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Volume 10
Issue 12
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J. Mar. Sci. Eng., Volume 10, Issue 12 (December 2022) – 240 articles

Cover Story (view full-size image): The use of modern high-powered internal combustion engines is inevitably associated with the risk of a fire or an explosion. The most important places where such risks occur are during the operation of a low-speed crosshead engine. Based on available sources, the frequency of explosions in the marine engine’s starting air manifolds is determined under real conditions. A cause-and-effect analysis of these explosions is carried out, and their root causes are identified. A probabilistic model of an explosion in the starting air manifold of a marine engine is built. The significance of each basic event is assessed to determine their individual impact on the explosion incident. View this paper
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14 pages, 5516 KiB  
Article
Modeling of Ship DC Power Grid and Research on Secondary Control Strategy
by Hong Zeng, Yuanhao Zhao, Xuming Wang, Taishan He and Jundong Zhang
J. Mar. Sci. Eng. 2022, 10(12), 2037; https://doi.org/10.3390/jmse10122037 - 19 Dec 2022
Cited by 3 | Viewed by 1791
Abstract
Compared to alternating current (AC) grids, direct current (DC) grids are becoming more and more popular. A power distribution approach is suggested in order to solve the issue of uneven power distribution of distributed generation (DG) in a ship DC microgrid. Power control [...] Read more.
Compared to alternating current (AC) grids, direct current (DC) grids are becoming more and more popular. A power distribution approach is suggested in order to solve the issue of uneven power distribution of distributed generation (DG) in a ship DC microgrid. Power control is carried out using a tracking differentiator (TD), while the output power change rate is not greater than the maximum power ramp rate permitted by the battery, and state-of-charge balance is attained quickly. The proposed strategy also reduces the communication pressure on the power grid. A distributed hierarchical control model of a DC microgrid based on a consensus algorithm is created in order to validate the suggested methodology. The simulation results demonstrate that the established model is capable of simulating the DC microgrid accurately, that the states of charge values of the five batteries gradually converge under the adjustment of the secondary strategy, and that the suggested strategy is reasonable and efficient. Full article
(This article belongs to the Section Ocean Engineering)
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Figure 1

Figure 1
<p>Four-phase interleaved parallel bidirectional converter.</p> Full article ">Figure 2
<p>Controller Diagram.</p> Full article ">Figure 3
<p>Physical layer structure diagram of ship DC microgrid.</p> Full article ">Figure 4
<p>Hierarchical control diagram.</p> Full article ">Figure 5
<p>Equivalent model for stability analysis.</p> Full article ">Figure 6
<p>Comparison of convergence rates under different weights. (<b>a</b>) <span class="html-italic">ε</span> = 0.2; (<b>b</b>) <span class="html-italic">ε</span> = 0.3; (<b>c</b>) <span class="html-italic">ε</span> = 0.4; (<b>d</b>) <span class="html-italic">ε</span> = 0.5.</p> Full article ">Figure 7
<p>Network dynamics under different topologies. (<b>a</b>) Line; (<b>b</b>) ring; (<b>c</b>) star; (<b>d</b>) full.</p> Full article ">Figure 8
<p>Simulation without secondary control: (<b>a</b>) output voltages of converters; (<b>b</b>) output currents of converters; (<b>c</b>) DC bus voltage.</p> Full article ">Figure 8 Cont.
<p>Simulation without secondary control: (<b>a</b>) output voltages of converters; (<b>b</b>) output currents of converters; (<b>c</b>) DC bus voltage.</p> Full article ">Figure 9
<p>Simulation under the proposed strategy: (<b>a</b>) state of charge of batteries; (<b>b</b>) output current of converters under secondary control; (<b>c</b>) output voltage of converters under secondary control; (<b>d</b>) DC bus voltage under secondary control.</p> Full article ">Figure 9 Cont.
<p>Simulation under the proposed strategy: (<b>a</b>) state of charge of batteries; (<b>b</b>) output current of converters under secondary control; (<b>c</b>) output voltage of converters under secondary control; (<b>d</b>) DC bus voltage under secondary control.</p> Full article ">Figure A1
<p>Block diagram of decoupling current loop control.</p> Full article ">
31 pages, 26140 KiB  
Article
Comparison of Velocity Obstacle and Artificial Potential Field Methods for Collision Avoidance in Swarm Operation of Unmanned Surface Vehicles
by Hyun-Jae Jo, Su-Rim Kim, Jung-Hyeon Kim and Jong-Yong Park
J. Mar. Sci. Eng. 2022, 10(12), 2036; https://doi.org/10.3390/jmse10122036 - 19 Dec 2022
Cited by 3 | Viewed by 2295
Abstract
As the research concerning unmanned surface vehicles (USVs) intensifies, research on swarm operations is also being actively conducted. A swarm operation imitates the appearance of nature, such as ants, bees, and birds, in forming swarms, moving, and attacking in the search for food. [...] Read more.
As the research concerning unmanned surface vehicles (USVs) intensifies, research on swarm operations is also being actively conducted. A swarm operation imitates the appearance of nature, such as ants, bees, and birds, in forming swarms, moving, and attacking in the search for food. However, several problems are encountered in the USV swarm operation. One of these is the problem of collisions between USVs. A conflict between agents in a swarm can lead to operational failure and property loss. This study attempted to solve this problem. In this study, a virtual matrix approach was applied as a swarm operation. Velocity obstacle (VO) and artificial potential field (APF) methods were used and compared as algorithms for collision avoidance for USVs in a swarm when the formation is changed. For effective collision avoidance, evasive maneuvers should be performed at an appropriate time and location. Therefore, a closest point of approach (CPA)-based method, which considers both temporal and spatial factors, was used. The swarm operation was verified through a large-scale simulation in which 30 USVs changed their formation seven times in 3400 s. When comparing the averages of the distance, error to waypoint, and battery usage, no significant differences were noticed between the VO and APF methods. However, when comparing the cumulative time using the minimum distance, VO was demonstrably safer than APF, and VO completed the formation faster. In conclusion, both the APF and VO methods can evidently perform swarm operations without collisions. Full article
(This article belongs to the Special Issue Control and Stability of Ship Motions)
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Figure 1
<p>Wedge formation with virtual leader vessel, agents, and matrix (Kim et al., 2021 [<a href="#B16-jmse-10-02036" class="html-bibr">16</a>]).</p> Full article ">Figure 2
<p>Conceptual description of command optimization.</p> Full article ">Figure 3
<p>Concept of the APF method.</p> Full article ">Figure 4
<p>Possible designs (2D and 3D) for the control magnitude for the repulsive force.</p> Full article ">Figure 5
<p>APF simulation snapshot at 0, 15, 24, and 30 s.</p> Full article ">Figure 6
<p>Comparison between APF and B-APF expressed in 3D.</p> Full article ">Figure 7
<p>B-APF expressed in 2D.</p> Full article ">Figure 8
<p>B-APF simulation snapshots at 0, 15, 24, and 30 s.</p> Full article ">Figure 9
<p>Setting of collision cone.</p> Full article ">Figure 10
<p>Setting of VO.</p> Full article ">Figure 11
<p>Example of non-collision situation.</p> Full article ">Figure 12
<p>Example of collision situation.</p> Full article ">Figure 13
<p>Conceptual descriptions of <span class="html-italic">CPA</span>, <span class="html-italic">DCPA</span>, and <span class="html-italic">TCPA</span>.</p> Full article ">Figure 14
<p>(<b>a</b>) B-APF swarm simulation snapshots and (<b>b</b>) trajectory.</p> Full article ">Figure 14 Cont.
<p>(<b>a</b>) B-APF swarm simulation snapshots and (<b>b</b>) trajectory.</p> Full article ">Figure 15
<p><span class="html-italic">TCPA</span> and <span class="html-italic">DCPA</span> from agent 1 using B-APF method.</p> Full article ">Figure 16
<p>(<b>a</b>) VO swarm simulation snapshots and (<b>b</b>) trajectory.</p> Full article ">Figure 16 Cont.
<p>(<b>a</b>) VO swarm simulation snapshots and (<b>b</b>) trajectory.</p> Full article ">Figure 17
<p><span class="html-italic">TCPA</span> and <span class="html-italic">DCPA</span> for agent 1 using VO method.</p> Full article ">Figure 18
<p>Mean and standard deviation of minimum distance.</p> Full article ">Figure 19
<p>(<b>a</b>) Snapshots of swarm simulation without collision-avoidance algorithm and (<b>b</b>) trajectory.</p> Full article ">Figure 19 Cont.
<p>(<b>a</b>) Snapshots of swarm simulation without collision-avoidance algorithm and (<b>b</b>) trajectory.</p> Full article ">Figure 20
<p>Comparison of B-APF and VO methods considering the averages of the (<b>a</b>) distance, (<b>b</b>) waypoint error, and (<b>c</b>) battery usage.</p> Full article ">Figure 20 Cont.
<p>Comparison of B-APF and VO methods considering the averages of the (<b>a</b>) distance, (<b>b</b>) waypoint error, and (<b>c</b>) battery usage.</p> Full article ">Figure 21
<p>Cumulative time graph with minimum distance of less than 1 L and 2 L.</p> Full article ">Figure A1
<p>APF simulation snapshots at 0, 15, 22, and 35 s.</p> Full article ">Figure A2
<p>Potential function according to coefficient a.</p> Full article ">Figure A3
<p>Minimum distance according to coefficient a.</p> Full article ">Figure A4
<p>Potential function according to coefficient b.</p> Full article ">Figure A5
<p>Minimum distance according to coefficient b.</p> Full article ">Figure A6
<p>Changes in the <span class="html-italic">TCPA</span> and <span class="html-italic">DCPA</span> of agent 1 during simulation.</p> Full article ">Figure A7
<p>Minimum distance and distance according to the <span class="html-italic">TCPA<sub>max</sub></span>.</p> Full article ">Figure A8
<p>Minimum distance and distance according to the <span class="html-italic">DCPA<sub>min</sub></span>.</p> Full article ">
11 pages, 2303 KiB  
Article
Laboratory Experiments on the Influence of the Wave Spectrum Enhancement Factor on a Rubble Mound Breakwater
by Hosny Bakali, Ismail Aouiche, Najat Serhir, Youssef Zahir, El hassan Ziane, Abderrazak Harti, Zakariae Zerhouni and Edward Anthony
J. Mar. Sci. Eng. 2022, 10(12), 2035; https://doi.org/10.3390/jmse10122035 - 19 Dec 2022
Cited by 1 | Viewed by 1813
Abstract
This paper experimentally explored the influence of the wave spectrum shape variation on breakwater design. The energy spectrum function generally considered for the design of coastal structures is the JONSWAP spectrum. The laboratory results were therefore used to assess the impact of changing [...] Read more.
This paper experimentally explored the influence of the wave spectrum shape variation on breakwater design. The energy spectrum function generally considered for the design of coastal structures is the JONSWAP spectrum. The laboratory results were therefore used to assess the impact of changing the spectrum shape parameter (PEF). We analysed armour stability and wave overtopping in a wave flume with a geometric similarity ratio of 1:30. The experimental results showed that the PEF has maximum influence on overtopping and wave pressures on the crown wall. For a PEF value of 3.3, overtopping was much higher (30% to 100% higher) than with a PEF of 1. Pressure on the crown wall was 20% higher with a PEF of 3.3 in comparison with that for a PEF equal to 1. The stability of the breakwater’s block armour is less sensitive to the PEF variation. Full article
(This article belongs to the Special Issue Measuring, Monitoring and Modeling the Ocean Waves: Possible Combined Uses for Advances and Future Perspectives)
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Figure 1
<p>Photo of the wave flume with the model breakwater in the foreground (<b>a</b>) and components of the breakwater (<b>b</b>) front slope (1), crown wall (2), and back slope (3).</p> Full article ">Figure 2
<p>Schematic view of the wave flume physical model.</p> Full article ">Figure 3
<p>Section of the experimental rubble mound breakwater.</p> Full article ">Figure 4
<p>Location of the pressure sensor to measure the instantaneous variation of wave pressure impact on the crown wall.</p> Full article ">Figure 5
<p>Results of the mean overtopping flow for <span class="html-italic">Hs</span> = 5.5 m (<b>a</b>) and <span class="html-italic">Hs</span> = 6 m (<b>b</b>).</p> Full article ">Figure 6
<p>Maximum pressures of waves with <span class="html-italic">Hs</span> = 6 m for different values of peak periods.</p> Full article ">
17 pages, 1963 KiB  
Article
Fleet Deployment Optimization for LNG Shipping Vessels Considering the Influence of Mixed Factors
by Zhongbin Zhao, Xifu Wang, Hao Wang, Suxin Cheng and Wei Liu
J. Mar. Sci. Eng. 2022, 10(12), 2034; https://doi.org/10.3390/jmse10122034 - 19 Dec 2022
Cited by 8 | Viewed by 3811
Abstract
Driven by China’s booming natural gas consumption market, LNG (Liquified Natural Gas) shipping import has grown rapidly. To facilitate scientific and efficient decision making on LNG shipping fleet deployment and the development of the LNG shipping industry, this article proposes an optimization model [...] Read more.
Driven by China’s booming natural gas consumption market, LNG (Liquified Natural Gas) shipping import has grown rapidly. To facilitate scientific and efficient decision making on LNG shipping fleet deployment and the development of the LNG shipping industry, this article proposes an optimization model to minimize annual fleet operating costs, including voyage cost, running cost, and capital cost. Under the consideration of the mixed factors of self-owned and time charter vessels, epidemic prevention and control, port congestion, transportation time cost, and evaporation loss, as well as navigation security and emergency situations, the validity and optimality of the model are demonstrated by the empirical example and the cost comparison between the conventional and optimized solution. The results show that this optimization model can reduce the total cost by 9.87%. Then, through sensitivity analysis, various significant factors affecting the operating costs of LNG shipping enterprises and their degrees of influence are determined. Based on the analysis of the relevant causes, some actionable countermeasures are recommended, including establishing a shipping price reciprocity mechanism and full chain investment planning, optimizing the inbound link to reduce invalid berthing time, strengthening the construction competitiveness and economy of scale of larger LNG ships, and building a combined dual resource pool transportation mode. This paper contributes to improving transregional maritime energy transport and management capacity, while further enhancing the energy security and development of port cities and their economic hinterlands. Full article
(This article belongs to the Special Issue Marine Industry and Regional Development – Maritime Roadmap Up to the Future)
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Figure 1
<p>Number of LNG ships delivered, scrapped, and in new building from 2011 to 2021.</p> Full article ">Figure 2
<p>System dynamics feedback loop diagram.</p> Full article ">Figure 3
<p>Framework of various cost components.</p> Full article ">
15 pages, 1083 KiB  
Article
Learning-Based Nonlinear Model Predictive Controller for Hydraulic Cylinder Control of Ship Steering System
by Xiaolong Tang, Changjie Wu and Xiaoyan Xu
J. Mar. Sci. Eng. 2022, 10(12), 2033; https://doi.org/10.3390/jmse10122033 - 19 Dec 2022
Cited by 8 | Viewed by 2673
Abstract
The steering mechanism of ship steering gear is generally driven by a hydraulic system. The precise control of the hydraulic cylinder in the steering mechanism can be achieved by the target rudder angle. However, hydraulic systems are often described as nonlinear systems with [...] Read more.
The steering mechanism of ship steering gear is generally driven by a hydraulic system. The precise control of the hydraulic cylinder in the steering mechanism can be achieved by the target rudder angle. However, hydraulic systems are often described as nonlinear systems with uncertainties. Since the system parameters are uncertain and system performances are influenced by disturbances and noises, the robustness cannot be satisfied by approximating the nonlinear theory by a linear theory. In this paper, a learning-based model predictive controller (LB-MPC) is designed for the position control of an electro-hydraulic cylinder system. In order to reduce the influence of uncertainty of the hydraulic system caused by the model mismatch, the Gaussian process (GP) is adopted, and also the real-time input and output data are used to improve the model. A comparative simulation of GP-MPC and MPC is performed assuming that the interference and uncertainty terms are bounded. Consequently, the proposed control strategy can effectively improve the piston position quickly and precisely with multiple constraint conditions. Full article
(This article belongs to the Special Issue Advanced Studies in the Autonomy and Control of Marine Vehicle Systems)
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Figure 1
<p>Electro-hydraulic servo system model diagram.</p> Full article ">Figure 2
<p>Principle diagram of valve-controlled asymmetrical cylinder system.</p> Full article ">Figure 3
<p>The force control loop of hydraulic system based on LB-NMPC. The Gaussian process is used to correct the model mismatch online. Due to the enormous computational complexity it brings, we use a support vector machine to approximate the NMPC control law.</p> Full article ">Figure 4
<p>Gaussian process mean, kernel = Square exponent.</p> Full article ">Figure 5
<p>Gaussian process standard deviation, kernel = Square exponent.</p> Full article ">Figure 6
<p>Gaussian process prediction confidence.</p> Full article ">Figure 7
<p>Position control under different strategies.</p> Full article ">Figure 8
<p>The speed under different control strategies.</p> Full article ">Figure 9
<p>Displacement control error of GP-MPC and MPC.</p> Full article ">Figure 10
<p>Pressure of hydraulic system under different control strategies.</p> Full article ">
18 pages, 5900 KiB  
Article
Wave Transformation behind a Breakwater in Jukbyeon Port, Korea—A Comparison of TOMAWAC and ARTEMIS of the TELEMAC System
by Jong-Dae Do, Sang-Kwon Hyun, Jae-Youll Jin, Byunggil Lee, Weon-Mu Jeong, Kyong-Ho Ryu, Won-Dae Back, Jae-Ho Choi and Yeon S. Chang
J. Mar. Sci. Eng. 2022, 10(12), 2032; https://doi.org/10.3390/jmse10122032 - 19 Dec 2022
Cited by 2 | Viewed by 2050
Abstract
Severe shoreline erosions are commonly observed due to the side effects of breakwaters constructed to protect the habitat. These breakwaters can cause wave energy differences behind the structure due to diffraction, inducing longshore sediment transport and resulting in shoreline changes. Therefore, it is [...] Read more.
