Ocean Engineering最新文献

Evolution analysis of key mechanical properties during the forming process of JCOE and JCOC pipes and its influence on collapse failure JCOE和JCOC管材成形过程关键力学性能演化分析及其对坍塌破坏的影响

IF 4.6 2区工程技术

Ocean Engineering Pub Date : 2025-05-31 DOI: 10.1016/j.oceaneng.2025.121684

Shaocong Qi , Guoyi Shen , Xihai Liu , Wanlin Cheng , Ruixue Zhai , Qingdang Meng , Jun Zhao , Gaochao Yu

{"title":"Evolution analysis of key mechanical properties during the forming process of JCOE and JCOC pipes and its influence on collapse failure","authors":"Shaocong Qi , Guoyi Shen , Xihai Liu , Wanlin Cheng , Ruixue Zhai , Qingdang Meng , Jun Zhao , Gaochao Yu","doi":"10.1016/j.oceaneng.2025.121684","DOIUrl":"10.1016/j.oceaneng.2025.121684","url":null,"abstract":"<div><div>The LSAW pipes are main trunk pipes for offshore oil and gas energy development. Enhancing the buckling resistance performance of submarine pipes and accurately evaluating its collapse pressure are research priorities. This study analyzes for the first time the evolution of residual stress and compressive strength during the full-process forming of JCOE and JCOC pipes, and reveals the influence mechanism of mechanical properties on collapse. The yield plateau phenomenon and variable elastic modulus behavior of X65 steel were described by modifying Chaboche model. A theoretical model for the evolution of mechanical properties during JCO forming, expansion, and compression processes were established. The full-process forming and collapse simulations were conducted. The experimental and theoretical yield stresses have good consistency. The calibration ratio and <em>t</em>/<em>D</em> ratio are key factors affecting the yield stress and residual stress. Compared with JCOE pipes, JCOC pipes have lower residual stress but higher compressive strength. Within calibration ratio range of ±0.4 % to ±1.0 %, residual stress is the main factor affecting collapse. As the calibration ratio increases, residual stress improves and compressive yield stress becomes a key factor. This study is of great significance for the forming and buckling analysis of pipelines.</div></div>","PeriodicalId":19403,"journal":{"name":"Ocean Engineering","volume":"335 ","pages":"Article 121684"},"PeriodicalIF":4.6,"publicationDate":"2025-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144178225","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

Three-dimensional simulation of wave-induced seabed liquefaction in the Chengdao area 埕岛地区海底波浪液化的三维模拟

IF 4.6 2区工程技术

Ocean Engineering Pub Date : 2025-05-31 DOI: 10.1016/j.oceaneng.2025.121648

Youqi Wang , Shengyi Jiao , Haibo Chen , Ruichen Cao , Xianqing Lv

引用次数: 0

Supervised visual docking network for unmanned surface vehicles using auto-labeling in real-world water environments 基于自动标记的无人水面车辆监督视觉对接网络

