Ocean Engineering最新文献

{"title":"A Eulerian-Lagrangian method for local scour incorporating a novel sediment initiation","authors":"Xuelian Guo ,&nbsp;Steven N. Ward ,&nbsp;Jiajia Wang ,&nbsp;Lili Xiao ,&nbsp;Wanshui Han","doi":"10.1016/j.oceaneng.2026.124779","DOIUrl":"10.1016/j.oceaneng.2026.124779","url":null,"abstract":"<div><div>Local scour is a primary cause of bridge collapse. Numerical simulation is a widely used approach for predicting local scour. However, existing methods often produce scour hole morphologies that are inconsistent with real-world observations and tend to underestimate the scour depth in front of the pile. To address these shortcomings, this study scalarizes the Reynolds stress as the Deviatoric Strain Rate (<em>DEV</em>), utilizes <em>DEV</em> (<strong><em>r</em></strong>,<em>t</em>) to quantify flow erosion capacity, and adopts the critical <em>DEV</em> (<strong><em>r</em></strong>,<em>t</em>)--<em>DEV</em>_c as the criterion for sediment incipient motion. Building upon the Tsunami Squares (TS) method, we have developed a Eulerian-Lagrangian numerical simulation approach for local scour, termed Local Scour Squares (LSS), which enables the coupled simulation of water, sediment, and structures. Through comparative analysis with the mainstream FLOW-3D software using a typical local scour test case, the LSS method demonstrates superior computational efficiency while maintaining comparable accuracy and convergence. Notably, LSS achieves a better agreement with experimental observations in simulating scour hole morphology. The developed model offers a theoretical foundation for predicting scour and protecting hydraulic structures, including bridge piles, offshore wind turbines, and other submerged installations.</div></div>","PeriodicalId":19403,"journal":{"name":"Ocean Engineering","volume":"353 ","pages":"Article 124779"},"PeriodicalIF":5.5,"publicationDate":"2026-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147386612","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}

{"title":"A risk analysis model for Arctic escort operations based on STPA and Bayesian Networks","authors":"Yang Lu,&nbsp;Yutong Jiang,&nbsp;Yuejun Liu,&nbsp;Baoyu Ni,&nbsp;Yanzhuo Xue","doi":"10.1016/j.oceaneng.2026.124782","DOIUrl":"10.1016/j.oceaneng.2026.124782","url":null,"abstract":"<div><div>This study proposes a risk analysis model integrating Systems-Theoretic Process Analysis (STPA) and Bayesian Networks. The model comprehensively incorporates environmental, vessel, human, and organisational/management factors to assess the risks of collision between the escorted vessel and the icebreaker, collision between the escorted vessel and ice, and besetting of the escorted vessel in ice across the five sea areas along the Northeast Passage. The results demonstrate that the proposed model effectively captures the distribution characteristics of risks associated with icebreaker escort operations across the various sea areas. Consequently, it can serve as a valuable tool for guiding icebreaker service operations and navigational decision-making. Furthermore, this study confirms that August and September represent the optimal navigation period along the Northeast Passage. Compared to the other sea areas, the East Siberian Sea exhibits substantially higher risks during icebreaker escort operations. Ice thickness, ice concentration, navigation experience, emergency response capability, vessel speed, propulsion and manoeuvring system performance, ice condition information services, and emergency response and rescue plans are identified as the primary risk factors influencing icebreaker escort operations in this region.</div></div>","PeriodicalId":19403,"journal":{"name":"Ocean Engineering","volume":"353 ","pages":"Article 124782"},"PeriodicalIF":5.5,"publicationDate":"2026-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147387102","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}

