Jing Li, Lili Wan, Zhen Huang, Yan Chen, Huiying Tang
Path planning is one of the core issues in the autonomous navigation of an Unmanned Surface Vehicle (USV), as the accuracy of the results directly affects the safety of the USV. Hence, this paper proposes a USV path planning algorithm that integrates an improved Particle Swarm Optimisation (PSO) algorithm with a Dynamic Window Approach (DWA). Firstly, in order to advance the solution accuracy and convergence speed of the PSO algorithm, a nonlinear decreasing inertia weight and adaptive learning factors are introduced. Secondly, in order to solve the problem of long path and path non-smoothness, the fitness function of PSO is modified to consider both path length and path smoothness. Finally, the International Regulations for Preventing Collisions at Sea (COLREGS) are utilised to achieve dynamic obstacle avoidance while complying with maritime practices. Numerical cases verify that the path planned via the proposed algorithm is shorter and smoother, guaranteeing the safety of USV navigation while complying with the COLREGS.
{"title":"Hybrid Path Planning Strategy Based on Improved Particle Swarm Optimisation Algorithm Combined with DWA for Unmanned Surface Vehicles","authors":"Jing Li, Lili Wan, Zhen Huang, Yan Chen, Huiying Tang","doi":"10.3390/jmse12081268","DOIUrl":"https://doi.org/10.3390/jmse12081268","url":null,"abstract":"Path planning is one of the core issues in the autonomous navigation of an Unmanned Surface Vehicle (USV), as the accuracy of the results directly affects the safety of the USV. Hence, this paper proposes a USV path planning algorithm that integrates an improved Particle Swarm Optimisation (PSO) algorithm with a Dynamic Window Approach (DWA). Firstly, in order to advance the solution accuracy and convergence speed of the PSO algorithm, a nonlinear decreasing inertia weight and adaptive learning factors are introduced. Secondly, in order to solve the problem of long path and path non-smoothness, the fitness function of PSO is modified to consider both path length and path smoothness. Finally, the International Regulations for Preventing Collisions at Sea (COLREGS) are utilised to achieve dynamic obstacle avoidance while complying with maritime practices. Numerical cases verify that the path planned via the proposed algorithm is shorter and smoother, guaranteeing the safety of USV navigation while complying with the COLREGS.","PeriodicalId":16168,"journal":{"name":"Journal of Marine Science and Engineering","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2024-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141862719","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Atsuko Fukunaga, Kailey H. Pascoe, Randall K. Kosaki, John H. R. Burns
Coral reefs worldwide are under increasing levels of pressure due to global and local stressors. Long-term monitoring of coral reefs through repeated observations at fixed survey sites allows scientists to assess temporal patterns in coral-reef communities and plays important roles in informing managers of the state of the ecosystems. Here, we describe coral assemblages in Papahānaumokuākea, the largest contiguous fully protected marine conservation area in the United States, using long-term monitoring data collected from 20 permanent (fixed) sites at three islands/atolls, Lalo, Kapou and Manawai, between 2014 and 2021. Significant temporal shifts in coral colony composition were detected at some of the monitoring sites, which were attributed to the impact of a mass coral bleaching event in 2014 and Hurricane Walaka in 2018. In particular, the bleaching affected multiple sites at Kapou and one site at Manawai where coral assemblages shifted from the Montipora dilatata/flabellata/turgescens complex to M. capitata dominance; despite being the dominant species at multiple monitoring sites prior to the bleaching, the M. dilatata/flabellata/turgescens complex has not been recorded at any of our monitoring sites in recent years. Coral conditions, such as bleaching, predation, subacute tissue loss, Porites pigmentation response and trematodiasis, did not show differences in the occurrence among the three islands/atolls once the site and temporal variabilities, as well as environmental covariates for bleaching, were considered. Coral genera, however, exhibited different sensitivities to these conditions. These findings highlight the importance of continuing coral reef monitoring at the species level, covering a broad range of coral assemblage compositions and habitat types in Papahānaumokuākea.
