mrow> </msub> </mrow> </semantics></math> vs. <math display="inline"><semantics> <mrow> <msub> <mi>D</mi> <mi>p</mi> </msub> </mrow> </semantics></math>, (<b>e</b>) <math display="inline"><semantics> <mrow> <msub> <mi>R</mi> <mrow> <mi>W</mi> <mn>6</mn> </mrow> </msub> </mrow> </semantics></math> vs. <math display="inline"><semantics> <mrow> <msub> <mi>D</mi> <mi>p</mi> </msub> </mrow> </semantics></math>, (<b>f</b>) <math display="inline"><semantics> <mrow> <msub> <mi>R</mi> <mrow> <mi>W</mi> <mn>7</mn> </mrow> </msub> </mrow> </semantics></math> vs. <math display="inline"><semantics> <mrow> <msub> <mi>D</mi> <mi>p</mi> </msub> </mrow> </semantics></math>. The red line in each panel shows the linear regression calculated for each data set. It is noted that <math display="inline"><semantics> <mrow> <msub> <mi>R</mi> <mrow> <mi>W</mi> <mn>2</mn> </mrow> </msub> </mrow> </semantics></math> was calculated from the BC01 data (2008–2009) due to the poor quality of the measurements in BC02 (2018–2019). In addition, BC01 data were not available in the case of <math display="inline"><semantics> <mrow> <msub> <mi>R</mi> <mrow> <mi>W</mi> <mn>6</mn> </mrow> </msub> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msub> <mi>R</mi> <mrow> <mi>W</mi> <mn>7</mn> </mrow> </msub> </mrow> </semantics></math>, as the waves were not measured in these stations in the 2008–2009 experimental period. <math display="inline"><semantics> <mrow> <msub> <mi>R</mi> <mrow> <mi>W</mi> <mo>#</mo> </mrow> </msub> </mrow> </semantics></math> consistently tended to increase with increasing <math display="inline"><semantics> <mrow> <msub> <mi>D</mi> <mrow> <mi>p</mi> <mo>,</mo> <mo> </mo> <mi>W</mi> <mn>1</mn> </mrow> </msub> </mrow> </semantics></math> in the wave stations inside the port (W2, W3, W4, and W5) and that located in the west corner of Yeongil Bay (W7), whereas <math display="inline"><semantics> <mrow> <msub> <mi>R</mi> <mrow> <mi>W</mi> <mo>#</mo> </mrow> </msub> </mrow> </semantics></math> decreased with increasing <math display="inline"><semantics> <mrow> <msub> <mi>D</mi> <mrow> <mi>p</mi> <mo>,</mo> <mo> </mo> <mi>W</mi> <mn>1</mn> </mrow> </msub> </mrow> </semantics></math> in the wave station located in the southeast corner of the bay.</p> Full article ">Figure 9
<p>Same as <a href="#jmse-10-01537-f008" class="html-fig">Figure 8</a>, but for the data measured during the 27 wave events in AC. Similar to the case of BC, it was found that <math display="inline"><semantics> <mrow> <msub> <mi>R</mi> <mrow> <mi>W</mi> <mo>#</mo> </mrow> </msub> </mrow> </semantics></math> consistently tended to increase with increasing <math display="inline"><semantics> <mrow> <msub> <mi>D</mi> <mrow> <mi>p</mi> <mo>,</mo> <mi>W</mi> <mn>1</mn> </mrow> </msub> </mrow> </semantics></math> in the wave stations inside the port (W2, W3, W4, and W5) and that located in the west corner of Yeongil Bay (W7), whereas <math display="inline"><semantics> <mrow> <msub> <mi>R</mi> <mrow> <mi>W</mi> <mo>#</mo> </mrow> </msub> </mrow> </semantics></math> decreased with increasing <math display="inline"><semantics> <mrow> <msub> <mi>D</mi> <mrow> <mi>p</mi> <mo>,</mo> <mi>W</mi> <mn>1</mn> </mrow> </msub> </mrow> </semantics></math> in the wave station located in the southeast corner of the bay.</p> Full article ">Figure 10