{"title":"Optimization study on a square array of wave energy converters in irregular waves","authors":"Zechen He ,&nbsp;Dezhi Ning ,&nbsp;Ying Gou ,&nbsp;Wenhao Hou ,&nbsp;Yu Zhou ,&nbsp;Wenlin Chen","doi":"10.1016/j.oceaneng.2026.124796","DOIUrl":"10.1016/j.oceaneng.2026.124796","url":null,"abstract":"<div><div>In this study, a wave energy converter (WEC) array is optimized in multi-frequency incident wave conditions. The findings are compared with the results obtained from arrays in single-frequency incident wave conditions, with the aim of determining an array parameter configuration method suitable for irregular waves. Taking a square array composed of four identical cylindrical heaving WECs as an example, four variable parameters are considered, namely the buoy radius and draft, damping of the power take-off (PTO) system, and the spacing between WECs. The differential evolution (DE) algorithm is adopted for multi-parameter optimization. Based on linear potential flow theory, the high-order boundary element method is selected to solve the hydrodynamic interaction between waves and WECs. The results indicate that the array layout obtained in multi-frequency incident waves exhibits good power performance over a wide frequency range, as well as in unidirectional and multi-directional irregular waves, but with high-cost indicators and elevated computational costs. The array obtained by optimizing the spacing between adjacent pre-designed WECs, with large PTO damping and small suboptimal buoy submerged volume, can balance the high power and low cost indicator of the array in irregular waves.</div></div>","PeriodicalId":19403,"journal":{"name":"Ocean Engineering","volume":"353 ","pages":"Article 124796"},"PeriodicalIF":5.5,"publicationDate":"2026-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147386355","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}

{"title":"Data-driven event-triggered heading control for autonomous surface vehicles based on an adaptive observer","authors":"Yihang Zhao ,&nbsp;Yongchao Liu ,&nbsp;Shuaixi Li ,&nbsp;Shubo Wang","doi":"10.1016/j.oceaneng.2026.124669","DOIUrl":"10.1016/j.oceaneng.2026.124669","url":null,"abstract":"<div><div>This paper presents a data-driven event-triggered heading control strategy for autonomous surface vehicles (ASVs) based on an adaptive observer. First, an adaptive observer is constructed by employing heading angle of the ASV, which effectively mitigates the adverse effect deriving from measurement noise. Second, the proposed data-driven heading control strategy only utilizes the observed heading angle and rudder control input data, which does not require any ASV model parameter information. Meanwhile, to address the non-self-balancing characteristic of the ASV heading system, a historical rudder angle compensation term is incorporated into the rudder control criterion function. Third, to enhance the utilization efficiency of network resource, an event-triggered mechanism is constructed by using the measurement error and tracking error from the adaptive observer. Then, the stability of the presented data-driven event-triggered heading strategy has been rigorously verified by using the mathematical analysis. Finally, the simulation experiment validates the effectiveness of the heading control strategy.</div></div>","PeriodicalId":19403,"journal":{"name":"Ocean Engineering","volume":"353 ","pages":"Article 124669"},"PeriodicalIF":5.5,"publicationDate":"2026-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147387247","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}

{"title":"Distributed optimal formation control for UAV-USV multiagent systems: A FEWNN-based RL approach with self-adjusting prescribed performance","authors":"Haitao Liu ,&nbsp;Huiting Huang ,&nbsp;Xuehong Tian ,&nbsp;Qingqun Mai","doi":"10.1016/j.oceaneng.2026.124707","DOIUrl":"10.1016/j.oceaneng.2026.124707","url":null,"abstract":"<div><div>This paper designs a fuzzy Elman wavelet neural network (FEWNN)-based reinforcement learning (RL) optimal controller with self-adjusting prescribed performance for the formation tracking control problem of unmanned aerial vehicle-unmanned surface vehicle (UAV-USV) systems. First, a self-adjusting prescribed performance function (SAPF) is developed to constrain tracking errors and adaptively adjust the error constraint boundaries. This function ensures that tracking errors converge within desired bounds while avoiding singularity issues during formation reconfiguration, addressing the limitations of traditional fixed-boundary prescribed performance control. Second, an FEWNN is proposed to approximate the lumped uncertainty and is embedded into the reinforcement learning strategy for solving the Hamilton-Jacobi-Bellman (HJB) equation. Compared with the traditional radial basis function neural network (RBFNN), the proposed FEWNN incorporates the dynamic memory feature of the Elman neural network, which can effectively enhance the function approximation capability. Third, an RL-based optimal formation controller with self-adjusting prescribed performance is designed. Compared with traditional optimal control methods that lack the prescribed performance constraint, the proposed controller can not only achieve optimal control but also guarantee the steady-state performance and precise trajectory tracking. Finally, the effectiveness of the proposed algorithm is validated through Lyapunov stability theory and numerical simulation analyses.</div></div>","PeriodicalId":19403,"journal":{"name":"Ocean Engineering","volume":"353 ","pages":"Article 124707"},"PeriodicalIF":5.5,"publicationDate":"2026-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147387250","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}