{"title":"Elucidating Temporal Patterns in Coral Health and Assemblage Structure in Papahānaumokuākea","authors":"Atsuko Fukunaga, Kailey H. Pascoe, Randall K. Kosaki, John H. R. Burns","doi":"10.3390/jmse12081267","DOIUrl":"https://doi.org/10.3390/jmse12081267","url":null,"abstract":"Coral reefs worldwide are under increasing levels of pressure due to global and local stressors. Long-term monitoring of coral reefs through repeated observations at fixed survey sites allows scientists to assess temporal patterns in coral-reef communities and plays important roles in informing managers of the state of the ecosystems. Here, we describe coral assemblages in Papahānaumokuākea, the largest contiguous fully protected marine conservation area in the United States, using long-term monitoring data collected from 20 permanent (fixed) sites at three islands/atolls, Lalo, Kapou and Manawai, between 2014 and 2021. Significant temporal shifts in coral colony composition were detected at some of the monitoring sites, which were attributed to the impact of a mass coral bleaching event in 2014 and Hurricane Walaka in 2018. In particular, the bleaching affected multiple sites at Kapou and one site at Manawai where coral assemblages shifted from the Montipora dilatata/flabellata/turgescens complex to M. capitata dominance; despite being the dominant species at multiple monitoring sites prior to the bleaching, the M. dilatata/flabellata/turgescens complex has not been recorded at any of our monitoring sites in recent years. Coral conditions, such as bleaching, predation, subacute tissue loss, Porites pigmentation response and trematodiasis, did not show differences in the occurrence among the three islands/atolls once the site and temporal variabilities, as well as environmental covariates for bleaching, were considered. Coral genera, however, exhibited different sensitivities to these conditions. These findings highlight the importance of continuing coral reef monitoring at the species level, covering a broad range of coral assemblage compositions and habitat types in Papahānaumokuākea.","PeriodicalId":16168,"journal":{"name":"Journal of Marine Science and Engineering","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2024-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141862718","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Caiquan Xiong, Hao Shi, Jiaming Li, Xinyun Wu, Rong Gao
Ship trajectory prediction is a complex time series forecasting problem that necessitates models capable of accurately capturing both long-term trends and short-term fluctuations in vessel movements. While existing deep learning models excel in short-term predictions, they struggle with long-sequence time series forecasting (LSTF) due to difficulties in capturing long-term dependencies, resulting in significant prediction errors. This paper proposes the Informer-TP method, leveraging Automatic Identification System (AIS) data and based on the Informer model, to enhance the ability to capture long-term dependencies, thereby improving the accuracy of long-term ship trajectory predictions. Firstly, AIS data are preprocessed and divided into trajectory segments. Secondly, the time series is separated from the trajectory data in each segment and input into the model. The Informer model is utilized to improve long-term ship trajectory prediction ability, and the output mechanism is adjusted to enable predictions for each segment. Finally, the proposed model’s effectiveness is validated through comparisons with baseline models, and the influence of various sequence lengths Ltoken on the Informer-TP model is explored. Experimental results show that compared with other models, the proposed model exhibits the lowest Mean Squared Error, Mean Absolute Error, and Haversine distance in long-term forecasting, demonstrating that the model can effectively capture long-term dependencies in the trajectories, thereby improving the accuracy of long-term vessel trajectory predictions. This provides an effective and feasible method for ensuring ship navigation safety and advancing intelligent shipping.