<p>Same as <a href="#jmse-10-01537-f008" class="html-fig">Figure 8</a>, but the x-axis is <math display="inline"><semantics> <mrow> <msub> <mi>H</mi> <mrow> <mi>m</mi> <mn>0</mn> </mrow> </msub> </mrow> </semantics></math> measured in W1 in BC. (<b>a</b>) <math display="inline"><semantics> <mrow> <msub> <mi>R</mi> <mrow> <mi>W</mi> <mn>2</mn> </mrow> </msub> </mrow> </semantics></math> vs. <math display="inline"><semantics> <mrow> <msub> <mi>H</mi> <mrow> <mi>m</mi> <mn>0</mn> </mrow> </msub> </mrow> </semantics></math>, (<b>b</b>) <math display="inline"><semantics> <mrow> <msub> <mi>R</mi> <mrow> <mi>W</mi> <mn>3</mn> </mrow> </msub> </mrow> </semantics></math> vs. <math display="inline"><semantics> <mrow> <msub> <mi>H</mi> <mrow> <mi>m</mi> <mn>0</mn> </mrow> </msub> </mrow> </semantics></math>, (<b>c</b>) <math display="inline"><semantics> <mrow> <msub> <mi>R</mi> <mrow> <mi>W</mi> <mn>4</mn> </mrow> </msub> </mrow> </semantics></math> vs. <math display="inline"><semantics> <mrow> <msub> <mi>H</mi> <mrow> <mi>m</mi> <mn>0</mn> </mrow> </msub> </mrow> </semantics></math>, (<b>d</b>) <math display="inline"><semantics> <mrow> <msub> <mi>R</mi> <mrow> <mi>W</mi> <mn>5</mn> </mrow> </msub> </mrow> </semantics></math> vs. <math display="inline"><semantics> <mrow> <msub> <mi>H</mi> <mrow> <mi>m</mi> <mn>0</mn> </mrow> </msub> </mrow> </semantics></math>, (<b>e</b>) <math display="inline"><semantics> <mrow> <msub> <mi>R</mi> <mrow> <mi>W</mi> <mn>6</mn> </mrow> </msub> </mrow> </semantics></math> vs. <math display="inline"><semantics> <mrow> <msub> <mi>H</mi> <mrow> <mi>m</mi> <mn>0</mn> </mrow> </msub> </mrow> </semantics></math>, (<b>f</b>) <math display="inline"><semantics> <mrow> <msub> <mi>R</mi> <mrow> <mi>W</mi> <mn>7</mn> </mrow> </msub> </mrow> </semantics></math> vs. <math display="inline"><semantics> <mrow> <msub> <mi>H</mi> <mrow> <mi>m</mi> <mn>0</mn> </mrow> </msub> </mrow> </semantics></math>.The correlation between <math display="inline"><semantics> <mrow> <msub> <mi>R</mi> <mrow> <mi>W</mi> <mo>#</mo> </mrow> </msub> </mrow> </semantics></math> and the wave propagation direction (<math display="inline"><semantics> <mrow> <msub> <mi>D</mi> <mrow> <mi>p</mi> <mo>,</mo> <mi>W</mi> <mn>1</mn> </mrow> </msub> </mrow> </semantics></math> ) observed in <a href="#jmse-10-01537-f008" class="html-fig">Figure 8</a> and <a href="#jmse-10-01537-f009" class="html-fig">Figure 9</a> was not found in the case of the wave height (<math display="inline"><semantics> <mrow> <msub> <mi>H</mi> <mrow> <mi>m</mi> <mn>0</mn> </mrow> </msub> </mrow> </semantics></math> ).</p> Full article ">Figure 11
<p>Same as <a href="#jmse-10-01537-f010" class="html-fig">Figure 10</a>, but in AC. (<b>a</b>) <math display="inline"><semantics> <mrow> <msub> <mi>R</mi> <mrow> <mi>W</mi> <mn>2</mn> </mrow> </msub> </mrow> </semantics></math> vs. <math display="inline"><semantics> <mrow> <msub> <mi>H</mi> <mrow> <mi>m</mi> <mn>0</mn> </mrow> </msub> </mrow> </semantics></math>, (<b>b</b>) <math display="inline"><semantics> <mrow> <msub> <mi>R</mi> <mrow> <mi>W</mi> <mn>3</mn> </mrow> </msub> </mrow> </semantics></math> vs. <math display="inline"><semantics> <mrow> <msub> <mi>H</mi> <mrow> <mi>m</mi> <mn>0</mn> </mrow> </msub> </mrow> </semantics></math>, (<b>c</b>) <math display="inline"><semantics> <mrow> <msub> <mi>R</mi> <mrow> <mi>W</mi> <mn>4</mn> </mrow> </msub> </mrow> </semantics></math> vs. <math