Severe shoreline erosions are commonly observed due to the side effects of breakwaters constructed to protect the habitat. These breakwaters can cause wave energy differences behind the structure due to diffraction, inducing longshore sediment transport and resulting in shoreline changes. Therefore, it is essential to correctly simulate the effect of wave transformation in the lee side of structures, but such studies reporting performance of models in the field have been relatively rare. In this study, two wave models of the TELEMAC system were used to investigate the accuracy of modeling the wave transformation effect in a lee area of a breakwater built to secure the harbor’s tranquility, near Jukbyeon Port in Korea, through comparisons with field observations. Two cases of wave conditions with different wave heights and directions were tested. In both cases, the TEL EMAC–ARTEMIS model had lower errors than TELEMAC–TOMAWAC at the onshore wave location, confirming that the phase-resolving ARTEMIS showed better performance in simulating the wave transformation than the phase-averaged TOMAWAC, as expected. However, ARTEMIS had slightly higher errors than TOMAWAC at the offshore location, probably due to the interference by reflected waves from the complex coastlines formed by the different coastal structures. The results also provide various implications learned from the numerical experiments, which can be usefully applied to engineering aspects, such as for the estimation of harbor tranquility. Full article
(This article belongs to the Special Issue Sandy Beach Erosion and Protection: Past, Present and Future)
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Figure 1

Figure 1
<p>(<b>a</b>) Map of the Korea Peninsula and the location of Jukbyeon Port (JP); (<b>b</b>) map of JP.</p> Full article ">Figure 2
<p>Rose diagram: (<b>a</b>) winter (December 2019–February 2020); (<b>b</b>) summer (June–August 2020).</p> Full article ">Figure 3
<p>(<b>a</b>) Aerial photo of the area near the HB and SB shown in <a href="#jmse-10-02032-f001" class="html-fig">Figure 1</a>b, taken on 16 October 2005 and (<b>b</b>) 29 September 2010; (<b>c</b>) Google Earth image of the same site, taken on 3 March 2018.</p> Full article ">Figure 4
<p>The unstructured grid system for TOMAWAC and ARTEMIS: (<b>a</b>) meshes around JP, with the smallest grid size of 5 m; (<b>b</b>) map of the water depths in the models based on bathymetry measurements.</p> Full article ">Figure 5
<p>Time variations of the wave parameters during the field experiment: (<b>a</b>) significant wave height (<math display="inline"><semantics> <mrow> <msub> <mi>H</mi> <mi>s</mi> </msub> </mrow> </semantics></math>) and (<b>b</b>) peak wave direction (<math display="inline"><semantics> <mrow> <msub> <mi>D</mi> <mi>m</mi> </msub> </mrow> </semantics></math> ) calculated from the wave spectra measured at the four wave stations shown in <a href="#jmse-10-02032-f001" class="html-fig">Figure 1</a>b. Black: J1; green: B1; red: B2; blue: B3.</p> Full article ">Figure 6
<p>Example of a pattern of sea surface elevation resulting from the regular wave mode of TELEMAC–ARTEMIS, Case 2.</p> Full article ">Figure 7
<p>Distributions of wave heights modeled by (<b>a</b>) TELEMAC–TOMAWAC and (<b>b</b>) TELEMAC–ARTEMIS, Case 1.</p> Full article ">Figure 8
<p>Distribution of the wave propagating direction, <math display="inline"><semantics> <mrow> <msub> <mi>D</mi> <mi>m</mi> </msub> </mrow> </semantics></math>, modeled by TELEMAC–TOMAWAC, Case 1.</p> Full article ">Figure 9
<p>Distributions of wave heights modeled by (<b>a</b>) TELEMAC–TOMAWAC and (<b>b</b>) TEL EMAC–ARTEMIS, Case 2.</p> Full article ">Figure 10
<p>Distribution of the wave propagating direction, <math display="inline"><semantics> <mrow> <msub> <mi>D</mi> <mi>m</mi> </msub> </mrow> </semantics></math>, modeled by TEL EMAC– TOMAWAC, Case 2.</p> Full article ">Figure 11
<p>Same as <a href="#jmse-10-02032-f009" class="html-fig">Figure 9</a> but at a magnified view for comparison of the wave height distributions near the coastal structures, headland breakwaters, and submerged breakwaters, (<b>a</b>) TELEMAC–TOMAWAC and (<b>b</b>) TEL EMAC–ARTEMIS, Case 2.</p> Full article ">
18 pages, 1897 KiB  
Article
Estimation of LNG Dolphin Capacity: Dolphins of Different Size in Republic of Korea
by Nam Kyu Park and Yohan An
J. Mar. Sci. Eng. 2022, 10(12), 2031; https://doi.org/10.3390/jmse10122031 - 19 Dec 2022
Cited by 1 | Viewed by 1510
Abstract
The LNG terminals are characterized by a large number of ships entering the port during the winter season due to the seasonality of rapidly increasing demand for heating. In winter, there is a shortage of dolphin jetty wharf (dolphins), which increases the waiting [...] Read more.
The LNG terminals are characterized by a large number of ships entering the port during the winter season due to the seasonality of rapidly increasing demand for heating. In winter, there is a shortage of dolphin jetty wharf (dolphins), which increases the waiting rate for ships. Therefore, there is a practical argument that dolphins should be additionally built to solve the ship standby problem. This study proposes the proper LNG handling capacity of a terminal with multiple dolphins of different size. Studies on calculating the LNG handling capacity of LNG terminal dolphins have been proposed by UNCTAD and Ministry of Transport of China (MTC). The formula-based calculation of LNG handling capacity has the advantage of being simple, but it has the disadvantage of not reflecting the actual operation. In this study, the proper LNG handling capacity is measured using a simulation method to overcome the limitations of formula-based calculation for Incheon port in South Korea. In order to check whether the method by simulation is justified, it is compared with the unloading capacity by the calculation formula. This study finds that the proper (or optimal) LNG handling capacity of Incheon port is determined by a dolphin occupancy of 49%, where the dolphin’s profits are maximized. As the results of simulation model, the proper (or optimal) loading capacity is 38.5 million m3 when dolphin occupancy is 49%. The capacity of individual dolphin is estimated at 17.0 million m3 for 70,000 DWT dolphin and 21.2 million m3 for 120,000 DWT dolphin, respectively. The main points of this study to use simulation model are as follows: First, the number of non-working days should be considered. Second, the optimal dolphin occupancy should be determined by finding the maximum profit point of using the pier. Third, if the size of the dolphin is different, an appropriate simulation will be implemented. Fourth, the data of the peak season should be analyzed. Finally, it should be checked whether the ship waiting rate is acceptable level or not. Full article
(This article belongs to the Special Issue Advances in Maritime Economics and Logistics)
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<p>Simulation Model for LNG Dolphin.</p> Full article ">Figure 2
<p>Distribution of ship arrival time interval of Incheon terminal.</p> Full article ">Figure 3
<p>Distribution of LNG volume of calling ships.</p> Full article ">Figure 4
<p>Regression analysis of cargo volume and full rate time.</p> Full article ">Figure 5
<p>Regression analysis of cargo volume and preparation time.</p> Full article ">Figure 6
<p>Dolphin occupancy and ship waiting ratio.</p> Full article ">Figure 7
<p>Dolphin profit and occupancy with USD 40,000 demurrage per day.</p> Full article ">Figure 8
<p>Dolphin profit and occupancy with USD 20,000 demurrage per day.</p> Full article ">
29 pages, 12520 KiB  
Article
On the Development of a Mid-Depth Lagrangian Float for Littoral Deployment
by Yuri Katz and Morel Groper
J. Mar. Sci. Eng. 2022, 10(12), 2030; https://doi.org/10.3390/jmse10122030 - 19 Dec 2022
Cited by 1 | Viewed by 2111
Abstract
This study presents the complete, detailed development process of an enhanced one-man portable Lagrangian float designed for littoral deployment to depths of up to 300 m. The design focused on maximization of the Lagrangian characteristics of the hull, minimization of the noise emission [...] Read more.
This study presents the complete, detailed development process of an enhanced one-man portable Lagrangian float designed for littoral deployment to depths of up to 300 m. The design focused on maximization of the Lagrangian characteristics of the hull, minimization of the noise emission and energy efficiency of the propulsion system, and the versatility of the platform for various scientific missions. The platform is propelled by a variable buoyancy engine that is actuated by an oil-submerged, gas-pressure assisted micro gear pump. The pressure assistance lowers the pressure differential across the pump ports at depth, resulting in quieter and more efficient operation. An enhanced proportional–integral–differential control scheme is employed to pilot the platform. To enhance diving safety, a software safety agent was incorporated. If the software safety agent detects a major failure, a drop weight is released. To eliminate the chance of water ingress through dynamic hull penetration, the drop weight is actuated by an in-house developed magnetic coupling mechanism. An onboard installed hydrophone continuously records and monitors ambient sounds for phenomena of interest and enables commands and mission updates from the surface. For surface recovery, the platform is equipped with GPS and an Iridium beacon for long-range localization, and an RF beacon and strobe for short-range localization and as a backup. The performance of the platform is demonstrated in a simulation and in an actual real sea mission conducted in the eastern Mediterranean at a depth of 10 and 12 m. Full article
(This article belongs to the Section Ocean Engineering)
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Figure 1
<p>Float design spiral.</p> Full article ">Figure 2
<p>Hardware architecture.</p> Full article ">Figure 3
<p>Drop-weight mechanism (upside down for clarity).</p> Full article ">Figure 4
<p>VBE model (cross-section).</p> Full article ">Figure 5
<p>Float general arrangement: (<b>a</b>) CAD model (section); (<b>b</b>) bottom endcap assembly.</p> Full article ">Figure 6
<p>Simple cylinder–linear buckling FEA: loads, boundary conditions, mesh and mesh details. (Red arrows indicate loads, green arrows indicate applied fixtures).</p> Full article ">Figure 7
<p>Simple cylinder–Linear buckling FEA: analysis results.</p> Full article ">Figure 8
<p>Simple cylinder–nonlinear buckling FEA: (<b>a</b>) mesh and mesh details; (<b>b</b>) analysis results.</p> Full article ">Figure 9
<p>Stiffened cylinder–Linear buckling: (<b>a</b>) mesh and parameters; (<b>b</b>) analysis results.</p> Full article ">Figure 10
<p>Stiffened cylinder–Nonlinear buckling: (<b>a</b>) mesh and parameters; (<b>b</b>) results.</p> Full article ">Figure 11
<p>Stiffened cylinder compressibility FEA: (<b>a</b>) mesh, loads, boundary conditions and mesh details; (<b>b</b>) static analysis total displacement; (<b>c</b>) deformed body shape employed in volume computation.</p> Full article ">Figure 12
<p>Float hull geometry and main dimensions.</p> Full article ">Figure 13
<p>Simulink motion simulation results.</p> Full article ">Figure 14
<p>Simulink power simulation #1: no pressure assistance; HPA minimum pressure equal 0 bar gauge.</p> Full article ">Figure 15
<p>Simulink power simulation #2: pressure assistance; HPA minimum pressure equal 10 bar gauge.</p> Full article ">Figure 16
<p>Pi state machine (simplified).</p> Full article ">Figure 17
<p>Mega state machine (simplified).</p> Full article ">Figure 18
<p>(<b>a</b>) float lowered into the pool; (<b>b</b>) on the surface as part of ballasting; (<b>c</b>) in front of the pressure chamber; (<b>d</b>) in the pressure chamber with cable leading to a PC.</p> Full article ">Figure 19
<p>Sea trial deployment #1: (<b>a</b>) hydrostatic pressure reading; (<b>b</b>) bladder volume.</p> Full article ">Figure 20
<p>Sea trial deployment #2: (<b>a</b>) hydrostatic pressure reading; (<b>b</b>) bladder volume.</p> Full article ">Figure 21
<p>Sea trial: (<b>a</b>) during first deployment; (<b>b</b>) surfacing after first deployment.</p> Full article ">
11 pages, 863 KiB  
Article
Analytical Description of an Axisymmetric Supercavitation Bubble in a Viscous Flow
by Lotan Arad Ludar and Alon Gany
J. Mar. Sci. Eng. 2022, 10(12), 2029; https://doi.org/10.3390/jmse10122029 - 19 Dec 2022
Cited by 2 | Viewed by 1635
Abstract
One of the basic elements which characterizes flow regimes, is viscosity. This element has typically been neglected in research on supercavitational flows, describing and predicting supercavitation bubbles geometry and formation using non-viscous potential flows. Arguing that the viscosity effect is much smaller than [...] Read more.
One of the basic elements which characterizes flow regimes, is viscosity. This element has typically been neglected in research on supercavitational flows, describing and predicting supercavitation bubbles geometry and formation using non-viscous potential flows. Arguing that the viscosity effect is much smaller than the inertial effect at high flow speeds, the viscosity has been ignored and the only parameter for modeling the flow has been the cavitation number. However, for some situations and conditions, the viscosity was found to be significant and crucial for the bubble geometry and formation, especially at the supercavitation bubble detachment point, hence some investigations based on numerical calculations have taken viscosity into account. This paper presents an analytical model of an axisymmetric supercavitation bubble in a viscous flow according to Serebryakov annular model for calculation of axisymmetric cavity flows. Viscosity effect on the bubble geometry is suggested, and an analysis for validation and examination is presented as well. The results show the change of the bubble formation from past models due to the viscosity, and offer a more accurate description of the bubble geometry close to the detachment point. Moreover, the slenderness parameter is calculated and presented for supercavitation bubbles in a viscous flow together with its dependency on Reynolds number and the cavitation number. The analysis reveals that the slenderness parameter increases with increasing both the cavitation number and Reynolds number, where the latter has a substantial effect. Full article
(This article belongs to the Special Issue Verification and Validation Analysis on Marine Applications)
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<p>A scheme of the physical problem.</p> Full article ">Figure 2
<p>A scheme of the bubble dimensions change.</p> Full article ">Figure 3
<p>The slenderness parameter vs. the cavitation number for different Reynolds numbers.</p> Full article ">
15 pages, 950 KiB  
Article
Daily Rhythmicity of Hepatic Rhythm, Lipid Metabolism and Immune Gene Expression of Mackerel Tuna (Euthynnus affinis) under Different Weather
by Wenwen Wang, Jing Hu, Zhengyi Fu, Gang Yu and Zhenhua Ma
J. Mar. Sci. Eng. 2022, 10(12), 2028; https://doi.org/10.3390/jmse10122028 - 19 Dec 2022
Cited by 2 | Viewed by 2246
Abstract
In order to investigate the rhythmic changes in gene expression in the liver of mackerel tuna (Euthynnus affinis) under sunny and cloudy conditions, this experiment had four sampling times (6:00, 12:00, 18:00 and 24:00) set on sunny and cloudy days to [...] Read more.
