Ocean Engineering最新文献

Hydrodynamic modeling of kelp (Saccharina latissima) farms: From an aggregate of kelp to a single line cultivation system 海带（Saccharina latissima）养殖场的水动力模型：从海带总量到单线栽培系统

IF 4.6 2区工程技术

Ocean Engineering Pub Date : 2024-11-06 DOI: 10.1016/j.oceaneng.2024.119519

{"title":"Hydrodynamic modeling of kelp (Saccharina latissima) farms: From an aggregate of kelp to a single line cultivation system","authors":"","doi":"10.1016/j.oceaneng.2024.119519","DOIUrl":"10.1016/j.oceaneng.2024.119519","url":null,"abstract":"<div><div>With the expansion of macroalgae aquaculture in oceanic waters, especially of order <em>Laminariales</em>, a need exists to have optimized cultivation systems suitable for exposed conditions. To enable the design of such systems with a quantifiable level of confidence, in this paper, we developed a high-fidelity hydrodynamic modeling technique for kelp farms by introducing equivalent kelp elements for kelp aggregates with Reynolds number-based drag coefficients. After validating the model with towing tests for model kelp aggregates, it was then compared with comprehensive field datasets for a single line cultivation system with two mooring connections, in Saco Bay, Maine. The model yielded a larger tension than the measured tension by 23.3% on the west mooring line but a smaller tension by 23.2% on the east mooring line. The discrepancies may be caused by the uncertainties in the model configuration and input due to difficulties quantifying exact longline orientation, anchor-anchor distance, current reduction along the kelp longline, kelp mass density, and rope axial stiffness. Sensitivity analysis indicates that addressing these uncertainties may improve the model technique. Even though, the developed model is still reliable with a safety factor in the application for the design, installation and management of kelp aquaculture farms.</div></div>","PeriodicalId":19403,"journal":{"name":"Ocean Engineering","volume":null,"pages":null},"PeriodicalIF":4.6,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142594000","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Event-triggered predictive path following control of autonomous ships with an MMG model 利用 MMG 模型对自动驾驶船舶进行事件触发式预测路径跟踪控制

IF 4.6 2区工程技术

Ocean Engineering Pub Date : 2024-11-06 DOI: 10.1016/j.oceaneng.2024.119582

引用次数: 0

Marine steel corrosion prediction and zonation using feature extraction and machine learning in the seas around China 利用特征提取和机器学习进行中国周边海域海洋钢腐蚀预测和分区

IF 4.6 2区工程技术

Ocean Engineering Pub Date : 2024-11-06 DOI: 10.1016/j.oceaneng.2024.119649

{"title":"Marine steel corrosion prediction and zonation using feature extraction and machine learning in the seas around China","authors":"","doi":"10.1016/j.oceaneng.2024.119649","DOIUrl":"10.1016/j.oceaneng.2024.119649","url":null,"abstract":"<div><div>To reduce the losses caused by the marine corrosion of steel, it is important to establish a prediction model to determine the corrosion rate of steel in depth-varying aggressive marine environments. The use of statistical feature extraction methods and machine learning modeling for marine steel corrosion prediction and zoning in the seas around China is investigated. In this study, 856 samples were collected. Mean and standard deviation were selected as environmental characteristics and corrosion loss time-varying relationships were log-transformed. Subsequently, four main supervised machine learning (ML) algorithms including Decision Tree (DT), Random Forest (RF), Gradient Boosting (GB), and XGBoost were explored for predicting corrosion loss in different depth-varying marine exposure zones. The GB model showed the best prediction accuracy and generalization ability with MSE, RMSE, MAE, and R2 values of 0.08, 0.43, 0.19, and 0.92, respectively. The spatial and temporal distribution of corrosion loss and zoning map in the seas around China were obtained. According to the corrosion zoning map of the splash zone, the South China Sea has a higher degree of corrosion, particularly in its northwestern region.</div></div>","PeriodicalId":19403,"journal":{"name":"Ocean Engineering","volume":null,"pages":null},"PeriodicalIF":4.6,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142594001","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Survey of AI-driven routing protocols in underwater acoustic networks for enhanced communication efficiency 水下声学网络中人工智能驱动的路由协议调查，以提高通信效率