display="inline"><semantics> <mrow> <msub> <mi>H</mi> <mrow> <mi>m</mi> <mn>0</mn> </mrow> </msub> </mrow> </semantics></math>, (<b>d</b>) <math display="inline"><semantics> <mrow> <msub> <mi>R</mi> <mrow> <mi>W</mi> <mn>5</mn> </mrow> </msub> </mrow> </semantics></math> vs. <math display="inline"><semantics> <mrow> <msub> <mi>H</mi> <mrow> <mi>m</mi> <mn>0</mn> </mrow> </msub> </mrow> </semantics></math>, (<b>e</b>) <math display="inline"><semantics> <mrow> <msub> <mi>R</mi> <mrow> <mi>W</mi> <mn>6</mn> </mrow> </msub> </mrow> </semantics></math> vs. <math display="inline"><semantics> <mrow> <msub> <mi>H</mi> <mrow> <mi>m</mi> <mn>0</mn> </mrow> </msub> </mrow> </semantics></math>, (<b>f</b>) <math display="inline"><semantics> <mrow> <msub> <mi>R</mi> <mrow> <mi>W</mi> <mn>7</mn> </mrow> </msub> </mrow> </semantics></math> vs. <math display="inline"><semantics> <mrow> <msub> <mi>H</mi> <mrow> <mi>m</mi> <mn>0</mn> </mrow> </msub> </mrow> </semantics></math>. The correlation between <math display="inline"><semantics> <mrow> <msub> <mi>R</mi> <mrow> <mi>W</mi> <mo>#</mo> </mrow> </msub> </mrow> </semantics></math> and the wave propagation direction (<math display="inline"><semantics> <mrow> <msub> <mi>D</mi> <mrow> <mi>p</mi> <mo>,</mo> <mi>W</mi> <mn>1</mn> </mrow> </msub> </mrow> </semantics></math> ) observed in <a href="#jmse-10-01537-f008" class="html-fig">Figure 8</a> and <a href="#jmse-10-01537-f009" class="html-fig">Figure 9</a> was not found in the case of the wave height (<math display="inline"><semantics> <mrow> <msub> <mi>H</mi> <mrow> <mi>m</mi> <mn>0</mn> </mrow> </msub> </mrow> </semantics></math> ).</p> Full article ">Figure 12
<p>Comparison of wave conditions (<math display="inline"><semantics> <mrow> <msub> <mi>H</mi> <mrow> <mi>m</mi> <mn>0</mn> </mrow> </msub> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>T</mi> <mi mathvariant="normal">p</mi> </msub> </mrow> </semantics></math>, and <math display="inline"><semantics> <mrow> <msub> <mi>D</mi> <mi>p</mi> </msub> </mrow> </semantics></math> ) between the observational (blue) and modeled (red) data in W1 during the period of the wave event selected for BC from 05:30 on the 26th to 21:30 on 27 April 2009 (39 h). It is noted that the modeled data matched well with the observational data due to the proximity of W1 to the open boundary, as shown in the computational domain (<a href="#jmse-10-01537-f004" class="html-fig">Figure 4</a>b).</p> Full article ">Figure 13
<p>Comparison of wave height (<math display="inline"><semantics> <mrow> <msub> <mi>H</mi> <mrow> <mi>m</mi> <mn>0</mn> </mrow> </msub> </mrow> </semantics></math>) between the observational (blue) and modeled (red) data at the four wave stations inside Pohang New Port (W2, W3, W4, and W5) during the period of the wave event selected for BC from 05:30 on the 26 to 21:30 on 27 April 2009. Reasonable agreements are observed between the observational and modeled data, supporting the model validation.</p> Full article ">Figure 14
<p>Comparison of wave conditions (<math display="inline"><semantics> <mrow> <msub> <mi>H</mi> <mrow> <mi>m</mi> <mn>0</mn> </mrow> </msub> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>T</mi> <mi mathvariant="normal">p</mi> </msub> </mrow> </semantics></math>, and <math display="inline"><semantics> <mrow> <msub> <mi>D</mi> <mi>p</mi> </msub> </mrow> </semantics></math> ) between the observational (blue) and modeled (red) data in W1 during the period of the wave event selected for AC from 07:00 on the 22nd to 21:00 on 23 September 2019 (37 h). It is noted that the modeled data matched well with the observational data due to the proximity of W1 to the open boundary, as shown in the computational domain (<a href="#jmse-10-01537-f004" class="html-fig">Figure 4</a>b).</p> Full article ">Figure 15