IF 4.6 2区工程技术

Ocean Engineering Pub Date : 2025-05-31 DOI: 10.1016/j.oceaneng.2025.121609

Yijie Chu , Ziniu Wu , Yong Yue , Eng Gee Lim , Paolo Paoletti , Xiaohui Zhu

{"title":"Supervised visual docking network for unmanned surface vehicles using auto-labeling in real-world water environments","authors":"Yijie Chu , Ziniu Wu , Yong Yue , Eng Gee Lim , Paolo Paoletti , Xiaohui Zhu","doi":"10.1016/j.oceaneng.2025.121609","DOIUrl":"10.1016/j.oceaneng.2025.121609","url":null,"abstract":"<div><div>Unmanned Surface Vehicles (USVs) are increasingly applied to water operations such as environmental monitoring and river-map modeling. However, precise autonomous docking at ports or stations remains a significant challenge, often relying on manual control or external positioning systems, which severely limits fully autonomous deployments. In this paper, we propose a novel supervised learning framework featuring an auto-labeling pipeline to enable USVs autonomous visual docking. The primary innovation lies in our automated data collection pipeline, which directly provides paired relative pose data and corresponding images, eliminating the conventional need for manual labeling, such as tagging bounding boxes. We introduce the Neural Dock Pose Estimator (NDPE), capable of accurately predicting the relative dock pose without relying on traditional methods such as handcrafted feature extraction, camera calibration, or peripheral markers. Unlike common bounding-box-based detection algorithms (e.g., Yolo-like methods), our NDPE explicitly predicts the relative pose transformation between the camera frame and USV body frame, significantly simplifying the data annotation and training process. Additionally, the generality of our data collection pipeline allows integration with various neural network architectures, ensuring broad applicability beyond the specific architecture demonstrated here. Experimental validation in real-world water environments demonstrates that NDPE robustly handles variations in docking distances and USV velocities, ensuring accurate and stable autonomous docking performance. The effectiveness and practicality of our approach are clearly verified through extensive experiments. The dataset, tutorial and experimental videos for this project are publicly available at: <span><span>https://sites.google.com/view/usv-docking/home</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":19403,"journal":{"name":"Ocean Engineering","volume":"335 ","pages":"Article 121609"},"PeriodicalIF":4.6,"publicationDate":"2025-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144184728","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

Axial and normal pullout tests of chains in soft clay 软粘土中锚链轴向和法向拉拔试验

IF 4.6 2区工程技术

Ocean Engineering Pub Date : 2025-05-31 DOI: 10.1016/j.oceaneng.2025.121698

Naloan Coutinho Sampa , Fernando Schnaid , Marcelo Maia Rocha , Roberto Cudmani

{"title":"Axial and normal pullout tests of chains in soft clay","authors":"Naloan Coutinho Sampa , Fernando Schnaid , Marcelo Maia Rocha , Roberto Cudmani","doi":"10.1016/j.oceaneng.2025.121698","DOIUrl":"10.1016/j.oceaneng.2025.121698","url":null,"abstract":"<div><div>Mooring systems are vital for stabilizing floating structures, where chain-soil interactions play a critical role in anchor performance. Accurate prediction of inverse catenary profiles, anchor trajectories, and load attenuation relies on a detailed understanding of these interactions. This study investigated the mechanisms of soil-chains or soil-steel plates interactions through axial and normal pullout tests on scale-reduced models in soft clay, conducted under undrained conditions. The load-displacement curves revealed three phases: linear-viscoelastic, strain softening, and residual, influenced by chain geometry and soil properties. Key parameters, including strain softening degree, displacement at peak resistance, bearing capacity factor, effective widths in bearing and sliding, and friction coefficient, were quantified and compared with values reported in the literature. The bearing capacity factor ranged from 7.05 to 12.87, while the effective width in bearing varied from 2.0 to 3.7, depending on the bearing area. The effective width in sliding was 9.34 for welded chains and 9.80 for non-welded chains, with friction coefficients (<span><math><mrow><mi>μ</mi></mrow></math></span>) ranging from 0.32 to 0.39, depending on chain stiffness and embedment direction. Strain softening caused resistance reduction of 10 % for welded chains, 13 % for 12.7 mm steel plates, and 38–56 % for 25.45 mm steel plates in normal pullout tests. The normalized displacement at peak resistance generally increased with normalized length, except for welded chains in axial pullout tests. These findings contribute to a deeper understanding of chain-soil interaction mechanisms and provide practical insights for predicting tangential and normal soil resistances, as well as inverse catenary profiles, in mooring system design.</div></div>","PeriodicalId":19403,"journal":{"name":"Ocean Engineering","volume":"335 ","pages":"Article 121698"},"PeriodicalIF":4.6,"publicationDate":"2025-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144178236","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

Localized Trefftz method for wave generation and propagation in a two-dimensional numerical wave tank 二维数值波槽中波浪产生和传播的局部Trefftz方法