In order to investigate the rhythmic changes in gene expression in the liver of mackerel tuna (Euthynnus affinis) under sunny and cloudy conditions, this experiment had four sampling times (6:00, 12:00, 18:00 and 24:00) set on sunny and cloudy days to determine the expression of their immune, metabolic and rhythmic genes. The results showed that daily rhythmicity was present within most of the rhythm genes (CREB1, CLOCK, PER1, PER2, PER3, REVERBA, CRY2 and BMAL1), metabolic genes (SIRT1 and SREBP1) and immune genes (NF-kB1, MHC-I, ALT, IFNA3, ISY1, ARHGEF13, GCLM and GCLC) in this study under the sunny and cloudy condition (p < 0.05). The expression levels of CREB1, PER1, PER3, RORA, REVERBA, CRY1 and BMAL1 within rhythm genes were significantly different (p < 0.05) in the same time point comparison between sunny and cloudy conditions at 6:00, 12:00, 18:00 and 24:00; metabolic genes had the expression levels of LPL at 6:00, 12:00, 18:00 and 24:00 in the same time point comparison (p < 0.05); immune genes only had significant differences in the expression levels of IFNA3 at 6:00, 12:00, 18:00 and 24:00 (p < 0.05). This study has shown that rhythm, lipid metabolism and immune genes in the livers of mackerel tuna are affected by time and weather and show significant changes in expression. Full article
(This article belongs to the Special Issue New Techniques in Marine Aquaculture)
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<p>Expression of liver rhythm genes during 24 h in different weather in mackerel tuna. (<b>a</b>): <span class="html-italic">CREB1</span>; (<b>b</b>): <span class="html-italic">CLOCK</span>; (<b>c</b>): <span class="html-italic">RORA</span>; (<b>d</b>): <span class="html-italic">PER1</span>; (<b>e</b>): <span class="html-italic">PER2</span>; (<b>f</b>): <span class="html-italic">PER3</span>; (<b>g</b>): <span class="html-italic">CRY1</span>; (<b>h</b>): <span class="html-italic">CRY2</span>; (<b>i</b>): <span class="html-italic">REVERBA</span>; (<b>j</b>): <span class="html-italic">BMAL1</span>. Red in each graph represents sunny days, and blue represents cloudy days. The presence of different letters indicates significance by ANOVA and Tukey’s tests (<span class="html-italic">p</span> &lt; 0.05). * represents significant differences at the same time point (<span class="html-italic">p</span> &lt; 0.05). Differences between those with different lowercase letters indicate significance (<span class="html-italic">p</span> &lt; 0.05), while the opposite difference is not significant (<span class="html-italic">p</span> &gt; 0.05); the same for the latter figure.</p> Full article ">Figure 2
<p>Expression of hepatic metabolic genes during 24 h in different weather in mackerel tuna. (<b>a</b>): <span class="html-italic">SIRT1</span>; (<b>b</b>): <span class="html-italic">GST</span>; (<b>c</b>): <span class="html-italic">LPL</span>; (<b>d</b>): <span class="html-italic">SREBP1</span>. Red in each graph represents sunny days, and blue represents cloudy days. The presence of different letters indicates significance by ANOVA and Tukey’s tests (<span class="html-italic">p</span> &lt; 0.05). * represents significant differences at the same time point (<span class="html-italic">p</span> &lt; 0.05).</p> Full article ">Figure 3
<p>Expression of liver immune genes in different weather conditions in mackerel tuna over a 24 h period. (<b>a</b>): <span class="html-italic">TRIM35</span>; (<b>b</b>): <span class="html-italic">NF-kB1</span>; (<b>c</b>): <span class="html-italic">MHC-I</span>; (<b>d</b>): <span class="html-italic">ALT</span>; (<b>e</b>): <span class="html-italic">IFNA3</span>; (<b>f</b>): <span class="html-italic">ISY1</span>; (<b>g</b>): <span class="html-italic">ARHGEF13</span>; (<b>h</b>): <span class="html-italic">GCLM</span>; (<b>i</b>): <span class="html-italic">GCLC</span>. Red in each graph represents sunny days, and blue represents cloudy days. The presence of different letters indicates significance by ANOVA and Tukey’s tests (<span class="html-italic">p</span> &lt; 0.05). * represents significant differences at the same time point (<span class="html-italic">p</span> &lt; 0.05).</p> Full article ">
3 pages, 189 KiB  
Editorial
Geological Oceanography: Towards a Conceptual Framework
by George Kontakiotis, Assimina Antonarakou and Dmitry A. Ruban
J. Mar. Sci. Eng. 2022, 10(12), 2027; https://doi.org/10.3390/jmse10122027 - 19 Dec 2022
Cited by 1 | Viewed by 1840
Abstract
Research into modern oceans, seas, and their coastal zones, as well as marine ecosystems, provides valuable information for deciphering the geological dynamics [...] Full article
(This article belongs to the Special Issue Recent Advances in Geological Oceanography)
15 pages, 3028 KiB  
Article
Double Broad Reinforcement Learning Based on Hindsight Experience Replay for Collision Avoidance of Unmanned Surface Vehicles
by Jiabao Yu, Jiawei Chen, Ying Chen, Zhiguo Zhou and Junwei Duan
J. Mar. Sci. Eng. 2022, 10(12), 2026; https://doi.org/10.3390/jmse10122026 - 18 Dec 2022
Cited by 1 | Viewed by 2248
Abstract
Although broad reinforcement learning (BRL) provides a more intelligent autonomous decision-making method for the collision avoidance problem of unmanned surface vehicles (USVs), the algorithm still has the problem of over-estimation and has difficulty converging quickly due to the sparse reward problem in a [...] Read more.
Although broad reinforcement learning (BRL) provides a more intelligent autonomous decision-making method for the collision avoidance problem of unmanned surface vehicles (USVs), the algorithm still has the problem of over-estimation and has difficulty converging quickly due to the sparse reward problem in a large area of sea. To overcome the dilemma, we propose a double broad reinforcement learning based on hindsight experience replay (DBRL-HER) for the collision avoidance system of USVs to improve the efficiency and accuracy of decision-making. The algorithm decouples the two steps of target action selection and target Q value calculation to form the double broad reinforcement learning method and then adopts hindsight experience replay to allow the agent to learn from the experience of failure in order to greatly improve the sample utilization efficiency. Through training in a grid environment, the collision avoidance success rate of the proposed algorithm was found to be 31.9 percentage points higher than that in the deep Q network (DQN) and 24.4 percentage points higher than that in BRL. A Unity 3D simulation platform with high fidelity was also designed to simulate the movement of USVs. An experiment on the platform fully verified the effectiveness of the proposed algorithm. Full article
(This article belongs to the Special Issue Ship Collision Risk Assessment)
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<p>Broad Learning System Structure.</p> Full article ">Figure 2
<p>Structure of the Broad Learning System with Incremental Learning.</p> Full article ">Figure 3
<p>DBRL-HER algorithm structure diagram.</p> Full article ">Figure 4
<p>10 × 10 original map.</p> Full article ">Figure 5
<p>20 × 20 original map.</p> Full article ">Figure 6
<p>Comparison of parameter performance curves.</p> Full article ">Figure 7
<p>Collision avoidance success rate curves under 10 × 10 map.</p> Full article ">Figure 8
<p>Collision avoidance success rate curves under 20 × 20 map.</p> Full article ">Figure 9
<p>High fidelity water environment.</p> Full article ">Figure 10
<p>USV model.</p> Full article ">Figure 11
<p>Platform training result.</p> Full article ">
25 pages, 6765 KiB  
Article
Real-Time Emergency Collision Avoidance for Unmanned Surface Vehicles with COLREGS Flexibly Obeyed
by Yang Qu and Lilong Cai
J. Mar. Sci. Eng. 2022, 10(12), 2025; https://doi.org/10.3390/jmse10122025 - 18 Dec 2022
Cited by 2 | Viewed by 1994
Abstract
This paper presents a real-time emergency collision-avoidance method for unmanned surface vehicles (USVs) with the International Regulations for Preventing Collisions at Sea (COLREGS) flexibly obeyed. The pivotal issue is that some traffic vessels may violate the demands of this convention, which would increase [...] Read more.
This paper presents a real-time emergency collision-avoidance method for unmanned surface vehicles (USVs) with the International Regulations for Preventing Collisions at Sea (COLREGS) flexibly obeyed. The pivotal issue is that some traffic vessels may violate the demands of this convention, which would increase the risk of collision if the USV blindly obeys the COLREGS rules. To avoid mandatory compliance with these COLREGS rules, a real-time truncated velocity obstacle (TVO) algorithm is proposed to assign a collision-free velocity vector for the control system to realize. Considering a reasonable trade-off between safety and the COLREGS rules, the proposed collision-avoidance method expands the TVO’s area based on the velocity uncertainties of traffic vessels, which greatly enhance the safety of collision-avoidance operations and encourage the USV to follow the COLREGS rules. To promptly realize an assigned collision-free velocity, this paper also develops a discrete simultaneous planning and executing (SPAE) controller design. The proposed discrete controller is divided into three parts: online polynomial planning to satisfy the constraints of tracking errors, an accurate uncertainty estimation, and an algebraic control law to promptly execute the planned polynomial. Numerical results have validated the reliability and intelligibility of the proposed collision-avoidance method. Furthermore, simulated and experimental results have validated the effectiveness of the proposed controller design. Full article
(This article belongs to the Special Issue Advances in Marine Vehicles, Automation and Robotics)
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<p>The configuration of a USV (vehicle A) and a moving vehicle (vehicle B). Their configuration radii are denoted by <math display="inline"><semantics> <msub> <mi>r</mi> <mi>R</mi> </msub> </semantics></math> and <math display="inline"><semantics> <msub> <mi>r</mi> <mi>o</mi> </msub> </semantics></math>, respectively.</p> Full article ">Figure 2
<p>The velocity obstacle <math display="inline"><semantics> <mrow> <mi>T</mi> <mi>V</mi> <msubsup> <mi>O</mi> <mrow> <mi>A</mi> <mo>|</mo> <mi>B</mi> </mrow> <mi>κ</mi> </msubsup> </mrow> </semantics></math> (shaded area) is geometrically represented as a truncated cone with its apex at the origin of the velocity-space coordinate system. The center line of this truncated cone passes through two circle centers, i.e., <math display="inline"><semantics> <mrow> <msub> <mi>P</mi> <mi>B</mi> </msub> <mo>−</mo> <msub> <mi>P</mi> <mi>A</mi> </msub> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mo>(</mo> <msub> <mi>P</mi> <mi>B</mi> </msub> <mo>−</mo> <msub> <mi>P</mi> <mi>A</mi> </msub> <mo>)</mo> <mo>/</mo> <mi>κ</mi> </mrow> </semantics></math>. Parameter <math display="inline"><semantics> <mrow> <mi>κ</mi> <mo>&gt;</mo> <mn>0</mn> </mrow> </semantics></math> is a preset collision-free time period, which determines the amount of truncation.</p> Full article ">Figure 3
<p>(<b>a</b>) The approximation of velocity obstacle <math display="inline"><semantics> <mrow> <mi>T</mi> <mi>V</mi> <msubsup> <mi>O</mi> <mrow> <mi>A</mi> <mo>|</mo> <mi>B</mi> </mrow> <mi>κ</mi> </msubsup> </mrow> </semantics></math> uses a line to replace the arc, which can simplify the collision-free solutions. The cone’s apex at the origin indicates that the collision-avoidance solution is the relative velocity <math display="inline"><semantics> <msub> <mi>v</mi> <mrow> <mi>A</mi> <mo>|</mo> <mi>B</mi> </mrow> </msub> </semantics></math>. (<b>b</b>) The cone’s apex of VTO has been shifted from origin <span class="html-italic">O</span> to the tip of velocity <math display="inline"><semantics> <msub> <mi>v</mi> <mi>o</mi> </msub> </semantics></math>. In this case, the USV velocity <math display="inline"><semantics> <msub> <mi>v</mi> <mi>R</mi> </msub> </semantics></math> will be the collision-avoidance solution.</p> Full article ">Figure 4
<p>Avoidance regulations for marine surface vehicles, where Cases I and II obey the COLREGS. Cases III and IV show the hybrid situations. It is worth noting that Cases I and II can be handled by a direction’s expansion of the moving ship speed, see Equation (<a href="#FD14-jmse-10-02025" class="html-disp-formula">14</a>). Although the COLREGS rules require that the own ship should stand on its course when it is on the starboard of the target ship, the practical problem is that some commercial ships may not obey the COLREGS in Case IV since USVs usually have small vehicle size and are difficult for other ships to detect. Hence, the USV can violate the COLREGS unless it is safe enough to stand on its course. Notice that Case III and IV can be handled by the magnitude expansion of the moving ship speed; see Equation (<a href="#FD12-jmse-10-02025" class="html-disp-formula">12</a>).</p> Full article ">Figure 5
<p>Collision-free graphical representation to avoid the velocity obstacle <math display="inline"><semantics> <mrow> <mi>T</mi> <mi>V</mi> <msubsup> <mi>O</mi> <mrow> <mi>A</mi> <mo>|</mo> <mi>B</mi> </mrow> <mi>κ</mi> </msubsup> <mo>⊕</mo> <msub> <mi>V</mi> <mi>B</mi> </msub> </mrow> </semantics></math> that considers the velocity expansion <math display="inline"><semantics> <msub> <mi>V</mi> <mi>B</mi> </msub> </semantics></math> of the moving vehicle.</p> Full article ">Figure 6
<p>USV heading assignment based on a LOS guidance system.</p> Full article ">Figure 7
<p>The proposed collision-avoidance flow diagram considering the COLREGS regulations.</p> Full article ">Figure 8
<p>Passive head-on and overtaking scenarios.</p> Full article ">Figure 9
<p>Passive crossing scenario when the USV has the moving vehicle on its own starboard.</p> Full article ">Figure 10
<p>Passive crossing scenario when the USV has the moving vehicle on its own port side.</p> Full article ">Figure 11
<p>Passive collision avoidance when the USV encounters multiple vehicles.</p> Full article ">Figure 12
<p>Passive collision avoidance among multiple vehicles with closed-loop trajectories.</p> Full article ">Figure 13
<p>Active crossing scenario when each one has other vehicles on its port, starboard, and head-on sides.</p> Full article ">Figure 14
<p>Active collision avoidance with multiple vehicles.</p> Full article ">Figure 15
<p>The underactuated USV prototype used for the experimental validations of the proposed SPAE controller. Particularly, the lower six figures are the selected photographs of a passive crossing scenario from the beginning to the end.</p> Full article ">Figure 16
<p>Simulated and experimental collision-avoidance motions, the corresponding yaw-track heading errors, and the surge-track velocity errors.</p> Full article ">Figure 17
<p>Proposed simulated and experimental yaw control executions to track the collision-free yaw heading.</p> Full article ">Figure 18
<p>Simulated and experimental north-east state estimations based on the position measurements.</p> Full article ">Figure 19
<p>Proposed simulated and experimental surge control executions to track the collision-free surge velocity.</p> Full article ">
24 pages, 4172 KiB  
Review
Advances in the Technologies for Marine Salinity Measurement
by Lijuan Gu, Xiangge He, Min Zhang and Hailong Lu
J. Mar. Sci. Eng. 2022, 10(12), 2024; https://doi.org/10.3390/jmse10122024 - 18 Dec 2022
Cited by 15 | Viewed by 4311
Abstract
As one of the most important physical parameters of seawater, salinity is essential to study climatological change, to trace seawater masses and to model ocean dynamics. The traditional way to conduct salinity measurement in hydrographical observation is to use a standard conductivity-temperature-depth (CTD) [...] Read more.