<p>Comparison of wave height (<math display="inline"><semantics> <mrow> <msub> <mi>H</mi> <mrow> <mi>m</mi> <mn>0</mn> </mrow> </msub> </mrow> </semantics></math>) between the observational (blue) and modeled (red) data at the four wave stations inside Pohang New Port (W2, W3, W4, and W5) during the period of the wave event selected for AC from 05:00 on the 22 to 21:00 on 23 September 2019. Reasonable agreements are observed between the observational and modeled data, supporting the model validation.</p> Full article ">Figure 16
<p>Contours of wave height isolines at 15:30 on 26 April 2009 when the <math display="inline"><semantics> <mrow> <msub> <mi>H</mi> <mrow> <mi>m</mi> <mn>0</mn> </mrow> </msub> </mrow> </semantics></math> value became maximum during the 39 h of the simulation time shown in <a href="#jmse-10-01537-f012" class="html-fig">Figure 12</a>, in the case of BC. The input <math display="inline"><semantics> <mrow> <msub> <mi>H</mi> <mrow> <mi>m</mi> <mn>0</mn> </mrow> </msub> </mrow> </semantics></math> was 3.35 m at the open boundary, which was reduced to 2.0 m (40% reduction) near the entrance of Pohang New Port (marked in a red rectangle) and to 0.3 m inside the slit near W4 and W5 (marked in a blue rectangle).</p> Full article ">Figure 17
<p>Contours of wave height isolines at 19:00 on 22 September 2019 when <math display="inline"><semantics> <mrow> <msub> <mi>H</mi> <mrow> <mi>m</mi> <mn>0</mn> </mrow> </msub> </mrow> </semantics></math> became maximum during the 39 h of the simulation time shown in <a href="#jmse-10-01537-f016" class="html-fig">Figure 16</a>, in the case of AC. The input <math display="inline"><semantics> <mrow> <msub> <mi>H</mi> <mrow> <mi>m</mi> <mn>0</mn> </mrow> </msub> </mrow> </semantics></math> was 4.84 m at the open boundary, which was reduced to 2.0 m (59% reduction) near the entrance of Pohang New Port (marked in a red rectangle). It is also noted that the location of the 2.0 m <math display="inline"><semantics> <mrow> <msub> <mi>H</mi> <mrow> <mi>m</mi> <mn>0</mn> </mrow> </msub> </mrow> </semantics></math> isoline was slightly outside the port entrance compared to that in BC (<a href="#jmse-10-01537-f016" class="html-fig">Figure 16</a>).</p> Full article ">Figure 18
<p>(<b>a</b>) Contours of wave height isolines in the case of BC. The model conditions were the same with those in <a href="#jmse-10-01537-f016" class="html-fig">Figure 16</a>, except that <math display="inline"><semantics> <mrow> <msub> <mi>D</mi> <mi>p</mi> </msub> </mrow> </semantics></math>, the input wave direction along the outer open boundary, was set as N15.0°E as an example of extreme conditions that represented NNE waves; (<b>b</b>) the same as (<b>a</b>), except that <math display="inline"><semantics> <mrow> <msub> <mi>D</mi> <mi>p</mi> </msub> </mrow> </semantics></math> was set as N55.0°E as for an example of extreme conditions that represented NE waves. In the case of the extreme NE waves (N55.0°E), the waves could enter into the port easily compared to the case of N15.0°E.</p> Full article ">Figure 18 Cont.