{"title":"Informer-Based Model for Long-Term Ship Trajectory Prediction","authors":"Caiquan Xiong, Hao Shi, Jiaming Li, Xinyun Wu, Rong Gao","doi":"10.3390/jmse12081269","DOIUrl":"https://doi.org/10.3390/jmse12081269","url":null,"abstract":"Ship trajectory prediction is a complex time series forecasting problem that necessitates models capable of accurately capturing both long-term trends and short-term fluctuations in vessel movements. While existing deep learning models excel in short-term predictions, they struggle with long-sequence time series forecasting (LSTF) due to difficulties in capturing long-term dependencies, resulting in significant prediction errors. This paper proposes the Informer-TP method, leveraging Automatic Identification System (AIS) data and based on the Informer model, to enhance the ability to capture long-term dependencies, thereby improving the accuracy of long-term ship trajectory predictions. Firstly, AIS data are preprocessed and divided into trajectory segments. Secondly, the time series is separated from the trajectory data in each segment and input into the model. The Informer model is utilized to improve long-term ship trajectory prediction ability, and the output mechanism is adjusted to enable predictions for each segment. Finally, the proposed model’s effectiveness is validated through comparisons with baseline models, and the influence of various sequence lengths Ltoken on the Informer-TP model is explored. Experimental results show that compared with other models, the proposed model exhibits the lowest Mean Squared Error, Mean Absolute Error, and Haversine distance in long-term forecasting, demonstrating that the model can effectively capture long-term dependencies in the trajectories, thereby improving the accuracy of long-term vessel trajectory predictions. This provides an effective and feasible method for ensuring ship navigation safety and advancing intelligent shipping.","PeriodicalId":16168,"journal":{"name":"Journal of Marine Science and Engineering","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2024-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141862720","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Sai Wang, Nan-Lin Chen, Yong-Duo Song, Tuan-Tuan Wang, Jing Wen, Tuan-Qi Guo, Hong-Jin Zhang, Ling Mo, Hao-Ran Ma, Lei Xiang
Healthy coral reefs provide diverse habitats for marine life, playing a crucial role in marine ecosystems. Coral health is under threat due to global climate change, ocean pollution, and other environmental stressors, leading to coral bleaching. Coral bleaching disrupts the symbiotic relationship between corals and algae, ultimately impacting the entire marine ecosystem. Processing complex underwater images manually is time-consuming and burdensome for marine experts. To rapidly locate and monitor coral health, deep neural networks are employed for identifying coral categories, which can facilitate the automated processing of extensive underwater imaging data. However, these classification networks may overlook critical classification criteria like color and texture. This paper proposes a multi-local perception network (ML-Net) for image classification of healthy and bleached corals. ML-Net focuses on local features of coral targets, leveraging valuable information for image classification. Specifically, the proposed multi-branch local adaptive block extracts image details through parallel convolution kernels. Then, the proposed multi-scale local fusion block integrates features of different scales vertically, enhancing the detailed information within the deep network. Residual structures in the shallow network transmit local information with more texture and color to the deep network. Both horizontal and vertical multi-scale fusion blocks in deep networks are used to capture and retain local details. We evaluated ML-Net using six evaluation metrics on the Bleached and Unbleached Corals Classification dataset. In particular, ML-Net achieves an ACC result of 86.35, which is 4.36 higher than ResNet and 8.5 higher than ConvNext. Experimental results demonstrate the effectiveness of the proposed modules for coral classification in underwater environments.
{"title":"ML-Net: A Multi-Local Perception Network for Healthy and Bleached Coral Image Classification","authors":"Sai Wang, Nan-Lin Chen, Yong-Duo Song, Tuan-Tuan Wang, Jing Wen, Tuan-Qi Guo, Hong-Jin Zhang, Ling Mo, Hao-Ran Ma, Lei Xiang","doi":"10.3390/jmse12081266","DOIUrl":"https://doi.org/10.3390/jmse12081266","url":null,"abstract":"Healthy coral reefs provide diverse habitats for marine life, playing a crucial role in marine ecosystems. Coral health is under threat due to global climate change, ocean pollution, and other environmental stressors, leading to coral bleaching. Coral bleaching disrupts the symbiotic relationship between corals and algae, ultimately impacting the entire marine ecosystem. Processing complex underwater images manually is time-consuming and burdensome for marine experts. To rapidly locate and monitor coral health, deep neural networks are employed for identifying coral categories, which can facilitate the automated processing of extensive underwater imaging