IF 4.6 2区工程技术

Ocean Engineering Pub Date : 2024-11-06 DOI: 10.1016/j.oceaneng.2024.119606

{"title":"Survey of AI-driven routing protocols in underwater acoustic networks for enhanced communication efficiency","authors":"","doi":"10.1016/j.oceaneng.2024.119606","DOIUrl":"10.1016/j.oceaneng.2024.119606","url":null,"abstract":"<div><div>The high-speed growth of undersea communication networks requires sophisticated routing protocols to deal with challenging underwater conditions including large latencies, limited bandwidths and varying topologies. In this paper, we examine the use of Artificial Intelligence (AI), Machine Learning (ML), Reinforcement Learning (RL) and fuzzy logic to optimize routing protocols for underwater networks. We provide a comprehensive survey of existing AI-based approaches, emphasizing their novelties and constraints underwater.</div><div>To assess the efficiency of these AI-based routing protocols, we carry out extensive simulations across various underwater environments where metrics such as packet delivery ratio, energy consumption, end-to-end delay, and computational efficiency are focused on. The results reveal that AI-aided protocols excel over conventional methods particularly in situations involving complex environmental dynamics as well as resources limitation.</div><div>However, there are practical implementation issues which must be solved before the real-world application of AI-based routing such as hardware constraints, concerns on energy usage , and scalability. This study provides valuable insights into the integration of AI technologies into underwater communication networks, paving the way for more reliable and efficient underwater operations. Our findings contribute to the growing body of knowledge in this field and offer a foundation for future advancements in underwater communication technologies.</div></div>","PeriodicalId":19403,"journal":{"name":"Ocean Engineering","volume":null,"pages":null},"PeriodicalIF":4.6,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142593994","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Data-driven model assessment: A comparative study for ship response determination 数据驱动模型评估：船舶响应测定比较研究

IF 4.6 2区工程技术

Ocean Engineering Pub Date : 2024-11-06 DOI: 10.1016/j.oceaneng.2024.119711

{"title":"Data-driven model assessment: A comparative study for ship response determination","authors":"","doi":"10.1016/j.oceaneng.2024.119711","DOIUrl":"10.1016/j.oceaneng.2024.119711","url":null,"abstract":"<div><div>Several machine learning approaches to determine ship responses via data-driven models have been applied. Input features and parameters used relied on time-series analyses obtained from computational-fluid-dynamics approach. As inputs into the data-driven model, the heave and pitch motions of a ship advancing in irregular, i.e., natural seaway were considered. By using different models in the framework of machine learning, required computation for the associated ship motions may be avoided, thus reducing the computational effort to forecast ship motions. Comparative predictions with numerical simulations revealed that the deep-neural-network method for training in auto-machine-learning instructions yielded the highest accuracy in heave motion, resulting in a non-normalized mean-absolute-error of 0.74, against the corresponding error of 1.07 from numerical computations, whereas the method trained with the tree-based models (the extreme gradient boosting and the Hist gradient boosting regressor) predicted less accurate motions for the tested ship. The model trained with the random forest regressor exhibited an error of 1.10. Numerical simulation based on a field method proved to be the most suitable choice for pitch motion. Despite the few samples available to train the regressors, results demonstrated that the measured data was sufficient to assess the developed data-driven model for ship response determination.</div></div>","PeriodicalId":19403,"journal":{"name":"Ocean Engineering","volume":null,"pages":null},"PeriodicalIF":4.6,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142593997","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Fatigue reliability analysis of floating offshore wind turbines under the random environmental conditions based on surrogate model 基于代用模型的随机环境条件下浮式海上风力涡轮机疲劳可靠性分析