As one of the most important physical parameters of seawater, salinity is essential to study climatological change, to trace seawater masses and to model ocean dynamics. The traditional way to conduct salinity measurement in hydrographical observation is to use a standard conductivity-temperature-depth (CTD) probe where the salinity determination is based on a measurement of electrical conductivity. This article describes some developments of recent years that could lead to a new generation of instruments for the determination of salinity in seawater. Salinity determination with optical salinity sensor based on the refractive index measurement have been extensively studied. Different ways to conduct refractive index measurements are summarized, including measurements based on beam deviation, light wave mode coupling and swelling of surface coating material, among which the optical fiber sensors are promising candidates for further commercialization. Complementary to the above-mentioned direct measurement salinity point sensors, seismic observation takes advantages of large scale multichannel seismic data to retrieve the ocean salinity with high lateral resolution of ∼10 m. This work provide comprehensive information in the techniques related to the marine salinity measurement. Full article
(This article belongs to the Section Physical Oceanography)
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<p>The relation of salinity and conductivity, temperature and pressure. (<b>a</b>) Pressure slice planes. (<b>b</b>) Conductivity slice planes. (<b>c</b>) Temperature slice planes. (<b>d</b>) The salinity versus conductivity under different temperature and pressure. (<b>e</b>) The salinity versus temperature under different conductivity and pressure. (<b>f</b>) The salinity versus pressure under different conductivity and temperature.</p> Full article ">Figure 2
<p>The micro CTD system for measurement of ocean water salinity. (<b>a</b>) The chip layout. (<b>b</b>) Photograph of finished chip. (<b>c</b>) Measured trans-impedance as a function of frequency for five different salinities (2, 4, 8, 16 and 32 psu). Reprinted from Publication [<a href="#B6-jmse-10-02024" class="html-bibr">6</a>], Copyright (2022), with permission from Elsevier.</p> Full article ">Figure 3
<p>The relation of refractive index and salinity, temperature and wavelength. (<b>a</b>) Temperature slice planes. (<b>b</b>) Salinity slice planes. (<b>c</b>) Wavelength slice planes. (<b>d</b>) The refractive index versus salinity under different temperature and wavelength. (<b>e</b>) The refractive index versus temperature under different salinity and wavelength. (<b>f</b>) The refractive index versus wavelength under different salinity and temperature.</p> Full article ">Figure 4
<p>Principle of salinity measurement based on beam deviation.</p> Full article ">Figure 5
<p>The schematic diagram of salinity measurement based on light intensity detection.</p> Full article ">Figure 6
<p>The schematic diagram of salinity measurement based on wavelength detection.</p> Full article ">Figure 7
<p>Demonstration of seismic data collection.</p> Full article ">Figure 8
<p>The workflow of FWI, reprinted with permission from Refs. [<a href="#B64-jmse-10-02024" class="html-bibr">64</a>,<a href="#B73-jmse-10-02024" class="html-bibr">73</a>]. Copyright (2022), with permission from Wiley and Nature.</p> Full article ">Figure 9
<p>Typical workflow of the deconvolution inversion method.</p> Full article ">Figure 10
<p>Typical workflow of the geostatistical seismic inversion method, <span class="html-italic">C</span> represents the global coefficient between the real and synthetic seismic data and <math display="inline"><semantics> <msub> <mi>C</mi> <mrow> <mi>t</mi> <mi>h</mi> </mrow> </msub> </semantics></math> is the threshold of the global coefficient. Adapted with permission from Refs. [<a href="#B87-jmse-10-02024" class="html-bibr">87</a>,<a href="#B89-jmse-10-02024" class="html-bibr">89</a>]. Copyright (2022), with permission from Springer and Frontiers.</p> Full article ">
26 pages, 7256 KiB  
Article
Dynamic Analysis and Extreme Response Evaluation of Lifting Operation of the Offshore Wind Turbine Jacket Foundation Using a Floating Crane Vessel
by Mingsheng Chen, Guibo Yuan, Chun Bao Li, Xianxiong Zhang and Lin Li
J. Mar. Sci. Eng. 2022, 10(12), 2023; https://doi.org/10.3390/jmse10122023 - 18 Dec 2022
Cited by 13 | Viewed by 3224
Abstract
The jacket is the most widely-used fixed foundation for offshore wind turbines due to its superior strength and low installation cost in relatively deep waters. Floating crane vessels are commonly used to install jacket foundations. However, the dynamic coupling between the jacket and [...] Read more.
The jacket is the most widely-used fixed foundation for offshore wind turbines due to its superior strength and low installation cost in relatively deep waters. Floating crane vessels are commonly used to install jacket foundations. However, the dynamic coupling between the jacket and the floating vessel might generate complex dynamic responses under wave action. The complexity of the multi-body system requires comprehensive time-domain simulations and statistical analysis to obtain reliable results, especially for the evaluation of the operational safety of offshore lift installations of a jacket foundation. In this context, this study performs numerical simulations and statistical analyses to predict the extreme responses and the preliminary allowable sea states for guiding the lowering operation of a jacket using a floating crane vessel. First, ANSYS-AQWA is used to obtain the hydrodynamic coefficients of the vessel in the frequency domain. A nonstationary time-domain simulation of jacket lowering with winches is performed to identify several preliminary critical vertical positions of the jacket from the time series in an irregular wave. The extreme responses of a target probability are evaluated by the extreme distribution model after a large number of steady-state time-domain simulations of the critical vertical positions in irregular waves. The most critical vertical position is determined from three preliminary critical vertical positions by comparing the extreme responses. Eigenvalue analysis and spectrum analysis of the most critical vertical position of the jacket are carried out to find the natural periods of the system and the dynamic coupling characteristics between different components. The influence of wave direction, significant wave height, and spectrum peak period on the dynamic responses are also analyzed in the most critical vertical position. Furthermore, the optimal wave direction is determined as the head sea. Preliminary allowable sea states are derived by comparing the calculated dynamic amplification coefficient with the defined operational criteria. Full article
(This article belongs to the Topic Wind, Wave and Tidal Energy Technologies in China)
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<p>Simulation of wire in AQWA.</p> Full article ">Figure 2
<p>General sketch of the lowering system in an arbitrary location.</p> Full article ">Figure 3
<p>Numerical model of the lowering system of the jacket in AQWA (top view).</p> Full article ">Figure 4
<p>Top view of the hydrodynamic model and the incidence angle of waves.</p> Full article ">Figure 5
<p>First-order wave excitation force RAOs. (<b>a</b>) Surge; (<b>b</b>) Heave; (<b>c</b>) Roll; (<b>d</b>) Pitch.</p> Full article ">Figure 6
<p>Motion RAOs. (<b>a</b>) Surge; (<b>b</b>) Heave; (<b>c</b>) Roll; (<b>d</b>) Pitch.</p> Full article ">Figure 7
<p>Comparison of the time-domain motions and the RAO-based responses. (<b>a</b>) Comparison of the heave motions; (<b>b</b>) Comparison of the pitch motions.</p> Full article ">Figure 8
<p>Time-domain simulation model and lifting system arrangement.</p> Full article ">Figure 9
<p>Response time series of the entire lowering process. (<span class="html-italic">H<sub>s</sub></span> = 1.5 m, <span class="html-italic">T<sub>p</sub></span> = 7 s, Dir = 45 deg, Nonstationary).</p> Full article ">Figure 10
<p>Vertical positions of the jacket. (<b>a</b>) JL1-Z1; (<b>b</b>) JL1-Z2; (<b>c</b>) JL1-Z3; (<b>d</b>) JL1-Z4.</p> Full article ">Figure 11
<p>Convergence test results. (<span class="html-italic">H<sub>s</sub></span> = 1.5 m, <span class="html-italic">T<sub>p</sub></span> = 7 s, Dir = 45 deg, JL1-Z3).</p> Full article ">Figure 12
<p>Fitted Gumbel distributions of extreme responses using 50 seeds. (<span class="html-italic">H<sub>s</sub></span>=1.5 m, <span class="html-italic">T<sub>p</sub></span>=7 s, Dir=45 deg, JL1-Z3). (<b>a</b>) Jacket leg1 X motion; (<b>b</b>) Jacket pitch motion; (<b>c</b>) Wire1 tension; (<b>d</b>) Sling1 tension.</p> Full article ">Figure 13
<p>Response time series and PSD of vessel motion (<span class="html-italic">H<sub>s</sub></span> = 1.5 m, <span class="html-italic">T<sub>p</sub></span> = 7 s, Dir = 45 deg, JL1-Z3). (<b>a</b>) Time series of vessel heave; (<b>b</b>) Time series of vessel pitch; (<b>c</b>) Time series of vessel roll; (<b>d</b>) PSD of vessel heave; (<b>e</b>) PSD of vessel pitch; (<b>f</b>) PSD of vessel roll.</p> Full article ">Figure 14
<p>Response time series and PSD of jacket motion (<span class="html-italic">H<sub>s</sub></span>=1.5 m, <span class="html-italic">T<sub>p</sub></span>=7 s, Dir=45 deg, JL1-Z3). (<b>a</b>) Time series of jacket heave; (<b>b</b>) Time series of jacket pitch; (<b>c</b>) Time series of jacket surge; (<b>d</b>) PSD of jacket heave; (<b>e</b>) PSD of jacket pitch; (<b>f</b>) PSD of jacket surge.</p> Full article ">Figure 15
<p>Response time series and PSD of tension of the Sling1 and Wire1 (<span class="html-italic">H<sub>s</sub></span> = 1.5 m, <span class="html-italic">T<sub>p</sub></span> = 7 s, Dir = 45 deg, JL1-Z3). (<b>a</b>) Time series of wire1 tension; (<b>b</b>) Time series of sling1 tension; (<b>c</b>) PSD of wire1 tension; (<b>d</b>) PSD of sling1 tension.</p> Full article ">Figure 16
<p>Extreme responses of several wave directions. (<span class="html-italic">H<sub>s</sub></span> = 1.5 m, <span class="html-italic">T<sub>p</sub></span> = 7 s, JL1-Z3). (<b>a</b>) Max motion of jacket leg1 X; (<b>b</b>) Max motion of jacket pitch; (<b>c</b>) Max tension of wire1; (<b>d</b>) Min tension of wire1; (<b>e</b>) Max tension of sling1; (<b>f</b>) Min tension of sling1.</p> Full article ">Figure 17
<p>Extreme responses for several sea states (Dir = 180 deg, JL1-Z3). (<b>a</b>) Max motion of jacket leg1 X; (<b>b</b>) Max motion of jacket pitch; (<b>c</b>) Max tension of wire1; (<b>d</b>) Max tension of sling1.</p> Full article ">
25 pages, 15796 KiB  
Article
A Hybrid Model Based on the Bifurcation Approach for Internal Turbulent Flow with Rotation and Streamline Curvature Effects
by Kaiwen Pang, Xianbei Huang, Zhuqing Liu, Yaojun Li and Wei Yang
J. Mar. Sci. Eng. 2022, 10(12), 2022; https://doi.org/10.3390/jmse10122022 - 18 Dec 2022
Cited by 2 | Viewed by 1548
Abstract
Abstract: This study aims to research the prediction performance of a bifurcated adaptive DDES (BADDES) model in different turbulent flows with rotation and curvature [...] Full article
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Figure 1

Figure 1
<p>A sketch of the rotating channel flow. Extract from Huang’s study [<a href="#B52-jmse-10-02022" class="html-bibr">52</a>].</p> Full article ">Figure 2
<p>The transient velocity with time at the center of the channel corresponds to the coordinates of (3.14, 1, 1) in the BADDES calculation results. (<b>a</b>) Total of about 334 flow cycles. (<b>b</b>) First 100 flow cycles.</p> Full article ">Figure 3
<p>Comparison of mean velocity distribution with different turbulence models. DNS data from Yang et al. [<a href="#B51-jmse-10-02022" class="html-bibr">51</a>].</p> Full article ">Figure 4
<p>Comparison of Reynolds stress components and the turbulent kinetic energy distribution with different turbulence models. DNS data from Yang et al. [<a href="#B51-jmse-10-02022" class="html-bibr">51</a>]. (<b>a</b>) Streamwise rms. (<b>b</b>) Normal direction rms. (<b>c</b>) Span direction rms. (<b>d</b>) Turbulent kinetic energy.</p> Full article ">Figure 5
<p>Comparison of the contour of the <math display="inline"><semantics> <mrow> <msub> <mi>f</mi> <mi>d</mi> </msub> </mrow> </semantics></math> variable with different turbulence models. (<b>a</b>) BADDES. (<b>b</b>) ADDES. (<b>c</b>) DDESO.</p> Full article ">Figure 5 Cont.
<p>Comparison of the contour of the <math display="inline"><semantics> <mrow> <msub> <mi>f</mi> <mi>d</mi> </msub> </mrow> </semantics></math> variable with different turbulence models. (<b>a</b>) BADDES. (<b>b</b>) ADDES. (<b>c</b>) DDESO.</p> Full article ">Figure 6
<p>Transient fluctuation velocity distributions near the wall with different turbulence models at <span class="html-italic">y/h</span> = 0.04. (<b>a</b>) BADDES. (<b>b</b>) ADDES. (<b>c</b>) BkO. (<b>d</b>) DDESO.</p> Full article ">Figure 7
<p>Transient fluctuation velocity distributions near the wall with different turbulence models at <span class="html-italic">y/h</span> = 1.96. (<b>a</b>) BADDES. (<b>b</b>) ADDES. (<b>c</b>) BkO. (<b>d</b>) DDESO.</p> Full article ">Figure 8
<p>The isosurface at a certain time when the turbulence is fully developed with different turbulence models. The isosurface of <span class="html-italic">Q</span> is colored using the magnitude of the mean flow velocity. (<b>a</b>) BADDES, <span class="html-italic">Q</span> = 0.0025 s<sup>−2</sup>. (<b>b</b>) ADDES, <span class="html-italic">Q</span> = 0.0025 s<sup>−2</sup>. (<b>c</b>) BkO, <span class="html-italic">Q</span> = 0.0025 s<sup>−2</sup>. (<b>d</b>) DDESO, <span class="html-italic">Q</span> = 0.0003 s<sup>−2</sup>.</p> Full article ">Figure 8 Cont.
<p>The isosurface at a certain time when the turbulence is fully developed with different turbulence models. The isosurface of <span class="html-italic">Q</span> is colored using the magnitude of the mean flow velocity. (<b>a</b>) BADDES, <span class="html-italic">Q</span> = 0.0025 s<sup>−2</sup>. (<b>b</b>) ADDES, <span class="html-italic">Q</span> = 0.0025 s<sup>−2</sup>. (<b>c</b>) BkO, <span class="html-italic">Q</span> = 0.0025 s<sup>−2</sup>. (<b>d</b>) DDESO, <span class="html-italic">Q</span> = 0.0003 s<sup>−2</sup>.</p> Full article ">Figure 9
<p>Sketch of the Taylor-Couette flow.</p> Full article ">Figure 10
<p>Comparison of mean azimuthal velocity and mean angular momentum with different turbulence models. DNS data from Dong [<a href="#B54-jmse-10-02022" class="html-bibr">54</a>]. (<b>a</b>) Mean azimuthal velocity. (<b>b</b>) Mean angular momentum.</p> Full article ">Figure 11
<p>Comparison of RMS fluctuation velocity and turbulent kinetic energy profiles of different models. The DNS results of the circumferential RMS fluctuation velocity from Dong [<a href="#B54-jmse-10-02022" class="html-bibr">54</a>] and other DNS results of the RMS fluctuation velocity are extracted from the study by Chouippe et al. [<a href="#B58-jmse-10-02022" class="html-bibr">58</a>]. (<b>a</b>) RMS azimuthal fluctuation velocity. (<b>b</b>) RMS radial fluctuation velocity. (<b>c</b>) RMS axial fluctuation velocity. (<b>d</b>) Turbulent kinetic energy.</p> Full article ">Figure 12
<p>Comparison of the contour of the <math display="inline"><semantics> <mrow> <msub> <mi>f</mi> <mi>d</mi> </msub> </mrow> </semantics></math> variable with different turbulence models. (<b>a</b>) BADDES. (<b>b</b>) ADDES. (<b>c</b>) DDESO.</p> Full article ">Figure 13
<p>The expansion diagram of the instantaneous wall shear stress distribution on the inner cylindrical wall with different turbulence models. (<b>a</b>) BADDES, (<b>b</b>) ADDES, (<b>c</b>) BkO, and (<b>d</b>) DDESO.</p> Full article ">Figure 14
<p>The expansion diagram of the instantaneous wall shear stress distribution on the outer cylindrical wall with different turbulence models. (<b>a</b>) BADDES, (<b>b</b>) ADDES, (<b>c</b>) BkO, and (<b>d</b>) DDESO.</p> Full article ">Figure 15
<p>Sketch of swirling flow through an abrupt axisymmetric expansion.</p> Full article ">Figure 16
<p>Schematic diagram of the grid used in the swirling flow through an abrupt expansion. (<b>a</b>) Grid distribution in the whole computing domain. (<b>b</b>) Grid distribution in the sudden expansion position. (<b>c</b>) Cross-section grid.</p> Full article ">Figure 17
<p>Radial distributions of circumferential mean velocity, circumferential RMS velocity, mean axial velocity, and axial RMS velocity downstream of the expansion. Experimental data are from Foroutan and Yavuzkurt [<a href="#B60-jmse-10-02022" class="html-bibr">60</a>]. <span class="html-fig-inline" id="jmse-10-02022-i001"><img alt="Jmse 10 02022 i001" src="/jmse/jmse-10-02022/article_deploy/html/images/jmse-10-02022-i001.png"/></span>.</p> Full article ">Figure 18
<p>Contour plots of the <math display="inline"><semantics> <mrow> <msub> <mi>f</mi> <mi>d</mi> </msub> </mrow> </semantics></math> variable with different turbulence models (<b>a</b>) BADDES, (<b>b</b>) ADDES, and (<b>c</b>) DDESO.</p> Full article ">Figure 19
<p>Contour plots of the transient axial velocity of different turbulence models on the meridian plane. The contour levels are between −0.4 and 0.8 at intervals of 0.05. (<b>a</b>) BADDES, (<b>b</b>) ADDES, (<b>c</b>) BkO, and (<b>d</b>) DDESO.</p> Full article ">Figure 19 Cont.