data. However, these classification networks may overlook critical classification criteria like color and texture. This paper proposes a multi-local perception network (ML-Net) for image classification of healthy and bleached corals. ML-Net focuses on local features of coral targets, leveraging valuable information for image classification. Specifically, the proposed multi-branch local adaptive block extracts image details through parallel convolution kernels. Then, the proposed multi-scale local fusion block integrates features of different scales vertically, enhancing the detailed information within the deep network. Residual structures in the shallow network transmit local information with more texture and color to the deep network. Both horizontal and vertical multi-scale fusion blocks in deep networks are used to capture and retain local details. We evaluated ML-Net using six evaluation metrics on the Bleached and Unbleached Corals Classification dataset. In particular, ML-Net achieves an ACC result of 86.35, which is 4.36 higher than ResNet and 8.5 higher than ConvNext. Experimental results demonstrate the effectiveness of the proposed modules for coral classification in underwater environments.","PeriodicalId":16168,"journal":{"name":"Journal of Marine Science and Engineering","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2024-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141862717","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The International Telecommunication Union (ITU) proposed the very high frequency data exchange system (VDES) to improve the efficiency of ship–ship and ship–shore communication; however, its existing single-hop transmission mode is insufficient for identifying all ships within a coastal zone. This paper proposes an adaptive cooperative ship identification method based on the VDES using multihop transmission, where the coastal zone is divided into a grid, with the ships acting as nodes, and the optimal sink and relay nodes are calculated for each grid element. An adaptive multipath transmission protocol is then applied to improve the transmission efficiency and stability of the links between the nodes. Simulations were performed utilizing real Automatic Identification System (AIS) data from a coastal zone, and the results showed that the proposed method effectively reduced the time-slot occupancy and collision rate while achieving a 100% identification of ships within 120 nautical miles (nm) of the coast with only 4.8% of the usual communication resources.
{"title":"Adaptive Cooperative Ship Identification for Coastal Zones Based on the Very High Frequency Data Exchange System","authors":"Qing Hu, Meng’en Song, Di Zhang, Shuaiheng Huai","doi":"10.3390/jmse12081264","DOIUrl":"https://doi.org/10.3390/jmse12081264","url":null,"abstract":"The International Telecommunication Union (ITU) proposed the very high frequency data exchange system (VDES) to improve the efficiency of ship–ship and ship–shore communication; however, its existing single-hop transmission mode is insufficient for identifying all ships within a coastal zone. This paper proposes an adaptive cooperative ship identification method based on the VDES using multihop transmission, where the coastal zone is divided into a grid, with the ships acting as nodes, and the optimal sink and relay nodes are calculated for each grid element. An adaptive multipath transmission protocol is then applied to improve the transmission efficiency and stability of the links between the nodes. Simulations were performed utilizing real Automatic Identification System (AIS) data from a coastal zone, and the results showed that the proposed method effectively reduced the time-slot occupancy and collision rate while achieving a 100% identification of ships within 120 nautical miles (nm) of the coast with only 4.8% of the usual communication resources.","PeriodicalId":16168,"journal":{"name":"Journal of Marine Science and Engineering","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2024-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141862716","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This study presents a comprehensive approach to trim optimization as an energy efficiency improvement measure, focusing on reducing fuel consumption for one RO-RO car carrier. Utilizing Computational Fluid Dynamics (CFD) software, the methodology incorporates artificial neural networks (ANNs) to develop a mathematical model for estimating key parameters such as the brake power, daily fuel oil consumption (DFOC) and propeller speed. The complex ANN model is then integrated into a user-friendly software tool for practical engineering applications. The research outlines a seven-phase trim optimization process and discusses its potential extension to other types of ships, aiming to establish a universal methodology for CFD-based engineering analyses. Based on the trim optimization results, the biggest DFOC goes up to 10.5% at 7.5 m draft and up to 8% for higher drafts. Generally, in every considered case, it is recommended to sail with the trim towards the bow, meaning that the ship’s longitudinal center of gravity should be adjusted to tilt slightly forward.