IF 4.6 2区工程技术

Ocean Engineering Pub Date : 2025-05-31 DOI: 10.1016/j.oceaneng.2025.121670

Xiran Lin , Liangbin Xu , Yan-Cheng Liu , Chia-Ming Fan

{"title":"Localized Trefftz method for wave generation and propagation in a two-dimensional numerical wave tank","authors":"Xiran Lin , Liangbin Xu , Yan-Cheng Liu , Chia-Ming Fan","doi":"10.1016/j.oceaneng.2025.121670","DOIUrl":"10.1016/j.oceaneng.2025.121670","url":null,"abstract":"<div><div>This study proposes a meshless numerical method for nonlinear free surface wave propagation, known as the localized Trefftz method (LTM). The method employs the fundamental solution of the Laplace equation as T-complete functions and integrates the explicit Euler method, semi-Lagrangian method, ramp functions, and sponge layers to construct a numerical wave flume, enabling accurate and efficient simulation of wave generation and propagation. Based on potential flow theory, wave propagation is formulated as a time-dependent boundary value problem governed by the Laplace equation for velocity potential and two nonlinear free surface boundary conditions. To absorb wave energy and prevent reflection, a sponge layer is implemented at the end of the flume. Initially, LTM is applied to simulate the generation of finite-amplitude, high-steepness waves at the wave generation end. Subsequently, it is used to model the propagation of regular waves over underwater obstacles, incorporating an exponential decay function in the sponge layer to further mitigate wave reflection at the flume's end. As LTM does not require global mesh generation, it is computationally efficient and particularly suitable for moving boundary problems. The accuracy and stability of the proposed method are validated through comparative analyses of four numerical examples against existing numerical results.</div></div>","PeriodicalId":19403,"journal":{"name":"Ocean Engineering","volume":"335 ","pages":"Article 121670"},"PeriodicalIF":4.6,"publicationDate":"2025-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144184726","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

Integrating detailed power take-off system models in wave energy converter simulations using an FMI-based co-simulation approach 利用基于fmi的联合仿真方法集成波能转换器仿真中详细的功率输出系统模型

IF 4.6 2区工程技术

Ocean Engineering Pub Date : 2025-05-31 DOI: 10.1016/j.oceaneng.2025.121651

Xinyuan Shao , Jan Forsberg , Jonas W. Ringsberg

{"title":"Integrating detailed power take-off system models in wave energy converter simulations using an FMI-based co-simulation approach","authors":"Xinyuan Shao , Jan Forsberg , Jonas W. Ringsberg","doi":"10.1016/j.oceaneng.2025.121651","DOIUrl":"10.1016/j.oceaneng.2025.121651","url":null,"abstract":"<div><div>The power take-off (PTO) system is a core component of a wave energy converter (WEC) that significantly influences its power performance. Careful design and testing of PTO systems are essential in WEC development. Numerical simulations have largely replaced physical testing, accelerating the PTO system design process. However, one major obstacle still prevents numerical modelling of the PTO system from achieving its full capability: integrating the PTO system model into the global simulation of the WEC system to include the effects of all subsystems. This paper uses a co-simulation approach based on the Functional Mock-Up Interface (FMI) standard to integrate a detailed PTO system model into a global WEC model that includes hydrodynamic, mechanical, and mooring subsystems. The approach is compared against a model incorporating a simplified linear-damper PTO system. The results indicate that a higher-fidelity PTO system model can have a significant impact on predictions of WEC motion, mooring fatigue damage accumulation, and power performance. For example, it is observed that a simplified PTO model can lead to a tenfold underestimation of mooring fatigue damage under some environmental conditions.</div></div>","PeriodicalId":19403,"journal":{"name":"Ocean Engineering","volume":"335 ","pages":"Article 121651"},"PeriodicalIF":4.6,"publicationDate":"2025-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144178235","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

Interpretable prediction of floating offshore wind turbine dynamic Responses: An attention-based deep learning approach 浮式海上风力发电机动态响应的可解释预测：一种基于注意力的深度学习方法