<p>Contour plots of the transient axial velocity of different turbulence models on the meridian plane. The contour levels are between −0.4 and 0.8 at intervals of 0.05. (<b>a</b>) BADDES, (<b>b</b>) ADDES, (<b>c</b>) BkO, and (<b>d</b>) DDESO.</p> Full article ">Figure 20
<p>Iso-surface of <span class="html-italic">Q</span> = 1200 s<sup>−2</sup> at a certain time when the turbulence is fully developed with different turbulence models. The iso-surface of the <span class="html-italic">Q</span> is colored using the transient axial velocity. The background is the average pressure distribution on the meridian plane. (<b>a</b>) BADDES, (<b>b</b>) ADDES, (<b>c</b>) BkO, and (<b>d</b>) DDESO.</p> Full article ">
20 pages, 7461 KiB  
Review
Ocean Fronts and Their Acoustic Effects: A Review
by Yuyao Liu, Zhou Meng, Wen Chen, Yan Liang, Wei Chen and Yu Chen
J. Mar. Sci. Eng. 2022, 10(12), 2021; https://doi.org/10.3390/jmse10122021 - 17 Dec 2022
Cited by 3 | Viewed by 2335
Abstract
As one of the widespread physical phenomena in the global ocean system, the ocean front has a very important influence on underwater sound propagation. Firstly, this paper systematically reviews several methods for the detection of ocean fronts in the past decades, including traditional [...] Read more.
As one of the widespread physical phenomena in the global ocean system, the ocean front has a very important influence on underwater sound propagation. Firstly, this paper systematically reviews several methods for the detection of ocean fronts in the past decades, including traditional oceanographic methods, artificial intelligence methods, and acoustic methods, highlighting the advantages and disadvantages of each method. Next, some modeling studies of ocean fronts are reported in this paper. Based on the above research, we pay more attention to research progress on the acoustic effects of ocean fronts, including simulation analysis and experimental research, which has also been the focus of underwater acousticians for a long time. In addition, this paper looks forward to the future development direction of this field, which can provide good guidance for the study of ocean fronts and their acoustic effects in the future. Full article
(This article belongs to the Section Physical Oceanography)
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<p>Influences of the ocean front in the world’s oceanic and atmospheric environment.</p> Full article ">Figure 2
<p>Distribution of main ocean fronts in some global sea areas [<a href="#B3-jmse-10-02021" class="html-bibr">3</a>]: (<b>a</b>) the North Sea; (<b>b</b>) the Sea of Okhotsk; (<b>c</b>) the East China Sea; (<b>d</b>) the East Bering Sea; (<b>e</b>) the Gulf of Mexico; and (<b>f</b>) the Northeast US Continental Shelf. The red lines represent the main axes of ocean fronts. The yellow lines represent the boundary of the Large Marine Ecosystems (LMF).</p> Full article ">Figure 3
<p>Comparison with the traditional method for ocean fronts detection [<a href="#B43-jmse-10-02021" class="html-bibr">43</a>]: (<b>a</b>) false color SST image; (<b>b</b>) multiscale deep framework (MDF); (<b>c</b>) traditional method; and (<b>d</b>)–(<b>f</b>) differences between MDF and the traditional method.</p> Full article ">Figure 4
<p>Results of cluster analysis and the KEF sound speed characteristic model [<a href="#B20-jmse-10-02021" class="html-bibr">20</a>]: (<b>a</b>) major feature of the three kinds of SSP; (<b>b</b>) spatial distributions of the three SSP groups; and (<b>c</b>) the KEF sound speed characteristic model.</p> Full article ">Figure 5
<p>Ocean front model based on SSP [<a href="#B69-jmse-10-02021" class="html-bibr">69</a>]: (<b>a</b>) the variation of melt function with the range in ocean front model. The blue lines show the melt function at 0.1 and 0.9, respectively, while the red line represents the melt function at 0.5; (<b>b</b>) three typical sound speed profiles as input of ocean front model; and (<b>c</b>,<b>d</b>) the distribution of sound speed field of ocean front with different intensity output from the model.</p> Full article ">Figure 6
<p>Seasonal variations of parameters when the sound source was on the onshore side of the ocean front in the Celtic Sea [<a href="#B73-jmse-10-02021" class="html-bibr">73</a>]: (<b>a</b>,<b>b</b>) temperature distribution; (<b>c</b>,<b>d</b>) sound speed distribution; (<b>e</b>,<b>f</b>) the TL at frequency 300 Hz with source depth 7 m; and (<b>g</b>,<b>h</b>) the TL at frequency 1000 Hz with source depth 20 m. The position of red dots represents the depth of the sound source.</p> Full article ">Figure 7
<p>The forecasting curve of the depth of the convergence area (the red dotted line) and the calculating results of the model (blue dots) and actual ocean front (black dots): (<b>a</b>) 1 January, representing winter; (<b>b</b>) 1 July, representing summer. The forecasting results using the melt function were in good agreement with the model and the actual results, which proved that the melt function was applied to effectively achieve the depth forecast of the convergence area in the ocean front environment.</p> Full article ">Figure 8
<p>The TL in cold side (blue dotted line), warm side (black line), and experimental results with 100 m explosions (red square) in depths of: (<b>a</b>) 75 m; (<b>b</b>)150 m; (<b>c</b>) 175 m; and (<b>d</b>) 200 m [<a href="#B11-jmse-10-02021" class="html-bibr">11</a>].</p> Full article ">Figure 9
<p>The technical route of the research process.</p> Full article ">
14 pages, 2691 KiB  
Article
Development and Influence of Pore Pressure around a Bucket Foundation in Silty Soil
by Xue-Liang Zhao, Xin Wang, Peng-Cheng Ding, Shu-Huan Sui and Wen-Ni Deng
J. Mar. Sci. Eng. 2022, 10(12), 2020; https://doi.org/10.3390/jmse10122020 - 17 Dec 2022
Cited by 4 | Viewed by 1774
Abstract
Silty soil is common in the seabed of eastern coastal areas of China. The behaviors of the silty soil and a bucket foundation installed within it need more study. In this work, model tests of a bucket foundation in silty soil were performed. [...] Read more.
Silty soil is common in the seabed of eastern coastal areas of China. The behaviors of the silty soil and a bucket foundation installed within it need more study. In this work, model tests of a bucket foundation in silty soil were performed. The development of the excess pore water pressures in the different positions around the bucket was measured. Different loading conditions, with a change in the horizontal cyclic load amplitude ratio, horizontal cyclic frequency, and vertical load ratio, were considered. The effects of the pore water pressure on the shear strength of the soil around the bucket and the horizontal bearing capacity of the bucket foundation were investigated. The results show that the normalized pore water pressures close to the bucket wall at depths between 0.1 L and 0.3 L exhibit distinct change under the cyclic load. Consistent with the distribution of the pore water pressure, the degradation of the undrained shear strength is more obvious with a greater load amplitude ratio, a greater load frequency, and a smaller vertical load. The degradation rate of the static horizontal ultimate bearing capacity is in a range of 1.57% to 14.90%, under different loading conditions. Full article
(This article belongs to the Special Issue New Challenges in Offshore Geotechnical Engineering Developments)
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<p>Arrangement of pore pressure gauges (mm).</p> Full article ">Figure 2
<p>Normalized pore pressures with different horizontal cyclic load amplitudes: (<b>a</b>) close to the wall; (<b>b</b>) 0.8 D from the bucket; (<b>c</b>) 1.2 D from the bucket(0.2 L).</p> Full article ">Figure 2 Cont.
<p>Normalized pore pressures with different horizontal cyclic load amplitudes: (<b>a</b>) close to the wall; (<b>b</b>) 0.8 D from the bucket; (<b>c</b>) 1.2 D from the bucket(0.2 L).</p> Full article ">Figure 3
<p>Normalized pore water pressures with different cyclic load frequencies: (<b>a</b>) close to the wall; (<b>b</b>) 0.8 D from the wall; (<b>c</b>) 1.2 D from the wall (at 0.2 L).</p> Full article ">Figure 4
<p>Normalized pore pressures with different vertical load ratios: (<b>a</b>) close to the bucket; (<b>b</b>) 0.8 D from the bucket; (<b>c</b>) 1.2 D from the wall (at 0.2 L).</p> Full article ">Figure 5
<p>Relationship between <math display="inline"><semantics> <mrow> <mi>p</mi> <mo>/</mo> <msubsup> <mi>σ</mi> <mn>0</mn> <mo>′</mo> </msubsup> </mrow> </semantics></math> and undrained shear strength degradation rate: (<b>a</b>) horizontal cyclic load amplitude ratio; (<b>b</b>) horizonal cyclic load frequency; (<b>c</b>) vertical load ratio.</p> Full article ">Figure 6
<p>Load displacement curves with different cyclic load amplitudes ratios.</p> Full article ">Figure 7
<p>Bearing capacity attenuation rates with different cyclic load amplitude ratios.</p> Full article ">Figure 8
<p>Load displacement curves with different cyclic frequencies.</p> Full article ">Figure 9
<p>Degradation ratios of bearing capacity with different cyclic frequencies.</p> Full article ">Figure 10
<p>Load displacement curves with different vertical load ratios.</p> Full article ">Figure 11
<p>Degradation ratios of bearing capacity with different vertical load ratios.</p> Full article ">
14 pages, 5919 KiB  
Article
Numerical Investigations on Scour and Flow around Two Crossing Pipelines on a Sandy Seabed
by Fan Zhang, Zhipeng Zang, Ming Zhao, Jinfeng Zhang, Botao Xie and Xing Zou
J. Mar. Sci. Eng. 2022, 10(12), 2019; https://doi.org/10.3390/jmse10122019 - 17 Dec 2022
Cited by 2 | Viewed by 1964
Abstract
When a pipeline is laid on the seabed, local scour often occurs below it due to sea currents. In practical engineering, there are some cases that two pipelines laid on the seabed need to cross with each other. The complex flow structures around [...] Read more.
When a pipeline is laid on the seabed, local scour often occurs below it due to sea currents. In practical engineering, there are some cases that two pipelines laid on the seabed need to cross with each other. The complex flow structures around two crossing pipelines make the scour characteristics different from that of an isolated single pipeline. In this study, scour below two crossing pipelines was simulated numerically using the CFD software Flow-3D. The study is focused on the effect of the intersecting angle on the equilibrium depth and time scale of scour below the crossing position. Five intersecting angles, i.e., α = 0°, 15°, 30°, 45° and 90°, are considered, where α = 0° and 90° represent two pipelines parallel and perpendicular to each other, respectively. The results show that the equilibrium depth and the time scale of scour below the two crossing pipelines are greater than those of an isolated single pipeline. The equilibrium depth and time scale of scour have the largest values at α = 0° and decrease with the increase of the intersecting angle. Finally, the flow structures around the crossing pipelines are presented to explain the scour process. Full article
(This article belongs to the Special Issue CFD Analysis in Ocean Engineering)
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<p>Sketch of numerical model setup.</p> Full article ">Figure 2
<p>The numerical results of scour hole profile with different mesh levels (<span class="html-italic">t</span> = 370 min).</p> Full article ">Figure 3
<p>Comparison of scour hole profiles below the pipeline: (<b>a</b>) <span class="html-italic">t</span> = 10 min; (<b>b</b>) <span class="html-italic">t</span> = 30 min; (<b>c</b>) <span class="html-italic">t</span> = 200 min.</p> Full article ">Figure 3 Cont.
<p>Comparison of scour hole profiles below the pipeline: (<b>a</b>) <span class="html-italic">t</span> = 10 min; (<b>b</b>) <span class="html-italic">t</span> = 30 min; (<b>c</b>) <span class="html-italic">t</span> = 200 min.</p> Full article ">Figure 4
<p>Numerical results of 3D scour below a pipeline, (<b>a</b>) vertical profiles along the pipeline axis, (<b>b</b>) development of span length with time.</p> Full article ">Figure 5
<p>Sketch of the 3D numerical model for scour below two crossing pipelines.</p> Full article ">Figure 6
<p>The 3D profiles of scour hole for <span class="html-italic">α</span> = 0°: (<b>a</b>) <span class="html-italic">t</span> = 0 s; (<b>b</b>) <span class="html-italic">t</span> = 50 s; (<b>c</b>) <span class="html-italic">t</span> = 100 s; (<b>d</b>) <span class="html-italic">t</span> = 125 s.</p> Full article ">Figure 6 Cont.
<p>The 3D profiles of scour hole for <span class="html-italic">α</span> = 0°: (<b>a</b>) <span class="html-italic">t</span> = 0 s; (<b>b</b>) <span class="html-italic">t</span> = 50 s; (<b>c</b>) <span class="html-italic">t</span> = 100 s; (<b>d</b>) <span class="html-italic">t</span> = 125 s.</p> Full article ">Figure 7
<p>The 3D profiles of scour hole for <span class="html-italic">α</span> = 30°: (<b>a</b>) <span class="html-italic">t</span> = 0 s; (<b>b</b>) <span class="html-italic">t</span> = 100 s; (<b>c</b>) <span class="html-italic">t</span> = 200 s; (<b>d</b>) <span class="html-italic">t</span> = 350 s.</p> Full article ">Figure 8
<p>The scour profiles for different intersecting angles: (<b>a</b>) <span class="html-italic">t</span> = 300 s; (<b>b</b>) <span class="html-italic">t</span> = 900 s.</p> Full article ">Figure 9
<p>Temporal developments of scour depth for isolated and two crossing pipelines.</p> Full article ">Figure 10
<p>Variations of (<b>a</b>) <span class="html-italic">S</span>/<span class="html-italic">D</span> and (<b>b</b>) <span class="html-italic">T</span>* with intersecting angle α.</p> Full article ">Figure 11
<p>Streamlines around the pipelines for <span class="html-italic">α</span> = 0°: (<b>a</b>) <span class="html-italic">t</span> = 5 s; (<b>b</b>) <span class="html-italic">t</span> = 25 s; (<b>c</b>) <span class="html-italic">t</span> = 50 s; (<b>d</b>) <span class="html-italic">t</span> = 150 s.</p> Full article ">Figure 11 Cont.
<p>Streamlines around the pipelines for <span class="html-italic">α</span> = 0°: (<b>a</b>) <span class="html-italic">t</span> = 5 s; (<b>b</b>) <span class="html-italic">t</span> = 25 s; (<b>c</b>) <span class="html-italic">t</span> = 50 s; (<b>d</b>) <span class="html-italic">t</span> = 150 s.</p> Full article ">Figure 12
<p>Streamlines around the pipelines for α = 30°: (<b>a</b>) <span class="html-italic">t</span> = 10 s; (<b>b</b>) <span class="html-italic">t</span> = 50 s; (<b>c</b>) <span class="html-italic">t</span> = 200 s; (<b>d</b>) <span class="html-italic">t</span> = 900 s.</p> Full article ">Figure 12 Cont.
<p>Streamlines around the pipelines for α = 30°: (<b>a</b>) <span class="html-italic">t</span> = 10 s; (<b>b</b>) <span class="html-italic">t</span> = 50 s; (<b>c</b>) <span class="html-italic">t</span> = 200 s; (<b>d</b>) <span class="html-italic">t</span> = 900 s.</p> Full article ">
22 pages, 1770 KiB  
Article
Autonomous Underwater Vehicle Path Planning Method of Soft Actor–Critic Based on Game Training
by Zhuo Wang, Hao Lu, Hongde Qin and Yancheng Sui
J. Mar. Sci. Eng. 2022, 10(12), 2018; https://doi.org/10.3390/jmse10122018 - 16 Dec 2022
Cited by 6 | Viewed by 2299
Abstract
This study aims to solve the issue of the safe navigation of autonomous underwater vehicles (AUVs) in an unknown underwater environment. AUV will encounter canyons, rocks, reefs, fish, and underwater vehicles that threaten its safety during underwater navigation. A game-based soft actor–critic (GSAC) [...] Read more.