{"title":"CFD-Powered Ship Trim Optimization: Integrating ANN for User-Friendly Software Tool Development","authors":"Matija Vasilev, Milan Kalajdžić, Ines Ivković","doi":"10.3390/jmse12081265","DOIUrl":"https://doi.org/10.3390/jmse12081265","url":null,"abstract":"This study presents a comprehensive approach to trim optimization as an energy efficiency improvement measure, focusing on reducing fuel consumption for one RO-RO car carrier. Utilizing Computational Fluid Dynamics (CFD) software, the methodology incorporates artificial neural networks (ANNs) to develop a mathematical model for estimating key parameters such as the brake power, daily fuel oil consumption (DFOC) and propeller speed. The complex ANN model is then integrated into a user-friendly software tool for practical engineering applications. The research outlines a seven-phase trim optimization process and discusses its potential extension to other types of ships, aiming to establish a universal methodology for CFD-based engineering analyses. Based on the trim optimization results, the biggest DFOC goes up to 10.5% at 7.5 m draft and up to 8% for higher drafts. Generally, in every considered case, it is recommended to sail with the trim towards the bow, meaning that the ship’s longitudinal center of gravity should be adjusted to tilt slightly forward.","PeriodicalId":16168,"journal":{"name":"Journal of Marine Science and Engineering","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2024-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141778773","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
To analyze the dynamic response of a rigid M-shaped jumper subjected to combined internal and external flows, a one-way coupled fluid–structure interaction process is applied. First, CFD simulations are conducted separately for the internal and external fluid domains. The pressure histories on the inner and outer walls are exported and loaded into the finite element model using inverse distance interpolation. Then, FEA is performed to determine the dynamic response, followed by a fatigue assessment based on the obtained stress data. The displacement, acceleration, and stress distribution along the M-shaped jumper are obtained. External flow velocity dominates the displacements, while internal flow velocity dominates the vibrations and stresses. The structural response to the combined effect of internal and external flows, plus the response to gravity alone, equals the sum of the structural responses to internal flow alone and external flow alone. Fatigue damage is calculated for the bend exhibiting the most intense vibration and higher stress levels, and the locations with significant damage correspond to areas with high maximum von Mises stress. This paper aims to evaluate multiple flow fields acting simultaneously on subsea pipelines and to identify the main factors that provide valuable information for their design, monitoring, and maintenance.
为了分析刚性 M 型跳线在内部和外部气流共同作用下的动态响应，采用了单向耦合流固耦合过程。首先，分别对内部和外部流体域进行 CFD 模拟。使用反距离插值法将内外壁上的压力历史导出并加载到有限元模型中。然后，进行有限元分析以确定动态响应，并根据获得的应力数据进行疲劳评估。结果得出了沿 M 型跳线的位移、加速度和应力分布。外部流速主导位移，而内部流速主导振动和应力。结构对内部和外部水流共同作用的响应，加上对重力单独作用的响应，等于结构对内部水流单独作用和外部水流单独作用的响应之和。对振动最剧烈、应力水平较高的弯道进行疲劳损伤计算，损伤严重的位置与最大 von Mises 应力较高的区域相对应。本文旨在评估同时作用于海底管道的多个流场，并确定可为管道设计、监测和维护提供有价值信息的主要因素。
{"title":"Dynamic Response Analysis of a Subsea Rigid M-Shaped Jumper under Combined Internal and External Flows","authors":"Guangzhao Li, Wenhua Li, Shanying Lin, Fenghui Han, Xingkun Zhou","doi":"10.3390/jmse12081261","DOIUrl":"https://doi.org/10.3390/jmse12081261","url":null,"abstract":"To analyze the dynamic response of a rigid M-shaped jumper subjected to combined internal and external flows, a one-way coupled fluid–structure interaction process is applied. First, CFD simulations are conducted separately for the internal and external fluid domains. The pressure histories on the inner and outer walls are exported and loaded into the finite element model using inverse distance interpolation. Then, FEA is performed to determine the dynamic response, followed by a fatigue assessment based on the obtained stress data. The displacement, acceleration, and stress distribution along the M-shaped jumper are obtained. External flow velocity dominates the displacements, while internal flow velocity dominates the vibrations and stresses. The structural response to the combined effect of internal and external flows, plus the response to gravity alone, equals the sum of the structural responses to internal flow alone and external flow alone. Fatigue damage is calculated for the bend exhibiting the most intense vibration and higher stress levels, and the locations with significant damage correspond to areas with high maximum von Mises stress. This paper aims to evaluate multiple flow fields acting simultaneously on subsea pipelines and to identify the main factors that provide valuable information for their design, monitoring, and maintenance.","PeriodicalId":16168,"journal":{"name":"Journal of Marine Science and Engineering","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2024-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141778775","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Guolei Huang, Yifan Liu, Jianjian Xin, Tiantian Bao
Evaluating the degradation of hull and ship performance and exploring their degradation pathways is crucial for developing scientific and reasonable ship maintenance plans. This paper proposes a two-stage optimization (TSO) algorithm that combines the Genetic Algorithm (GA) and Long Short-Term Memory (LSTM) network, capable of simultaneously optimizing input features and model parameters to enhance the accuracy and generalization ability of speed prediction models. Additionally, a performance degradation assessment method based on speed loss is provided, aimed at evaluating the degradation of hull and propeller performance, as well as extracting the performance degradation paths. The results indicated that the proposed TSO-LSTM-GA algorithm significantly outperformed existing baseline models. Furthermore, the provided performance degradation assessment method demonstrated certain effectiveness on the target ship data, with a measured degradation rate of 0.00344 kn/d and a performance degradation of 9.569% over 478 days, corresponding to an annual speed loss of 1.257 kn.