{"title":"Multi-period modeling of floating production storage and offloading dock mooring line tensions using TimesNet-based deep learning framework","authors":"Fangxin Hu ,&nbsp;Xinheng Zhang ,&nbsp;Yao Cheng ,&nbsp;Hongjie Ling ,&nbsp;Peng Dou ,&nbsp;Pei Zhang ,&nbsp;Yihan Xing ,&nbsp;Xiaosen Xu ,&nbsp;Shuaishuai Wang","doi":"10.1016/j.oceaneng.2026.124651","DOIUrl":"10.1016/j.oceaneng.2026.124651","url":null,"abstract":"<div><div>The safety and stability of dock mooring systems for Floating Production Storage and Offloading (FPSO) vessels are critical, as they directly impact dock structural integrity and cargo loading/unloading operations. However, existing research on mooring dynamics in complex marine environments faces several challenges, including inadequate multi-periodicity modeling, neglect of transient impact loads, and limited data fidelity. These challenges mainly arise from the scarcity of long-term field measurements and the uncertainty of environmental conditions, rather than from the limitations of numerical modeling itself. In this study, a high-fidelity dynamic mooring model based on ANSYS-AQWA—calibrated against the real configuration of the terminal project—is employed to generate reliable reference data for training and benchmarking of machine learning models. To address these issues, this study proposes a prediction model based on TimesNet, designed to explicitly capture the multi-periodic characteristics and dynamic coupling effects of mooring tension. A fully coupled numerical model of the FPSO dock mooring system is developed using ANSYS-AQWA (Advanced Quadrature Wave Analysis), generating a high-fidelity dataset that accounts for the combined effects of wind, waves, and currents. The prediction performance of six mainstream models—including Long Short-Term Memory (LSTM), Bidirectional Long Short-Term Memory (BiLSTM), and Complete Ensemble Empirical Mode Decomposition with Adaptive Noise-Convolutional Neural Networks-LSTM-Attention (CEEMDAN-CNN-LSTM-AT) is systematically compared. Results indicate that the TimesNet model outperforms others in ultra-short-term prediction (with a time step of 9s), achieving an R² value of 0.982 for bow mooring line tension forecasting. Under high-tide conditions, increased mooring line tension leads to greater nonlinearity and fluctuation, resulting in higher prediction errors compared to low-tide conditions. Despite larger load fluctuations in single cables within the operational mooring layout, the model maintains high predictive accuracy. The findings of this study provide theoretical support for the adaptive control of FPSO dock mooring winches and preventive maintenance strategies while laying the groundwork for the development of digital twin systems.</div></div>","PeriodicalId":19403,"journal":{"name":"Ocean Engineering","volume":"353 ","pages":"Article 124651"},"PeriodicalIF":5.5,"publicationDate":"2026-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147386762","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}