This study aims to solve the issue of the safe navigation of autonomous underwater vehicles (AUVs) in an unknown underwater environment. AUV will encounter canyons, rocks, reefs, fish, and underwater vehicles that threaten its safety during underwater navigation. A game-based soft actor–critic (GSAC) path planning method is proposed in this study to improve the adaptive capability of autonomous planning and the reliability of obstacle avoidance in the unknown underwater environment. Considering the influence of the simulation environment, the obstacles in the simulation environment are regarded as agents and play a zero-sum game with the AUV. The zero-sum game problem is solved by improving the strategy of AUV and obstacles, so that the simulation environment evolves intelligently with the AUV path planning strategy. The proposed method increases the complexity and diversity of the simulation environment, enables AUV to train in a variable environment specific to its strategy, and improves the adaptability and convergence speed of AUV in unknown underwater environments. Finally, the Python language is applied to write an unknown underwater simulation environment for the AUV simulation testing. GSAC can guide the AUV to the target point in the unknown underwater environment while avoiding large and small static obstacles, canyons, and small dynamic obstacles. Compared with the soft actor–critic(SAC) and the deep Q-network (DQN) algorithm, GSAC has better adaptability and convergence speed in the unknown underwater environment. The experiments verifies that GSAC has faster convergence, better stability, and robustness in unknown underwater environments. Full article
(This article belongs to the Special Issue AI for Navigation and Path Planning of Marine Vehicles)
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<p>GSAC framework.</p> Full article ">Figure 2
<p>Environment schematic.</p> Full article ">Figure 3
<p>Environment schematic.</p> Full article ">Figure 4
<p>Decision agent schematic.</p> Full article ">Figure 5
<p>Decision agent update schematic.</p> Full article ">Figure 6
<p>Training process of GSAC. <span style="color:red">●</span>: AUV; <span style="color:#66ff66">●</span>: Goal; ■: Obstacle; —: Border; <span style="color:red">—</span>: Sonar range; <span style="color:green">—</span>: AUV trajectory.</p> Full article ">Figure 7
<p>Test environment. (<b>a</b>) Static environment. (<b>b</b>) Dynamic environment. <span style="color:red">●</span>: AUV; <span style="color:#66ff66">●</span>: Goal; ■: Obstacle; <span style="color:blue">●</span>: Dynamic obstacle; <span style="color:blue">—</span>: Dynamic obstacle trajectory.</p> Full article ">Figure 8
<p>Pretraining process of SAC and DQN. <span style="color:red">●</span>: AUV; <span style="color:#66ff66">●</span>: Goal; ■: Obstacle; <span style="color:blue">—</span>: SAC training episode 1 trajectory; <span style="color:green">—</span>: SAC training episode 6000 trajectory; <span style="color:pink">—</span>: DQN training episode 1 trajectory; <span style="color:red">—</span>: DQN training episode 6000 trajectory.</p> Full article ">Figure 9
<p>Static environment simulation curves. (<b>a</b>) Success rate (last 100 episodes).(<b>b</b>) Reward per step. (<b>c</b>) Yaw angle. (<b>d</b>) Path length.</p> Full article ">Figure 10
<p>Static environment trajectory. <span style="color:red">●</span>: AUV; <span style="color:#66ff66">●</span>: Goal; ■: Obstacle.</p> Full article ">Figure 11
<p>Dynamic environment simulation curves. (<b>a</b>) Success rate (last 100 episodes). (<b>b</b>) Reward per step. (<b>c</b>) Yaw angle. (<b>d</b>) Path length.</p> Full article ">Figure 12
<p>Dynamic environment trajectory. <span style="color:red">●</span>: AUV; <span style="color:#66ff66">●</span>: Goal; ■: Obstacle; <span style="color:blue">●</span>: Dynamic obstacle; <span style="color:blue">—</span>: Dynamic obstacle trajectory; —: Border.</p> Full article ">
21 pages, 2524 KiB  
Article
Preliminary Analysis on the Hydrostatic Stability of a Self-Aligning Floating Offshore Wind Turbine
by Diane Scicluna, Claire De Marco Muscat-Fenech, Tonio Sant, Giuliano Vernengo and Tahsin Tezdogan
J. Mar. Sci. Eng. 2022, 10(12), 2017; https://doi.org/10.3390/jmse10122017 - 16 Dec 2022
Cited by 2 | Viewed by 2624
Abstract
There exist vast areas of offshore wind resources with water depths greater than 100 m that require floating structures. This paper provides a detailed analysis on the hydrostatic stability characteristics of a novel floating wind turbine concept. The preliminary design supports an 8 [...] Read more.
There exist vast areas of offshore wind resources with water depths greater than 100 m that require floating structures. This paper provides a detailed analysis on the hydrostatic stability characteristics of a novel floating wind turbine concept. The preliminary design supports an 8 MW horizontal-axis wind turbine with a custom self-aligning single-point mooring (SPM) floater, which is to be constructed within the existing shipyard facilities in the Maltese Islands, located in the Central Mediterranean Sea. The theoretical hydrostatic stability calculations used to find the parameters to create the model are validated using SESAM®. The hydrostatic stability analysis is carried out for different ballast capacities whilst also considering the maximum axial thrust induced by the rotor during operation. The results show that the entire floating structure exhibits hydrostatic stability characteristics for both the heeling and pitching axes that comply with the requirements set by the DNV ST-0119 standard. Numerical simulations using partial ballast are also presented. Full article
(This article belongs to the Special Issue Selected Papers from MARTECH 2022, the 6th International Conference on Maritime Technology and Engineering)
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<p>(<b>a</b>) Semi-submersible, (<b>b</b>) TLP (<b>c</b>) Spar-buoy.</p> Full article ">Figure 2
<p>Schematic diagram of the MPM and SPM systems.</p> Full article ">Figure 3
<p>Righting and heeling moment curves.</p> Full article ">Figure 4
<p>Proposed Model.</p> Full article ">Figure 5
<p>SA: RW wind direction.</p> Full article ">Figure 6
<p>Ballast tank layout.</p> Full article ">Figure 7
<p>GZ curves for heeling where A refers to the submersion of the columns and B, C represent the submersion of the deck of the structure.</p> Full article ">Figure 8
<p>GZ curves for pitching.</p> Full article ">Figure 9
<p>Moment curves about the heeling axis.</p> Full article ">Figure 10
<p>Moment curves about the pitching axis.</p> Full article ">Figure 11
<p>Effect of WPA on parameters.</p> Full article ">Figure 12
<p>Shift in LCF.</p> Full article ">Figure 13
<p>Shift in LCF position due to partial ballasting.</p> Full article ">Figure 14
<p>SA: PB—Righting moment and wind heeling moment curves.</p> Full article ">
16 pages, 3640 KiB  
Article
Collision Risk Index Calculation Based on an Improved Ship Domain Model
by Weifeng Li, Lufeng Zhong, Yang Xu and Guoyou Shi
J. Mar. Sci. Eng. 2022, 10(12), 2016; https://doi.org/10.3390/jmse10122016 - 16 Dec 2022
Cited by 10 | Viewed by 4788
Abstract
The traditional ship collision risk index model based on the distance at the closest point of approach (DCPA) and the time to the closest point of approach (TCPA) is insufficient for estimating ship collision risk and planning collision avoidance operations. This paper constructs [...] Read more.
The traditional ship collision risk index model based on the distance at the closest point of approach (DCPA) and the time to the closest point of approach (TCPA) is insufficient for estimating ship collision risk and planning collision avoidance operations. This paper constructs an elliptical, dynamic ship domain that changes with ship speed and maneuverability parameters to overcome subjective human factors. Based on the constructed domain model, the concept of the ship domain proximity factor is introduced to improve the ship collision risk model based on DCPA and TCPA, and a risk calculation function model that considers the safety of ship navigation is constructed. The numerical calculation of the improved collision risk index calculation model confirms that the enhanced model has a higher rate of identification of risk between ships. The model is more compatible with the requirements of ship navigation decision-making and can provide theoretical support and a technical basis for research on ship collision avoidance decision-making. Full article
(This article belongs to the Special Issue Application of Advanced Technologies in Maritime Safety)
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<p>Ship domain of Fuji.</p> Full article ">Figure 2
<p>Ship domain of Coldwell.</p> Full article ">Figure 3
<p>Quaternion ship domain.</p> Full article ">Figure 4
<p>A decentralized elliptic ship domain.</p> Full article ">Figure 5
<p>Elliptic dynamic ship domain model.</p> Full article ">Figure 6
<p>(<b>A</b>) The radii of the elliptical dynamic ship domain for various ship speeds; (<b>B</b>) combined elliptical dynamic ship domain and Coldwell domain.</p> Full article ">Figure 7
<p>Schematic diagram of collision risk parameters.</p> Full article ">Figure 8
<p>Schematic diagram of ship encounter parameters.</p> Full article ">Figure 9
<p>Schematic diagram of scaling factor of <math display="inline"><semantics> <mi>f</mi> </semantics></math>.</p> Full article ">Figure 10
<p>Ship encounter situation map.</p> Full article ">Figure 11
<p>Ship collision risk index line chart based on <math display="inline"><semantics> <mi>r</mi> </semantics></math> and <math display="inline"><semantics> <mrow> <msub> <mi>r</mi> <mi>f</mi> </msub> </mrow> </semantics></math>.</p> Full article ">
21 pages, 2090 KiB  
Article
Deep Water PAH Cycling in the Japan Basin (the Sea of Japan)
by Yuliya Koudryashova, Tatiana Chizhova, Mutsuo Inoue, Kazuichi Hayakawa, Seiya Nagao, Evgeniya Marina and Rodrigo Mundo
J. Mar. Sci. Eng. 2022, 10(12), 2015; https://doi.org/10.3390/jmse10122015 - 16 Dec 2022
Cited by 2 | Viewed by 1846
Abstract
A vertical pattern of fractionated polycyclic aromatic hydrocarbons (PAH) was studied in the Japan Basin in the Sea of Japan. The highest PAH concentration was found in the mesopelagic realm, possibly resulting from deep convection and/or subduction of intermediate water and its biogeochemical [...] Read more.
A vertical pattern of fractionated polycyclic aromatic hydrocarbons (PAH) was studied in the Japan Basin in the Sea of Japan. The highest PAH concentration was found in the mesopelagic realm, possibly resulting from deep convection and/or subduction of intermediate water and its biogeochemical setting in the western Japan Basin. Using 226Ra and 228Ra as tracers revealed the PAH load in the open sea from the coastal polluted water. Dissolved PAHs (DPAH, fraction < 0.5 µm) were significantly prevalent particulate PAHs (PPAH, fraction > 0.5 µm) at all depths, associated with a predominance of dissolved organic carbon (DOC) over particulate organic carbon (POC). Hydrophobicity was more important for higher-molecular-weight PAHs to be distributed between particles and the solution, while the high Koc of low-molecular-weight PAHs indicated that their partitioning was driven by other factors, such as adsorbing of soot particles. PPAH and DPAH profiles differed from the POC and DOC profiles; nevertheless, a positive moderate correlation was found for DPAH and DOC for depths below the epipelagic, suggesting the similarity of the mechanisms of input of dissolved organic matter and DPAH into the deep interior of the Sea of Japan. The PAH flux calculations showed that biological pumps and overturning circulation contribute almost equally to removing PAHs from the bathypelagic waters of the Japan Basin. Full article
(This article belongs to the Section Marine Pollution)
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<p>Location of the sampling stations in the Sea of Japan.</p> Full article ">Figure 2
<p>Salinity (PSU) and temperature (°C) vertical profiles at the sampling stations in the Japan Basin.</p> Full article ">Figure 3
<p>Vertical profiles of <sup>226</sup>Ra (<b>a</b>) and <sup>228</sup>Ra (<b>b</b>) activities at the sampling stations in the Japan Basin.</p> Full article ">Figure 4
<p>The deep water profiles of (<b>a</b>) ∑<sub>8</sub>PPAHs; (<b>b</b>) ∑<sub>9</sub>DPAHs; (<b>c</b>) POC and (<b>d</b>) DOC in the Japan Basin. POC at St.21 was not detected below the depth 200 m.</p> Full article ">Figure 5
<p>log K<sub>oc</sub> versus log K<sub>ow</sub> of individual PAHs in the epipelagic of the Japan Basin (the Sea of Japan). The K<sub>ow</sub> values were taken from [<a href="#B71-jmse-10-02015" class="html-bibr">71</a>]. The yellow crosses mean value of log K<sub>ow</sub> vs. logK<sub>oc</sub> for the same PAH.</p> Full article ">Figure 6
<p>The compositional profiles of (<b>a</b>) PPAHs and (<b>b</b>) DPAHs in the Japan Basin. Empty columns denote PAH composition at St.21; columns with strokes denote PAH composition at St.29.</p> Full article ">Figure 7
<p>Principal component analysis of the deep water samples from the Japan Basin. (<b>a</b>) Score plot; (<b>b</b>) loading plot for DPAHs. The numbers correspond to the depth where the samples were taken.</p> Full article ">
38 pages, 13954 KiB  
Article
Submarine Manoeuvrability Design: Traditional Cross-Plane vs. x-Plane Configurations in Intact and Degraded Conditions
by Benedetto Piaggio, Giuliano Vernengo, Marco Ferrando, Giorgio Mazzarello and Michele Viviani
J. Mar. Sci. Eng. 2022, 10(12), 2014; https://doi.org/10.3390/jmse10122014 - 16 Dec 2022
Cited by 2 | Viewed by 4801
Abstract
Submarines’ manoeuvrability both in intact and degraded operating conditions is the main design concern starting at the very early stages of design. This increased complexity of the design process compared to a surface vehicle can only be handled by using dynamics numerical simulations [...] Read more.
Submarines’ manoeuvrability both in intact and degraded operating conditions is the main design concern starting at the very early stages of design. This increased complexity of the design process compared to a surface vehicle can only be handled by using dynamics numerical simulations on both the vertical and horizontal manoeuvring planes. To this aim, a 6-DoF method is presented, validated, and applied to study the manoeuvring characteristics of several vessels. The analysis has been conducted considering two standpoints, i.e., to verify the design handling capabilities of the vehicles at low and high speeds and to study the off-design residual abilities in the eventual case of emergency operations with jammed/lost-control surfaces. The influence of different design features, such as, e.g., the stern plane “+” and “x” configurations, fairway size and positioning, hull dimensional ratios and restoring capabilities have been analysed in terms of impact on turning ability, course and depth changing abilities, and vertical/horizontal course stability, including the vertical damping ratio and critical velocity. Full article
(This article belongs to the Section Ocean Engineering)
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<p>Reference systems. (<b>a</b>) Vehicle kinematics. (<b>b</b>) Control planes definitions.</p> Full article ">Figure 2
<p>Bare hull—strip theory sectional forces.</p> Full article ">Figure 3
<p>Control surfaces geometry.</p> Full article ">Figure 4
<p>Stern control surfaces—configurations.</p> Full article ">Figure 5
<p>Stern control surface—stability loss due to fins at the tail end of hulls with respect to the full-span equivalent wing [<a href="#B29-jmse-10-02014" class="html-bibr">29</a>].</p> Full article ">Figure 6
<p>Control surface—control over stability contribution for elliptical sections [<a href="#B29-jmse-10-02014" class="html-bibr">29</a>].</p> Full article ">Figure 7
<p>Control surface—interaction factors for fins on cylindrical bodies of circular section [<a href="#B29-jmse-10-02014" class="html-bibr">29</a>].</p> Full article ">Figure 8
<p>Flapped control surfaces—effect on lift [<a href="#B61-jmse-10-02014" class="html-bibr">61</a>]. (<b>a</b>) Chord-wise extent and trailing edge angle. (<b>b</b>) Span-wise extent and taper ratio.</p> Full article ">Figure 9
<p>(<b>a</b>) SMG captive model testing validation—horizontal plane. (<b>b</b>) SMG captive model testing validation—horizontal plane roll. (<b>c</b>) SMG captive model testing validation—vertical plane.</p> Full article ">Figure 9 Cont.
<p>(<b>a</b>) SMG captive model testing validation—horizontal plane. (<b>b</b>) SMG captive model testing validation—horizontal plane roll. (<b>c</b>) SMG captive model testing validation—vertical plane.</p> Full article ">Figure 9 Cont.
<p>(<b>a</b>) SMG captive model testing validation—horizontal plane. (<b>b</b>) SMG captive model testing validation—horizontal plane roll. (<b>c</b>) SMG captive model testing validation—vertical plane.</p> Full article ">Figure 10
<p>(<b>a</b>) DARPA captive model testing validation—horizontal plane. (<b>b</b>) DARPA captive model testing validation—horizontal plane roll. (<b>c</b>) DARPA captive model testing validation—vertical plane.</p> Full article ">Figure 10 Cont.
<p>(<b>a</b>) DARPA captive model testing validation—horizontal plane. (<b>b</b>) DARPA captive model testing validation—horizontal plane roll. (<b>c</b>) DARPA captive model testing validation—vertical plane.</p> Full article ">Figure 10 Cont.
<p>(<b>a</b>) DARPA captive model testing validation—horizontal plane. (<b>b</b>) DARPA captive model testing validation—horizontal plane roll. (<b>c</b>) DARPA captive model testing validation—vertical plane.</p> Full article ">Figure 11
<p>(<b>a</b>) SWE captive model testing validation—horizontal plane. (<b>b</b>) SWE captive model testing validation—horizontal plane roll. (<b>c</b>) SWE captive model testing validation—vertical plane.</p> Full article ">Figure 11 Cont.
<p>(<b>a</b>) SWE captive model testing validation—horizontal plane. (<b>b</b>) SWE captive model testing validation—horizontal plane roll. (<b>c</b>) SWE captive model testing validation—vertical plane.</p> Full article ">Figure 11 Cont.