IF 4.6 2区工程技术

Ocean Engineering Pub Date : 2025-05-31 DOI: 10.1016/j.oceaneng.2025.121703

Byungho Kang, Semyung Park, Daeyong Kwon

{"title":"Interpretable prediction of floating offshore wind turbine dynamic Responses: An attention-based deep learning approach","authors":"Byungho Kang, Semyung Park, Daeyong Kwon","doi":"10.1016/j.oceaneng.2025.121703","DOIUrl":"10.1016/j.oceaneng.2025.121703","url":null,"abstract":"<div><div>This study develops, for the first time, an interpretable prediction framework for Floating Offshore Wind Turbine (FOWT) dynamic responses, focusing on mooring line tension and tower-top acceleration. The architecture combines a simplified attention mechanism with a Multi-Layer Perceptron (MLP), using a sliding window approach on time-series data generated from OpenFAST simulations of a 22 MW IEA reference turbine. The simulations represent diverse operational conditions, including normal operation and parked scenarios, with varying wind and wave environments. The model achieves high predictive accuracy, essential for reliable interpretation of the attention mechanism. Also, the analysis reveals that the model attends to different time scales: 2.5 s for mooring tension and 1.25 s for tower-top acceleration. Key input features are consistently prioritized, with their relative importance dynamically adjusted based on the operational state. Mooring tension prediction relies heavily on tower-base forces, lower-tower accelerations, and platform motions, whereas tower-top acceleration is primarily influenced by tower-top moments, upper-tower forces and accelerations, and rotor thrust. Validation using standard MLPs confirms that the high-attention features (7) provide better predictions than low-attention features (12). This interpretable model offers potential insights for ocean engineering, including guiding sensor placement, informing structural health monitoring, and contributing to control system design.</div></div>","PeriodicalId":19403,"journal":{"name":"Ocean Engineering","volume":"335 ","pages":"Article 121703"},"PeriodicalIF":4.6,"publicationDate":"2025-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144177807","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

An interpretable ship risk model based on machine learning and SHAP interpretation technique 基于机器学习和SHAP解释技术的可解释船舶风险模型

IF 4.6 2区工程技术

Ocean Engineering Pub Date : 2025-05-31 DOI: 10.1016/j.oceaneng.2025.121686

Zhizheng Wu , Shengzheng Wang , Leyao Li , Yongfeng Suo

{"title":"An interpretable ship risk model based on machine learning and SHAP interpretation technique","authors":"Zhizheng Wu , Shengzheng Wang , Leyao Li , Yongfeng Suo","doi":"10.1016/j.oceaneng.2025.121686","DOIUrl":"10.1016/j.oceaneng.2025.121686","url":null,"abstract":"<div><div>The widespread application of artificial intelligence has injected new momentum into the development of ship risk models. By utilizing machine learning techniques, these models can fully mine maritime navigation data, thereby improving their accuracy and applicability. However, machine learning models often have a ‘black box’ characteristic that makes the decision-making process difficult to understand intuitively. To address this issue, this paper proposed an interpretable ship risk model based on machine learning and SHAP interpretation technique. The method is built upon weather data and ship accident data to construct a machine learning-based risk prediction model. Subsequently, the SHAP attribution method is introduced for interpretability analysis of the model, quantifying the impact of each feature variable on the model's output and identifying key risk factors. Finally, based on the analysis results, the model's features are optimized, retaining only the most critical features that contribute to the model's prediction. The results show that in the four areas studied, LSTM shows high stability in recall, with average volatility of 0.77 %, 0.21 %, 0.04 % and 0.29 % respectively, and XGBoost has high stability in precision index, which is 0.02 %, 0.04 %, 0.03 % and 0.03 % respectively. It can be seen that the method enables accurate evaluation of ship risk levels, provides precise analysis of the influence of each feature on the model, and reduces the number of features while ensuring prediction accuracy, thereby effectively reducing the model's computational cost and enhancing its overall efficiency and practicality.</div></div>","PeriodicalId":19403,"journal":{"name":"Ocean Engineering","volume":"335 ","pages":"Article 121686"},"PeriodicalIF":4.6,"publicationDate":"2025-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144177806","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

Deep learning-based waveform forecasting and physical simulations for volcano collapse tsunami early warning: The Anak Krakatau case 基于深度学习的火山崩塌海啸预警波形预测和物理模拟：喀拉喀托火山案例