<p>(<b>a</b>) Contours of wave height isolines in the case of BC. The model conditions were the same with those in <a href="#jmse-10-01537-f016" class="html-fig">Figure 16</a>, except that <math display="inline"><semantics> <mrow> <msub> <mi>D</mi> <mi>p</mi> </msub> </mrow> </semantics></math>, the input wave direction along the outer open boundary, was set as N15.0°E as an example of extreme conditions that represented NNE waves; (<b>b</b>) the same as (<b>a</b>), except that <math display="inline"><semantics> <mrow> <msub> <mi>D</mi> <mi>p</mi> </msub> </mrow> </semantics></math> was set as N55.0°E as for an example of extreme conditions that represented NE waves. In the case of the extreme NE waves (N55.0°E), the waves could enter into the port easily compared to the case of N15.0°E.</p> Full article ">Figure 19
<p>(<b>a</b>) Contours of wave height isolines in the case of AC. The model conditions were the same with those in <a href="#jmse-10-01537-f017" class="html-fig">Figure 17</a>, except that <math display="inline"><semantics> <mrow> <msub> <mi>D</mi> <mi>p</mi> </msub> </mrow> </semantics></math>, the input wave direction along the outer open boundary, was set as N15.0°E as an example of extreme conditions that represented NNE waves; (<b>b</b>) the same as (<b>a</b>), except that <math display="inline"><semantics> <mrow> <msub> <mi>D</mi> <mi>p</mi> </msub> </mrow> </semantics></math> was set as N55.0°E as an example of extreme conditions that represented NE waves. In the case of the extreme NE waves (N55.0°E), the waves could enter into the port easily compared to the case of N15.0°E. When compared to the results in BC (<a href="#jmse-10-01537-f018" class="html-fig">Figure 18</a>), wave height reduction occurred more effectively inside the port due to the detached breakwater.</p> Full article ">Figure 19 Cont.
<p>(<b>a</b>) Contours of wave height isolines in the case of AC. The model conditions were the same with those in <a href="#jmse-10-01537-f017" class="html-fig">Figure 17</a>, except that <math display="inline"><semantics> <mrow> <msub> <mi>D</mi> <mi>p</mi> </msub> </mrow> </semantics></math>, the input wave direction along the outer open boundary, was set as N15.0°E as an example of extreme conditions that represented NNE waves; (<b>b</b>) the same as (<b>a</b>), except that <math display="inline"><semantics> <mrow> <msub> <mi>D</mi> <mi>p</mi> </msub> </mrow> </semantics></math> was set as N55.0°E as an example of extreme conditions that represented NE waves. In the case of the extreme NE waves (N55.0°E), the waves could enter into the port easily compared to the case of N15.0°E. When compared to the results in BC (<a href="#jmse-10-01537-f018" class="html-fig">Figure 18</a>), wave height reduction occurred more effectively inside the port due to the detached breakwater.</p> Full article ">Figure 20
<p>Comparison of the scatter plots of <math display="inline"><semantics> <mrow> <msub> <mi>R</mi> <mrow> <mi>W</mi> <mn>3</mn> </mrow> </msub> </mrow> </semantics></math> (the wave height reduction rate based on observation data) in terms of <math display="inline"><semantics> <mrow> <msub> <mi>D</mi> <mi>p</mi> </msub> </mrow> </semantics></math> in W1, (<b>a</b>) BC and (<b>b</b>) AC. The red line in each panel shows the linear regression calculated for each data set. The lower <math display="inline"><semantics> <mrow> <msub> <mi>R</mi> <mrow> <mi>W</mi> <mn>3</mn> </mrow> </msub> </mrow> </semantics></math> magnitude in AC indicates that the wave energy was effectively reduced inside the port, confirming the effect of the detached breakwater in securing the tranquility of the port.</p> Full article ">
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