<p>(<b>a</b>) SWE captive model testing validation—horizontal plane. (<b>b</b>) SWE captive model testing validation—horizontal plane roll. (<b>c</b>) SWE captive model testing validation—vertical plane.</p> Full article ">Figure 12
<p>Manoeuvring validation.</p> Full article ">Figure 13
<p>SMG—Manoeuvring design matrix—10 knots full-scale.</p> Full article ">Figure 14
<p>SMG +-Eq—Manoeuvring speed dependency.</p> Full article ">Figure 15
<p>SMG x-Eq—Manoeuvring speed dependency.</p> Full article ">Figure 16
<p>Stern-plane failures.</p> Full article ">Figure 17
<p>(<b>a</b>) SMG +-Eq—Turning circles in #1 plane jammed or lost condition. (<b>b</b>) SMG x-Eq—Turning circles in #1 plane jammed or lost condition.</p> Full article ">Figure 18
<p>(<b>a</b>) SMG +-Eq—Horizontal zigzag in #1 plane jammed or lost condition. (<b>b</b>) SMG x-Eq—Horizontal zigzag in #1 plane jammed or lost condition.</p> Full article ">Figure A1
<p>Meander test forces.</p> Full article ">Figure A2
<p>Meander test time-series and characteristics.</p> Full article ">Figure A3
<p>Dive fail forces.</p> Full article ">Figure A4
<p>Dive fail time-series and characteristics.</p> Full article ">Figure A5
<p>Depth change ability due to stern planes execution—critical point and velocity.</p> Full article ">Figure A6
<p>SMG—Damping ratio.</p> Full article ">Figure A7
<p>SMG +-Eq Failures.</p> Full article ">Figure A7 Cont.
<p>SMG +-Eq Failures.</p> Full article ">Figure A8
<p>SMG x-Eq Failures.</p> Full article ">Figure A8 Cont.
<p>SMG x-Eq Failures.</p> Full article ">
18 pages, 6447 KiB  
Article
Ships’ Small Target Detection Based on the CBAM-YOLOX Algorithm
by Yuchao Wang, Jingdong Li, Zeming Chen and Chenglong Wang
J. Mar. Sci. Eng. 2022, 10(12), 2013; https://doi.org/10.3390/jmse10122013 - 16 Dec 2022
Cited by 7 | Viewed by 2121
Abstract
In order to solve the problem of low accuracy of small target detection in traditional target detection algorithms, the YOLOX algorithm combined with Convolutional Block Attention Module (CBAM) is proposed. The algorithm first uses CBAM on the shallow feature map to better focus [...] Read more.
In order to solve the problem of low accuracy of small target detection in traditional target detection algorithms, the YOLOX algorithm combined with Convolutional Block Attention Module (CBAM) is proposed. The algorithm first uses CBAM on the shallow feature map to better focus on small target information, and the Focal loss function is used to regress the confidence of the target to overcome the positive and negative sample imbalance problem of the one-stage target detection algorithm. Finally, the Soft Non-Maximum Suppression (SNMS) algorithm is used for post-processing to solve the problem of missed detection in close range ship target detection. The experimental results show that the average accuracy of the proposed CBAM-YOLOX network target detection is improved by 4.01% and the recall rate is improved by 8.81% compared with the traditional YOLOX network, which verifies the effectiveness of the proposed algorithm. Full article
(This article belongs to the Section Ocean Engineering)
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<p>CBAM-YOLOX network structure.</p> Full article ">Figure 2
<p>CBAM module structure.</p> Full article ">Figure 3
<p>CAM module structure.</p> Full article ">Figure 4
<p>SAM module structure.</p> Full article ">Figure 5
<p>Partial SeaShips Dataset.</p> Full article ">Figure 6
<p>Partial SeaShips Dataset.</p> Full article ">Figure 7
<p>Comparison of YOLOX_s network and CBAM-YOLOX network Loss curves. (<b>a</b>) YOLOX_s network Loss curve; (<b>b</b>) CBAM-YOLOX network Loss curve.</p> Full article ">Figure 8
<p>F1 curves of YOLOX_s network compared with CBAM-YOLOX network. (<b>a</b>) F1 curve for bulk cargo carrier; (<b>b</b>) F1 curve for container ship; (<b>c</b>) F1 curve for fishing boat; (<b>d</b>) F1 curve for general cargo ship; (<b>e</b>) F1 curve for ore carrier; (<b>f</b>) F1 curve for passenger ship.</p> Full article ">Figure 9
<p>Confusion matrix of CBAM-YOLOX network detection results.</p> Full article ">Figure 10
<p>Comparison of small target detection results with occlusion conditions. (<b>a</b>) YOLOX_s detection results; (<b>b</b>) CBAM-YOLOX detection results.</p> Full article ">Figure 11
<p>Comparison of small target detection effect under dark light conditions. (<b>a</b>) YOLOX_s detection results; (<b>b</b>) CBAM-YOLOX detection results.</p> Full article ">Figure 12
<p>Comparison of small target detection effects under dark and partially occluded conditions. (<b>a</b>) YOLOX_s detection results; (<b>b</b>) CBAM-YOLOX detection results.</p> Full article ">Figure 13
<p>Comparison of the detection effect of small targets around large targets. (<b>a</b>) YOLOX_s detection results; (<b>b</b>) CBAM-YOLOX detection results.</p> Full article ">
26 pages, 8557 KiB  
Article
Semantic Recognition of Ship Motion Patterns Entering and Leaving Port Based on Topic Model
by Gaocai Li, Mingzheng Liu, Xinyu Zhang, Chengbo Wang, Kee-hung Lai and Weihuachao Qian
J. Mar. Sci. Eng. 2022, 10(12), 2012; https://doi.org/10.3390/jmse10122012 - 16 Dec 2022
Cited by 11 | Viewed by 2076
Abstract
Recognition and understanding of ship motion patterns have excellent application value for ship navigation and maritime supervision, i.e., route planning and maritime risk assessment. This paper proposes a semantic recognition method for ship motion patterns entering and leavingport based on a probabilistic topic [...] Read more.
Recognition and understanding of ship motion patterns have excellent application value for ship navigation and maritime supervision, i.e., route planning and maritime risk assessment. This paper proposes a semantic recognition method for ship motion patterns entering and leavingport based on a probabilistic topic model. The method enables the discovery of ship motion patterns from a large amount of trajectory data in an unsupervised manner and makes the results more interpretable. The method includes three modules: trajectory preprocessing, semantic process, and knowledge discovery. Firstly, based on the activity types and characteristics of ships in the harbor waters, we propose a multi-criteria ship motion state recognition and voyage division algorithm (McSMSRVD), and ship trajectory is divided into three sub-trajectories: hoteling, maneuvering, and normal-speed sailing. Secondly, considering the influence of port traffic rules on ship motion, the semantic transformation and enrichment of port traffic rules and ship location, course, and speed are combined to construct the trajectory text document. Ship motion patterns hidden in the trajectory document set are recognized using the Latent Dirichlet allocation (LDA) topic model. Meanwhile, topic coherence and topic correlation metrics are introduced to optimize the number of topics. Thirdly, a visualization platform based on ArcGIS and Electronic Navigational Charts (ENCs) is designed to analyze the knowledge of ship motion patterns. Finally, the Tianjin port in northern China is used as the experimental object, and the results show that the method is able to identify 17 representative inbound and outbound motion patterns from AIS data and discover the ship motion details in each pattern. Full article
(This article belongs to the Section Ocean Engineering)
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<p>Framework of the semantic recognition method for ship motion patterns entering and leaving the port.</p> Full article ">Figure 2
<p>Schematic diagram of the flow of ships entering and leaving the port. Ships with red marks are in a maneuvering state; ships with white marks are in a normal-speed sailing state; ships with black marks are in a hoteling state.</p> Full article ">Figure 3
<p>Semantic transformation and enrichment of ship motion features.</p> Full article ">Figure 4
<p>Analogy from trajectory-patterns discovering to document-topics learning [<a href="#B31-jmse-10-02012" class="html-bibr">31</a>].</p> Full article ">Figure 5
<p>Geographical representation of the Latent Dirichlet Allocation model. <span class="html-italic">K</span> represents the number of topics, <span class="html-italic">N</span> represents the number of all words in the document corpus, and <span class="html-italic">M</span> represents the number of documents in the corpus. <math display="inline"><semantics> <mover> <mi>α</mi> <mo stretchy="false">→</mo> </mover> </semantics></math> and <math display="inline"><semantics> <mover> <mi>β</mi> <mo stretchy="false">→</mo> </mover> </semantics></math> represent the Dirichlet prior parameters of the document-topic distribution and the topic-word distribution prior parameters, respectively, <math display="inline"><semantics> <mrow> <mover> <mrow> <msub> <mi>ϑ</mi> <mi>m</mi> </msub> </mrow> <mo stretchy="false">→</mo> </mover> </mrow> </semantics></math> represents the topic distribution for document <span class="html-italic">m</span>, which is a <span class="html-italic">K</span>-dimensional vector. <math display="inline"><semantics> <mrow> <mover> <mrow> <msub> <mi>φ</mi> <mi>k</mi> </msub> </mrow> <mo stretchy="false">→</mo> </mover> </mrow> </semantics></math> represents the word distribution for topic <span class="html-italic">k</span>, which is an <span class="html-italic">N</span>-dimensional vector. <math display="inline"><semantics> <mrow> <msub> <mi>z</mi> <mrow> <mi>m</mi> <mo>,</mo> <mi>n</mi> </mrow> </msub> </mrow> </semantics></math> is the topic of the <span class="html-italic">n</span>-th word in document m, and <math display="inline"><semantics> <mrow> <msub> <mi>w</mi> <mrow> <mi>m</mi> <mo>,</mo> <mi>n</mi> </mrow> </msub> </mrow> </semantics></math> represents a specific word.</p> Full article ">Figure 6
<p>Layout of water and land facilities in Tianjin Port.</p> Full article ">Figure 7
<p>Rank-frequency and CDF of motion words in the trajectory documents.</p> Full article ">Figure 8
<p>The change of topic coherence with topic number.</p> Full article ">Figure 9
<p>The change of topic correlation with topic number.</p> Full article ">Figure 10
<p>Representative motion words in the topic probability distribution.</p> Full article ">Figure 11
<p>Visualization of the spatial distribution of high-frequency motion words for 17 motion pattern topics in Tianjin Port. (<b>a</b>) Topic 1; (<b>b</b>) Topic2; (<b>c</b>) Topic 3; (<b>d</b>) Topic 4; (<b>e</b>) Topic 5; (<b>f</b>) Topic 6; (<b>g</b>) Topic 7; (<b>h</b>) Topic 8; (<b>i</b>) Topic 9; (<b>j</b>) Topic 10; (<b>k</b>) Topic 11; (<b>l</b>) Topic 12; (<b>m</b>) Topic 13; (<b>n</b>) Topic 14; (<b>o</b>) Topic 15; (<b>p</b>) Topic 16; (<b>q</b>) Topic 17; (<b>r</b>) Overhead view of the Tianjin Port waterway.</p> Full article ">Figure 11 Cont.
<p>Visualization of the spatial distribution of high-frequency motion words for 17 motion pattern topics in Tianjin Port. (<b>a</b>) Topic 1; (<b>b</b>) Topic2; (<b>c</b>) Topic 3; (<b>d</b>) Topic 4; (<b>e</b>) Topic 5; (<b>f</b>) Topic 6; (<b>g</b>) Topic 7; (<b>h</b>) Topic 8; (<b>i</b>) Topic 9; (<b>j</b>) Topic 10; (<b>k</b>) Topic 11; (<b>l</b>) Topic 12; (<b>m</b>) Topic 13; (<b>n</b>) Topic 14; (<b>o</b>) Topic 15; (<b>p</b>) Topic 16; (<b>q</b>) Topic 17; (<b>r</b>) Overhead view of the Tianjin Port waterway.</p> Full article ">Figure 11 Cont.
<p>Visualization of the spatial distribution of high-frequency motion words for 17 motion pattern topics in Tianjin Port. (<b>a</b>) Topic 1; (<b>b</b>) Topic2; (<b>c</b>) Topic 3; (<b>d</b>) Topic 4; (<b>e</b>) Topic 5; (<b>f</b>) Topic 6; (<b>g</b>) Topic 7; (<b>h</b>) Topic 8; (<b>i</b>) Topic 9; (<b>j</b>) Topic 10; (<b>k</b>) Topic 11; (<b>l</b>) Topic 12; (<b>m</b>) Topic 13; (<b>n</b>) Topic 14; (<b>o</b>) Topic 15; (<b>p</b>) Topic 16; (<b>q</b>) Topic 17; (<b>r</b>) Overhead view of the Tianjin Port waterway.</p> Full article ">Figure 11 Cont.
<p>Visualization of the spatial distribution of high-frequency motion words for 17 motion pattern topics in Tianjin Port. (<b>a</b>) Topic 1; (<b>b</b>) Topic2; (<b>c</b>) Topic 3; (<b>d</b>) Topic 4; (<b>e</b>) Topic 5; (<b>f</b>) Topic 6; (<b>g</b>) Topic 7; (<b>h</b>) Topic 8; (<b>i</b>) Topic 9; (<b>j</b>) Topic 10; (<b>k</b>) Topic 11; (<b>l</b>) Topic 12; (<b>m</b>) Topic 13; (<b>n</b>) Topic 14; (<b>o</b>) Topic 15; (<b>p</b>) Topic 16; (<b>q</b>) Topic 17; (<b>r</b>) Overhead view of the Tianjin Port waterway.</p> Full article ">Figure 12
<p>Visualization of the spatial distribution of 17 motion pattern topics entering and leaving the Tianjin port waterway.</p> Full article ">Figure 13
<p>Visualization of the spatial distribution of seven representation motion pattern topics entering and leaving the Tianjin port waterway.</p> Full article ">Figure 14
<p>Visualization of the spatial distribution of 8 motion pattern topics entering the Tianjin port waterway.</p> Full article ">Figure 15
<p>Visualization of the spatial distribution of nine motion pattern topics leaving the Tianjin port waterway.</p> Full article ">
23 pages, 4137 KiB  
Article
Decentralized Documentation of Maritime Traffic Incidents to Support Conflict Resolution
by Dennis Jankowski, Julius Möller, Hilko Wiards and Axel Hahn
J. Mar. Sci. Eng. 2022, 10(12), 2011; https://doi.org/10.3390/jmse10122011 - 16 Dec 2022
Cited by 1 | Viewed by 1493
Abstract
For the investigation of major traffic accidents, larger vessels are obliged to install a voyage data recorder (VDR). However, not every vessel is equipped with a VDR, and the readout is often a manual process that is costly. In addition, not only ship-related [...] Read more.
For the investigation of major traffic accidents, larger vessels are obliged to install a voyage data recorder (VDR). However, not every vessel is equipped with a VDR, and the readout is often a manual process that is costly. In addition, not only ship-related information can be relevant for reconstructing traffic accidents, but also information from other entities such as meteorological services or port operators. Moreover, another major challenge is that entities tend to trust only their records, and not those of others as these could be manipulated in favor of the particular recording entity (e.g., to disguise any damage caused). This paper presents an approach to documenting arbitrary data from different entities in a trustworthy, decentralized, and tamper-proof manner to support the conflict resolution process. For this purpose, all involved entities in a traffic situation can contribute to the documentation by persisting their available data. Since maritime stakeholders are equipped with various sensors, a diverse and meaningful data foundation can be aggregated. The data is then signed by a mutually agreed upon timestamping authority (TSA). In this way, everyone can cryptographically verify whether the data has been subsequently changed. This approach was successfully applied in practice by documenting a vessel’s mooring maneuver. Full article
(This article belongs to the Special Issue Maritime Security and Risk Assessments)
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<p>Timestamping process between a requester and a TSA [<a href="#B41-jmse-10-02011" class="html-bibr">41</a>].</p> Full article ">Figure 2
<p>Verification process of timestamped data [<a href="#B41-jmse-10-02011" class="html-bibr">41</a>].</p> Full article ">Figure 3
<p>Approach for Decentralized Documentation of Maritime Traffic Incidents.</p> Full article ">Figure 4
<p>Negotiation Protocol for the agreement on a trusted TSA between different participants.</p> Full article ">Figure 5
<p>Procedure for local data recording and signing.</p> Full article ">Figure 6
<p>(<b>a</b>) LiDAR sensor at the quay wall in Cuxhaven, Germany, to measure the distance between the quay wall and vessels (part of the SmartKai infrastructure) (<b>b</b>) Dredger vessel <span class="html-italic">Steubenhoeft</span> with which the mooring maneuver that has to be documented was performed.</p> Full article ">Figure 7
<p>Initial situation of the evaluation scenario.</p> Full article ">Figure 8
<p>Geographical region in front of the quay wall where a documentation process is initiated when a vessel enters (Cuxhaven, Germany).</p> Full article ">
21 pages, 10223 KiB  
Article
Discrete Element Simulation on Macro-Meso Mechanical Characteristics of Natural Gas Hydrate-Bearing Sediments under Shearing
by Meng Li, Hengjie Luan, Yujing Jiang, Sunhao Zhang, Qinglin Shan, Wei Liang and Xianzhuang Ma
J. Mar. Sci. Eng. 2022, 10(12), 2010; https://doi.org/10.3390/jmse10122010 - 16 Dec 2022
Viewed by 1523
Abstract
In order to study the macro-meso shear mechanical characteristics of natural gas hydrate-bearing sediments, the direct shear simulations of natural gas hydrate-bearing sediment specimens with different saturations under different normal stress boundary conditions were carried out using the discrete element simulation program of [...] Read more.