IF 4.6 2区工程技术

Ocean Engineering Pub Date : 2024-11-06 DOI: 10.1016/j.oceaneng.2024.119686

{"title":"Fatigue reliability analysis of floating offshore wind turbines under the random environmental conditions based on surrogate model","authors":"","doi":"10.1016/j.oceaneng.2024.119686","DOIUrl":"10.1016/j.oceaneng.2024.119686","url":null,"abstract":"<div><div>Fatigue reliability analysis is essential for ensuring the safe operation of floating offshore wind turbines (FOWTs) under random wind and wave loads. Traditionally, fatigue assessments are computationally expensive due to the need for numerous numerical simulations. To reduce computational costs, a fatigue reliability analysis method is proposed in the present study by implementing the surrogate model, C-vine copula, and Monte Carlo simulation. The multivariate distribution of environmental conditions is modeled using the C-vine copula and marginal mixed distribution models, while short-term fatigue damages are estimated by the surrogate model. Finally, Monte Carlo simulation is employed to assess the fatigue reliability. The proposed method is applied to evaluate fatigue reliability at three critical locations on a FOWT. Results show that both the back propagation neural network (BPNN) and the Kriging model can accurately predict short-term fatigue damage at various locations. However, the BPNN-based surrogate model is recommended for its lower computationally cost. Furthermore, the proposed method not only assesses the probability of fatigue failure at individual locations but also evaluates system-level fatigue reliability by accounting for correlation between fatigue damage at different locations.</div></div>","PeriodicalId":19403,"journal":{"name":"Ocean Engineering","volume":null,"pages":null},"PeriodicalIF":4.6,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142594003","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Numerical study of the effect of vegetation submerged ratio on turbulence characteristics in sediment-laden flow 植被淹没率对含泥沙水流湍流特性影响的数值研究

IF 4.6 2区工程技术

Ocean Engineering Pub Date : 2024-11-06 DOI: 10.1016/j.oceaneng.2024.119629

{"title":"Numerical study of the effect of vegetation submerged ratio on turbulence characteristics in sediment-laden flow","authors":"","doi":"10.1016/j.oceaneng.2024.119629","DOIUrl":"10.1016/j.oceaneng.2024.119629","url":null,"abstract":"<div><div>This study examines the flow and suspended sediment characteristics in sediment-laden flows under various vegetation submergence ratios (SRs), focusing on the evolution trends of turbulence characteristics with different SRs. The analysis of sediment-laden flow is performed by integrating the drift flux model with a vegetation source term, while the turbulence characteristics are simulated using the <span><math><mrow><mi>k</mi><mo>−</mo><mi>ω</mi></mrow></math></span> SST-IDDES turbulence model. The findings indicated that as the vegetation SR increases, the distributions of turbulent kinetic energy (TKE) and turbulent shear stress (TSS) in vertical and horizontal planes become more intense and intricate, exhibiting more pronounced peaks. Matrix cross-correlation analysis of the vertical TKE and TSS fields reveals a strong negative correlation in most of the same region, which ascends as the SR increases. The horizontal TKE and TSS distributions on both sides show a strong negative correlation. Statistical analysis revealed that higher SRs increase vertical TKE above the canopy but suppress vertical TKE within the canopy, while the transverse TKE intensity remains symmetric but non-uniform. The intensity of TSS also escalates as the SR increases. Vertical TSS distribution exhibits extreme values at the flume bottom and near the canopy top, with near-canopy extremes consistently positioned slightly above the canopy top.</div></div>","PeriodicalId":19403,"journal":{"name":"Ocean Engineering","volume":null,"pages":null},"PeriodicalIF":4.6,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142593998","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Enhanced digital twin framework for real-time prediction of fatigue damage on semi-submersible platforms under long-term multi-sea conditions 增强型数字孪生框架，用于在长期多海条件下实时预测半潜式平台的疲劳损伤

IF 4.6 2区工程技术

Ocean Engineering Pub Date : 2024-11-06 DOI: 10.1016/j.oceaneng.2024.119696

{"title":"Enhanced digital twin framework for real-time prediction of fatigue damage on semi-submersible platforms under long-term multi-sea conditions","authors":"","doi":"10.1016/j.oceaneng.2024.119696","DOIUrl":"10.1016/j.oceaneng.2024.119696","url":null,"abstract":"<div><div>As the development of offshore oil and gas resources progresses into deeper waters, the impact on marine structures becomes increasingly severe, resulting in significant structural damage. This highlights the importance of health monitoring for marine structures. The emergence of digital twin technology in ocean engineering has greatly advanced the development of marine structure monitoring technologies, improving the intelligence and real-time capabilities of structural health monitoring. This paper proposes an innovative data-driven digital twin framework, applied to the real-time fatigue damage prediction of the semi-submersible platform under long-term multi-sea conditions. Notably, this study introduces a novel stress twinning method along with a high-precision post-processing module that combines field monitoring data with high-fidelity simulation model results. This integration establishes a bidirectional connection between the physical structure and its digital counterpart, enabling real-time mapping of structural hotspot stresses and more accurate fatigue damage predictions. The proposed framework was validated on the semi-submersible platform in the South China Sea, and the results proved its practicality and transformative potential in offshore structure management.</div></div>","PeriodicalId":19403,"journal":{"name":"Ocean Engineering","volume":null,"pages":null},"PeriodicalIF":4.6,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142593995","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