IF 4.6 2区工程技术

Ocean Engineering Pub Date : 2025-05-30 DOI: 10.1016/j.oceaneng.2025.121683

E. Juanara, C.Y. Lam

{"title":"Deep learning-based waveform forecasting and physical simulations for volcano collapse tsunami early warning: The Anak Krakatau case","authors":"E. Juanara, C.Y. Lam","doi":"10.1016/j.oceaneng.2025.121683","DOIUrl":"10.1016/j.oceaneng.2025.121683","url":null,"abstract":"<div><div>Tsunamis triggered by volcanic collapses are high-impact, low-probability events that present unique challenges for disaster preparedness. The December 2018 Anak Krakatau Volcano (AKV) event, caused by flank instability, resulted in a catastrophic tsunami that struck without warning, leading to hundreds of fatalities. While research on volcanic tsunamis, such as numerical simulations and modelling, are ongoing, early warning systems remain underexplored. Existing systems primarily focus on earthquake-generated tsunamis, leaving a gap in addressing volcanic-specific dynamics. To bridge this gap, we propose a tsunami waveform forecasting method for volcanic collapse events, leveraging Long Short-Term Memory (LSTM) networks, specifically Vanilla and Bidirectional architectures, a deep learning approach optimized for short-term window inputs. The database was generated using physical simulation with enhanced parameters to improve upon previous studies. Our approach uses the first 3 min initial waveform to predict the remaining tsunami waveform, offering a novel solution for real-time early warning. Experiments conducted on multiple synthetic stations demonstrate that the proposed method achieves reliable forecast accuracy across 1000 scenarios. This study underscores the potential of AI-driven models to advance early warning systems, enhancing coastal resilience and risk reduction.</div></div>","PeriodicalId":19403,"journal":{"name":"Ocean Engineering","volume":"335 ","pages":"Article 121683"},"PeriodicalIF":4.6,"publicationDate":"2025-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144166153","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

Assessing misalignment effects on undrained HV capacity of caisson anchors in heterogeneous clays using a gradient Boosting–Differential evolution framework 利用梯度升压-差分演化框架评估非均质粘土中沉箱锚杆不排水高压承载力的错位效应

IF 4.6 2区工程技术

Ocean Engineering Pub Date : 2025-05-30 DOI: 10.1016/j.oceaneng.2025.121646

Katavut Vichai , Duy Tan Tran , Jim Shiau , Suraparb Keawsawasvong , Pitthaya Jamsawang

{"title":"Assessing misalignment effects on undrained HV capacity of caisson anchors in heterogeneous clays using a gradient Boosting–Differential evolution framework","authors":"Katavut Vichai , Duy Tan Tran , Jim Shiau , Suraparb Keawsawasvong , Pitthaya Jamsawang","doi":"10.1016/j.oceaneng.2025.121646","DOIUrl":"10.1016/j.oceaneng.2025.121646","url":null,"abstract":"<div><div>Misalignment is a critical factor influencing the horizontal-vertical (HV) load capacity of caisson foundation anchors. This study employs Finite Element Limit Analysis (FELA) integrated with a Gradient Boosting–Differential Evolution (GB-DE) framework to systematically investigate the impact of misalignment angles (<em>β</em> = 0°–90°) across varying embedment ratios (<em>L/D</em>) and soil heterogeneity levels (<em>κ</em>). Results reveal that the HV capacity increases with the load inclination angle <em>β</em>. This effect is amplified with greater embedment depth and influenced by the degree of soil heterogeneity, highlighting the complex interaction between geometric and geotechnical parameters. The findings demonstrate that misalignment can enhance soil-structure interaction, leading to increased resistance capacity. The GB-DE model achieves high predictive accuracy (R<sup>2</sup> = 0.999), highlighting its effectiveness in optimizing caisson anchor performance under misaligned conditions. These insights emphasize the importance of incorporating misalignment considerations into anchor design to improve load-bearing efficiency and structural stability.</div></div>","PeriodicalId":19403,"journal":{"name":"Ocean Engineering","volume":"335 ","pages":"Article 121646"},"PeriodicalIF":4.6,"publicationDate":"2025-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144166154","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