In order to study the macro-meso shear mechanical characteristics of natural gas hydrate-bearing sediments, the direct shear simulations of natural gas hydrate-bearing sediment specimens with different saturations under different normal stress boundary conditions were carried out using the discrete element simulation program of particle flow, and the macro-meso shear mechanical characteristics of the specimens and their evolution laws were obtained, and their shear damage mechanisms were revealed. The results show that the peak intensity of natural gas hydrate-bearing sediments increases with the increase in normal stress and hydrate saturation. Hydrate particles and sand particles jointly participate in the formation and evolution of the force chain, and sand particles account for the majority of the force chain particles and take the main shear resistance role. The number of cracks produced by shear increases with hydrate saturation and normal stress. The average porosity in the shear zone shows an evolutionary pattern of decreasing and then increasing during the shear process. Full article
(This article belongs to the Special Issue Gas Hydrate—Unconventional Geological Energy Development)
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<p>Cementation types of hydrate sediments: (<bold>a</bold>) padding, (<bold>b</bold>) skeleton, (<bold>c</bold>) cement. (Adapted with permission from Ref. [<xref ref-type="bibr" rid="B17-jmse-10-02010">17</xref>]. 2010, Brugada. J., Cheng, Y.P., Soga, K).</p> Full article ">Figure 2
<p>Schematic diagram of the anti-rolling linear contact model: (<bold>a</bold>) Normal contact part, (<bold>b</bold>) Tangential contact part, (<bold>c</bold>) Anti-rolling contact part. (Adapted with permission from Ref. [<xref ref-type="bibr" rid="B33-jmse-10-02010">33</xref>]. 2014, Song, Y.; Yang, L.; Zhao, J).</p> Full article ">Figure 3
<p>Anti-rolling torque contact model. (Adapted with permission from Ref. [<xref ref-type="bibr" rid="B36-jmse-10-02010">36</xref>]. 2020, Wang, H; Zhou, Z.Y.; Zhou, B).</p> Full article ">Figure 4
<p>Composition diagram of parallel bond contact model.</p> Full article ">Figure 5
<p>Schematic diagram of contact force displacement of parallel bonding model.</p> Full article ">Figure 6
<p>Gradation curves for gas hydrate deposits in experiments and discrete element simulations. (Adapted with permission from Ref. [<xref ref-type="bibr" rid="B37-jmse-10-02010">37</xref>]. 2021, Wu, D.J).</p> Full article ">Figure 7
<p>Biaxial discrete element numerical model and microstructure of gas hydrate deposits.</p> Full article ">Figure 8
<p>Comparison of discrete element simulation and experimental results of shear test of natural gas hydrate deposits. (Adapted with permission from Ref. [<xref ref-type="bibr" rid="B37-jmse-10-02010">37</xref>]. 2021, Wu, D.J).</p> Full article ">Figure 9
<p>Direct shear discrete element numerical model and microstructure of gas hydrate deposits.</p> Full article ">Figure 10
<p>Shear stress–shear displacement curve of simulated direct shear tests on nature gas hydrate deposition under different normal stresses: (<bold>a</bold>) <italic>S<sub>h</sub></italic> = 20%, (<bold>b</bold>) <italic>S<sub>h</sub></italic> = 30%.</p> Full article ">Figure 11
<p>The relationship between the peak strength of nature gas hydrate deposition, <italic>E</italic><sub>50,</sub> and the loading amplitude.</p> Full article ">Figure 12
<p>Mechanical parameters of nature gas hydrate sediments under different normal stresses.</p> Full article ">Figure 13
<p>The shear mechanism between soil particles in natural gas hydrate-bearing sediments: (<bold>a</bold>) Before the shear, (<bold>b</bold>) After the shear.</p> Full article ">Figure 14
<p>In the direct shear test, the particle displacement nephogram of natural gas hydrate sediments with normal stress of 1.2 MPa and saturation of 30%.</p> Full article ">Figure 15
<p>Shear zone range of natural gas hydrate deposits under different conditions with shear displacement of 1.4 mm.</p> Full article ">Figure 16
<p>In the direct shear test, the contact force chain cloud diagram of natural gas hydrate deposits with normal stress of 1.2 MPa and saturation of 30%.</p> Full article ">Figure 17
<p>In direct shear test, the number of high stress particles of natural gas hydrate deposits under different conditions: (<bold>a</bold>) <italic>S<sub>h</sub></italic> = 20%, (<bold>b</bold>) <italic>S<sub>h</sub></italic> = 30%.</p> Full article ">Figure 18
<p>In direct shear test, crack propagation nephogram of natural gas hydrate deposits with normal stress of 1.2 MPa and saturation of 30%.</p> Full article ">Figure 19
<p>In the direct shear test, the shear stress, acoustic emission impact number of natural gas hydrate deposits under different conditions, and the cumulative acoustic emission impact number of specimens in the shear process are as follows: (<bold>a</bold>) <italic>S<sub>h</sub></italic> = 20%, <inline-formula><mml:math id="mm62"><mml:semantics><mml:mrow><mml:msub><mml:mi>σ</mml:mi><mml:mi>w</mml:mi></mml:msub></mml:mrow></mml:semantics></mml:math></inline-formula> <italic>=</italic> 0.9 MPa, (<bold>b</bold>) <italic>S<sub>h</sub></italic> = 20%, <inline-formula><mml:math id="mm57"><mml:semantics><mml:mrow><mml:msub><mml:mi>σ</mml:mi><mml:mi>w</mml:mi></mml:msub></mml:mrow></mml:semantics></mml:math></inline-formula> = 1.2 MPa, (<bold>c</bold>) <italic>S<sub>h</sub></italic> = 20%, <inline-formula><mml:math id="mm58"><mml:semantics><mml:mrow><mml:msub><mml:mi>σ</mml:mi><mml:mi>w</mml:mi></mml:msub></mml:mrow></mml:semantics></mml:math></inline-formula> = 1.5 MPa, (<bold>d</bold>) <italic>S<sub>h</sub></italic> = 30%, <inline-formula><mml:math id="mm59"><mml:semantics><mml:mrow><mml:msub><mml:mi>σ</mml:mi><mml:mi>w</mml:mi></mml:msub></mml:mrow></mml:semantics></mml:math></inline-formula> = 0.9 MPa, (<bold>e</bold>) <italic>S<sub>h</sub></italic> = 30%, <inline-formula><mml:math id="mm60"><mml:semantics><mml:mrow><mml:msub><mml:mi>σ</mml:mi><mml:mi>w</mml:mi></mml:msub></mml:mrow></mml:semantics></mml:math></inline-formula> = 1.2 MPa, (<bold>f</bold>) <italic>S<sub>h</sub></italic> = 30%, <inline-formula><mml:math id="mm61"><mml:semantics><mml:mrow><mml:msub><mml:mi>σ</mml:mi><mml:mi>w</mml:mi></mml:msub></mml:mrow></mml:semantics></mml:math></inline-formula> = 1.5 MPa.</p> Full article ">Figure 20
<p>Total cracks accumulated in natural gas hydrate deposits under different conditions.</p> Full article ">Figure 21
<p>Schematic diagram of sample measurement circle layout in the direct shear test.</p> Full article ">Figure 22
<p>In the direct shear test, the porosity expansion nephogram of natural gas hydrate deposits with normal stress of 1.2 MPa and saturation of 30%.</p> Full article ">Figure 23
<p>In direct shear test, the local porosity evolution curves of natural gas hydrate deposits under different conditions.: (<bold>a</bold>) <italic>S<sub>h</sub></italic> = 20%, (<bold>b</bold>) <italic>S<sub>h</sub></italic> = 30%.</p> Full article ">
19 pages, 2033 KiB  
Article
A Critical Examination for Widespread Usage of Shipping Big Data Analytics in China
by Jinhai Chen, Siheng Chang, Pengfei Zhang, Qiong Chen, Peng Peng and Christophe Claramunt
J. Mar. Sci. Eng. 2022, 10(12), 2009; https://doi.org/10.3390/jmse10122009 - 16 Dec 2022
Cited by 2 | Viewed by 2631
Abstract
Big Data Analytics (BDA) provides valuable opportunities for the optimization of maritime shipping management and operations. This might have a significant and beneficial impact on the Chinese maritime industry, which has recently emerged as a prominent player on the global stage due to [...] Read more.
Big Data Analytics (BDA) provides valuable opportunities for the optimization of maritime shipping management and operations. This might have a significant and beneficial impact on the Chinese maritime industry, which has recently emerged as a prominent player on the global stage due to the fast development of its maritime infrastructures and economical opportunities. This paper introduces two-field research conducted by a web-based questionnaire survey and semi-structured interviews with a large number of stakeholders in the maritime sector. The analyses show the impact of the development of big data technologies as well as current obstacles which constrain their deployment in the global maritime sector. The paper finally suggests several directions for promoting the wide-scale utilization of BDA in the maritime industry. Full article
(This article belongs to the Section Ocean Engineering)
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<p>Distribution of stakeholders by business sector (<span class="html-italic">n</span> = 209).</p> Full article ">Figure 2
<p>Respondents’ attention by the business sector.</p> Full article ">Figure 3
<p>Attention and familiarity crosstabulation.</p> Full article ">Figure 4
<p>Prospect of commercialization.</p> Full article ">Figure 5
<p>Major challenges of commercialization.</p> Full article ">
14 pages, 4491 KiB  
Article
Identifying Submarine Engineering Geologic Hazards in a Potential Gas Hydrate Target Area on the Southern Continental Margin of the South China Sea
by Zhenqiang Xu, Yang Li, Wei Yan, Yaoyao Lv, Guoqing Zhang, Dongyu Lu and Zuofei Zhu
J. Mar. Sci. Eng. 2022, 10(12), 2008; https://doi.org/10.3390/jmse10122008 - 16 Dec 2022
Viewed by 1702
Abstract
The southern continental margin-slope area of the South China Sea is a complex passive continental margin with diverse tectonic structures and movements. This area is rich in gas hydrate resources and is also an area with a high incidence of potential geological hazards. [...] Read more.
The southern continental margin-slope area of the South China Sea is a complex passive continental margin with diverse tectonic structures and movements. This area is rich in gas hydrate resources and is also an area with a high incidence of potential geological hazards. Identifying and understanding the potential submarine geological hazards in this area is very important for disaster prevention and management during the future exploration and development of marine resources. In this paper, five types of potentially hazardous geological bodies are identified in the research area through high-precision two-dimensional seismic processing and interpretation, including submarine mounds, pockmarks, mass transport deposits, submarine collapses and faults. At the same time, the seismic reflection characteristics and the changes in its morphology and surrounding strata are described. In addition to the causes of geological hazards in this region and their influence on exploration and development, the research prospects of geological hazards in this region are also suggested. Special tectonic and sedimentary conditions, fluid activities and hydrate decomposition may be the conditions for geological hazards in this region, which pose a significant threat to the exploration and development of seabed resources and marine engineering construction in this region. Not only does our conclusion provide useful data for the development and utilization of gas hydrate, but it also presents theoretical suggestions for reducing geological hazards in the development process. Full article
(This article belongs to the Special Issue Marine Gas Hydrates: Advances in Petrophysics, Rock Physics and Geophysics)
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<p>Location of the study area and seismic profile of the southern margin of the South China Sea. The location of A–F in <a href="#jmse-10-02008-f001" class="html-fig">Figure 1</a> correspond to the position of Figures 2–7 in order. Carbonate platforms mapped in the Zengmu Basin are from Eduard et al., [<a href="#B12-jmse-10-02008" class="html-bibr">12</a>]. The Tinjar-West Baram Line marked by the dark blue line is from the fault patterns of Clift et al., [<a href="#B26-jmse-10-02008" class="html-bibr">26</a>], Cullen [<a href="#B27-jmse-10-02008" class="html-bibr">27</a>] and Zhao et al., [<a href="#B31-jmse-10-02008" class="html-bibr">31</a>]. The Rajang Delta mapped with the gray arrows is from Hutchison [<a href="#B28-jmse-10-02008" class="html-bibr">28</a>]. The position of multibeam survey as B is from Huang et al., [<a href="#B8-jmse-10-02008" class="html-bibr">8</a>]. The red lines are the locations of seismic sections showing different potential geological hazards.</p> Full article ">Figure 2
<p>Seismic profile (<b>a</b>) and geological interpretation (<b>b</b>) showing the submarine mound with several mud diapirs in the northern part of the Zengmu Basin at the continental margin of the southern South China Sea. A certain scale of gas and/or fluids with potential gas hydrates near BSRs marked by the deep blue lines below the submarine mound. The location of <a href="#jmse-10-02008-f002" class="html-fig">Figure 2</a> correspond to the red line A in <a href="#jmse-10-02008-f001" class="html-fig">Figure 1</a>.</p> Full article ">Figure 3
<p>Submarine mounds shown by multibeam image at the continental-shelf edge to the shelf slope of the southern South China Sea from Huang et al., [<a href="#B8-jmse-10-02008" class="html-bibr">8</a>]. The red circles indicate where the submarine mounds are located and the area of <a href="#jmse-10-02008-f003" class="html-fig">Figure 3</a> is shown in <a href="#jmse-10-02008-f001" class="html-fig">Figure 1</a> as the location of B with a blue-dotted frame.</p> Full article ">Figure 4
<p>Seismic profile (<b>a</b>) and geological interpretation (<b>b</b>) showing the pockmark and mass transport deposits with gas chimneys and fluid flow passages in the southern part of the Beikang Basin at the continental margin of the southern South China Sea. A distinct interface of landslide floor marked by the purple line appears at the bottom of the mass transport deposits. A large number of faults have been developed to connect the deep and shallow strata as fluid flow passages. The location of <a href="#jmse-10-02008-f004" class="html-fig">Figure 4</a> correspond to the red line C in <a href="#jmse-10-02008-f001" class="html-fig">Figure 1</a>.</p> Full article ">Figure 5
<p>Seismic profile (<b>a</b>) and geological interpretation (<b>b</b>) showing the mass transport deposits with fluid flow activity at a steep slope break zone of the southern South China Sea. A large number of stepped faults developed in this area. Potential shallow gas accumulates with strong amplitude anomalous reflections at the top of the fluid flow passage. The location of <a href="#jmse-10-02008-f005" class="html-fig">Figure 5</a> correspond to the red line D in <a href="#jmse-10-02008-f001" class="html-fig">Figure 1</a>.</p> Full article ">Figure 6
<p>Seismic profile (<b>a</b>) and geological interpretation (<b>b</b>) showing the mass transport deposits with gas chimneys on the middle- and lower-continental shelf slopes of the southern South China Sea. Faults rarely develop in this area and the strata spread more gently. Potential shallow gas accumulates at the top of the gas chimney. The location of <a href="#jmse-10-02008-f006" class="html-fig">Figure 6</a> correspond to the red line E in <a href="#jmse-10-02008-f001" class="html-fig">Figure 1</a>.</p> Full article ">Figure 7
<p>Seismic profile (<b>a</b>) and geological interpretation (<b>b</b>) showing the submarine collapse with fluid flow passage below and mass transport deposits at the middle- and lower-continental shelf slope. Dense normal faults developed in this area. Large-scale fluid flow passage appears beneath the submarine collapse. The location of <a href="#jmse-10-02008-f007" class="html-fig">Figure 7</a> correspond to the red line F in <a href="#jmse-10-02008-f001" class="html-fig">Figure 1</a>.</p> Full article ">Figure 8
<p>The model of potential geological hazards in the southern margin of the South China Sea as follows: (<b>a</b>) Submarine mounds, pockmarks, and small submarine collapses with columnar fluid flow passage and/or faults; (<b>b</b>) Large-scale mass transport deposits with potential gas hydrate, shallow gas and fluid flow passage below; (<b>c</b>) Large-scale submarine collapse with fluid flow passage below and small mass transport deposits.</p> Full article ">
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