High-resolution atlas of extreme wave height and relative risk ratio for US coastal regions 美国沿海地区极端波浪高度和相对风险比高分辨率地图集

IF 4.6 2区工程技术

Ocean Engineering Pub Date : 2024-11-06 DOI: 10.1016/j.oceaneng.2024.119684

{"title":"High-resolution atlas of extreme wave height and relative risk ratio for US coastal regions","authors":"","doi":"10.1016/j.oceaneng.2024.119684","DOIUrl":"10.1016/j.oceaneng.2024.119684","url":null,"abstract":"<div><div>Interest in marine energy development has motivated numerous studies on extreme wave conditions to characterize wave loads and project risks. Metrics on extreme wave conditions, including extreme wave height, are limited in nearshore regions by insufficient spatiotemporal coverage and resolution of wave data. This study estimates 1-, 5- and 50-year return period significant wave heights, and relative-risk-ratios computed by non-dimensionalizing these extreme wave heights with their mean values, for US nearshore regions using 32-year regional SWAN wave hindcasts with spatial resolutions of 200–300 m. The model-derived extreme wave height estimates are systematically biased lower than buoy-derived estimates, but are well correlated enabling simple bias correction to buoy-observations. As wave heights at shallow nearshore sites are physically limited by depth-induced wave breaking, model-derived extreme wave height estimates are replaced with estimates using common empirical models based on breaking depth limits. The corrected high-resolution extreme wave height and relative risk ratio atlas generated herein provides important metrics that support resource characterization for the marine energy industry, including resource and site assessment, and the establishment of upper design limits for device type classification and certification to streamline product line development.</div></div>","PeriodicalId":19403,"journal":{"name":"Ocean Engineering","volume":null,"pages":null},"PeriodicalIF":4.6,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142594002","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Real-time prediction of full-scale ship maneuvering motions at sea under random rudder actions based on BiLSTM-SAT hybrid method 基于 BiLSTM-SAT 混合方法的随机舵动作下全船海上机动运动的实时预测

IF 4.6 2区工程技术

Ocean Engineering Pub Date : 2024-11-06 DOI: 10.1016/j.oceaneng.2024.119664

{"title":"Real-time prediction of full-scale ship maneuvering motions at sea under random rudder actions based on BiLSTM-SAT hybrid method","authors":"","doi":"10.1016/j.oceaneng.2024.119664","DOIUrl":"10.1016/j.oceaneng.2024.119664","url":null,"abstract":"<div><div>The prompt identification and prediction of ship maneuvering motions under random rudder actions are crucial for providing valuable navigation decisions in practical navigations. In this study, a hybrid modeling method (BiLSTM-SAT) combining bidirectional long short-term memory (Bi-LSTM) and scaled dot-product attention (SAT) mechanism is developed to adaptively capture the time-series dynamic features of the ship system with multiple degrees of freedom (DOF) and to predict the full-scale ship maneuvering motion at sea in real time. Firstly, the ability of the identified model by BiLSTM-SAT method to predict the 3-DOF nonstandard maneuvering motion of an unmanned surface vessel (USV) in model scale under random rudder actions is validated. On this basis, utilizing the ship motion data from sea trials, the developed BiLSTM-SAT method is applied to predict the time-series 5-DOF maneuvering motions for a full-scale YUKUN ship under the impacts of environmental disturbances and random rudder actions. The results demonstrate that comparing with the traditional LSTM and back propagation (BP) neural network methods, BiLSTM-SAT method can more accurately and stably predict the full-scale ship maneuvering motions in real time characterized by coupled nonlinearity and stochasticity features under variable environmental impacts and random rudder actions with satisfactory confidence level.</div></div>","PeriodicalId":19403,"journal":{"name":"Ocean Engineering","volume":null,"pages":null},"PeriodicalIF":4.6,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142593996","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0