{"title":"Assessment of Hull and Propeller Performance Degradation Based on TSO-GA-LSTM","authors":"Guolei Huang, Yifan Liu, Jianjian Xin, Tiantian Bao","doi":"10.3390/jmse12081263","DOIUrl":"https://doi.org/10.3390/jmse12081263","url":null,"abstract":"Evaluating the degradation of hull and ship performance and exploring their degradation pathways is crucial for developing scientific and reasonable ship maintenance plans. This paper proposes a two-stage optimization (TSO) algorithm that combines the Genetic Algorithm (GA) and Long Short-Term Memory (LSTM) network, capable of simultaneously optimizing input features and model parameters to enhance the accuracy and generalization ability of speed prediction models. Additionally, a performance degradation assessment method based on speed loss is provided, aimed at evaluating the degradation of hull and propeller performance, as well as extracting the performance degradation paths. The results indicated that the proposed TSO-LSTM-GA algorithm significantly outperformed existing baseline models. Furthermore, the provided performance degradation assessment method demonstrated certain effectiveness on the target ship data, with a measured degradation rate of 0.00344 kn/d and a performance degradation of 9.569% over 478 days, corresponding to an annual speed loss of 1.257 kn.","PeriodicalId":16168,"journal":{"name":"Journal of Marine Science and Engineering","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2024-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141778780","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The mechanical response of the marine reef sand–geogrid (RG) interface can be influenced by a high-temperature climate, grain size, and variable stress environments. These factors are critical to the effectiveness of geogrid reinforcement in reef sand engineering. However, there are few studies on the influences of grain size, temperature, and stress history on the mechanical characteristics of RG interfaces, with most studies centering on the influence of single factors on the mechanical characteristics of RG interfaces. In this paper, based on self-developed temperature-controlled large interface shear equipment, a series of before/post-cyclic shear tests were carried out on RG interfaces in the temperature range of 5–80 °C. The impact of different reef sand grain sizes on the RG interface was explored (S1: 1–2 mm; S2: 2–4 mm). It was shown that temperature and grain size had significant influences on the mechanical characteristics of the RS interface. Compared with the S1 RG interfaces, the S2 RG interfaces had higher sensitivity to temperature changes with respect to the before/post-cyclic maximum shear strength. Moreover, in comparison to the before-cyclic shear strength, the post-cyclic maximum shear strength is more responsive to temperature changes. The before/post-cyclic maximum shear strength of the S2 RG interfaces was greater than the maximum shear strength of the S1 RG interfaces as the temperature changed. Based on the results of physical tests, a machine learning model containing 450 datasets was constructed, which can accurately predict the shear strength of the RG interface.
{"title":"Temperature-Dependent Post-Cyclic Mechanical Characteristics of Interfaces between Geogrid and Marine Reef Sand: Experimental Research and Machine Learning Modeling","authors":"Zhiming Chao, Haoyu Wang, Jinhai Zheng, Danda Shi, Chunxu Li, Gege Ding, Xianhui Feng","doi":"10.3390/jmse12081262","DOIUrl":"https://doi.org/10.3390/jmse12081262","url":null,"abstract":"The mechanical response of the marine reef sand–geogrid (RG) interface can be influenced by a high-temperature climate, grain size, and variable stress environments. These factors are critical to the effectiveness of geogrid reinforcement in reef sand engineering. However, there are few studies on the influences of grain size, temperature, and stress history on the mechanical characteristics of RG interfaces, with most studies centering on the influence of single factors on the mechanical characteristics of RG interfaces. In this paper, based on self-developed temperature-controlled large interface shear equipment, a series of before/post-cyclic shear tests were carried out on RG interfaces in the temperature range of 5–80 °C. The impact of different reef sand grain sizes on the RG interface was explored (S1: 1–2 mm; S2: 2–4 mm). It was shown that temperature and grain size had significant influences on the mechanical characteristics of the RS interface. Compared with the S1 RG interfaces, the S2 RG interfaces had higher sensitivity to temperature changes with respect to the before/post-cyclic maximum shear strength. Moreover, in comparison to the before-cyclic shear strength, the post-cyclic maximum shear strength is more responsive to temperature changes. The before/post-cyclic maximum shear strength of the S2 RG interfaces was greater than the maximum shear strength of the S1 RG interfaces as the temperature changed. Based on the results of physical tests, a machine learning model containing 450 datasets was constructed, which can accurately predict the shear strength of the RG interface.","PeriodicalId":16168,"journal":{"name":"Journal of Marine Science and Engineering","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2024-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141778776","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Freak waves are characterized by extreme wave height, irregular wave shape, high peak energy, short duration, and formidable destructive potential, posing a significant threat to offshore structures. Therefore, analyzing dynamic responses exhibited by advanced offshore platforms such as the offshore triceratops under the influence of freak waves holds paramount importance. However, the response analysis of offshore triceratops under freak waves has not been explored so far in the literature. Hence, the present study aims to investigate the dynamics of offshore triceratops intended for ultradeep waters under the impact of freak waves. Initially, the dual superposition model was utilized to generate the freak waves, and the numerical model of the platform was developed using ANSYS AQWA. Subsequently, the dynamic response characteristics of offshore triceratops under the influence of freak waves were analyzed in the time domain. The results demonstrate the effects of freak waves on the surge, heave, and pitch responses of the deck and buoyant legs were substantial, leading to a significant increase in maximum responses and variations in mean shift and standard deviations. The innovative insights derived from this study can serve as a benchmark for validating the effective performance and design of offshore triceratops.
{"title":"Study on the Dynamic Response of Offshore Triceratops under Freak Waves","authors":"Nagavinothini Ravichandran, Butsawan Bidorn","doi":"10.3390/jmse12081260","DOIUrl":"https://doi.org/10.3390/jmse12081260","url":null,"abstract":"Freak waves are characterized by extreme wave height, irregular wave shape, high peak energy, short duration, and formidable destructive potential, posing a significant threat to offshore structures. Therefore, analyzing dynamic responses exhibited by advanced offshore platforms such as the offshore triceratops under the influence of freak waves holds paramount importance. However, the response analysis of offshore triceratops under freak waves has not been explored so far in the literature. Hence, the present study aims to investigate the dynamics of offshore triceratops intended for ultradeep waters under the impact of freak waves. Initially, the dual superposition model was utilized to generate the freak waves, and the numerical model of the platform was developed using ANSYS AQWA. Subsequently, the dynamic response characteristics of offshore triceratops under the influence of freak waves were analyzed in the time domain. The results demonstrate the effects of freak waves on the surge, heave, and pitch responses of the deck and buoyant legs were substantial, leading to a significant increase in maximum responses and variations in mean shift and standard deviations. The innovative insights derived from this study can serve as a benchmark for validating the effective performance and design of offshore triceratops.","PeriodicalId":16168,"journal":{"name":"Journal of Marine Science and Engineering","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2024-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141778774","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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