{"title":"Research on the acoustic field characteristics in the sonar platform area based on novel acoustic black hole barrier panels","authors":"Jiajie Wei ,&nbsp;Yingying Zuo ,&nbsp;Xianzhong Wang ,&nbsp;Wenchao Qi ,&nbsp;Yuechuan Shao ,&nbsp;Zaopeng Dong","doi":"10.1016/j.oceaneng.2026.124708","DOIUrl":"10.1016/j.oceaneng.2026.124708","url":null,"abstract":"<div><div>This study addresses the problem that ship bow's sonar platform is susceptible to interference from propeller noise, mechanical noise, and flow-induced vibration noise of the sonar dome. A novel sound barrier structure based on the Acoustic Black Hole (ABH) effect is proposed and validated. A simplified model of the sonar platform area was established in modeling software, followed by finite element acoustic field simulation. The sound insulation and noise reduction performance of the new and traditional acoustic barriers was compared under two operating conditions: passive detection (diffuse incident mechanical noise) and active detection (phased array directional sound source). Results indicate that the novel acoustic barrier achieves higher sound transmission loss (STL) across the 200-5000Hz frequency range, with peak values of 27 dB in passive mode and 35 dB in active mode, significantly reducing background noise around the sonar array base. In active detection mode, it further enhances directional characteristics of sound pressure levels, minimizes reflection interference from non-target directions, and consequently improves detection accuracy. The study verifies the barrier's excellent sound absorption and insulation, providing theoretical and engineering references for acoustic optimization of ship sonar platforms.</div></div>","PeriodicalId":19403,"journal":{"name":"Ocean Engineering","volume":"353 ","pages":"Article 124708"},"PeriodicalIF":5.5,"publicationDate":"2026-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147386765","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}

{"title":"Underwater acoustic target classification using Fuzzy Inference System with Ensemble Learning","authors":"Qingbo Yin ,&nbsp;Siqi Zhang ,&nbsp;Yunsheng Fan ,&nbsp;Liran Shen","doi":"10.1016/j.oceaneng.2026.124713","DOIUrl":"10.1016/j.oceaneng.2026.124713","url":null,"abstract":"<div><div>Due to the complexity of the marine environment, underwater signals exhibit highly nonlinear characteristics, making underwater acoustic target recognition extremely challenging. Existing methods not only require further improvement in recognition accuracy but also lack interpretability. To address these issues, this paper proposes a novel underwater acoustic target recognition method called the Underwater Acoustic Fuzzy Inference System with ensemble learning (UAFISEL). UAFISEL employs a zero-order fuzzy inference system (ZOFIS) to model nonlinear underwater signals and further boosts the recognition capability through ensemble learning. UAFISEL possesses the following characteristics: (1) It is capable of modeling nonlinear signals from underwater acoustic targets (UAT). (2)It adopts ZOFIS as the base learner (ensemble component) to ensemble a multi-class classifier. (3)It possesses strong generalization capabilities, and works well on small sample datasets. (4)It has excellent interpretability due to the rules of ZOFIS. Experimental results obtained from two publicly-accessible underwater acoustic databases exhibit the capability of UAFISEL to surpass other leading algorithms, encompassing both traditional statistics-based and state-of-the-art learning-based methods. UAFISEL demonstrates a significant performance edge in few-shot learning environments, and possesses enhanced interpretability of its results.</div></div>","PeriodicalId":19403,"journal":{"name":"Ocean Engineering","volume":"353 ","pages":"Article 124713"},"PeriodicalIF":5.5,"publicationDate":"2026-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147386426","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}

{"title":"Automated machine learning-based predictive models for multi-hazard catastrophic risk assessment in offshore wind turbines","authors":"Tanmoy Chatterjee ,&nbsp;Sayan Bhattacharyya ,&nbsp;Suryakanta Biswal ,&nbsp;Subhamoy Bhattacharya","doi":"10.1016/j.oceaneng.2026.124800","DOIUrl":"10.1016/j.oceaneng.2026.124800","url":null,"abstract":"<div><div>Offshore wind turbines (OWTs) operate in harsh and unpredictable environments, where combined effects of wind, waves, and earthquakes produce highly nonlinear structural responses. Although finite element (FE) simulations provide accurate predictions, they are computationally intensive and impractical for large parametric studies or real-time decision-making. This study introduces a data-driven automated machine learning (AutoML) framework to develop predictive models of monopile-supported OWTs under multi-hazard conditions. The proposed methodology establishes mappings between critical input variables such as turbine rating, water depth, wind speed, wave characteristics, and seismic acceleration and key engineering demand parameters, including maximum hub displacement, hub acceleration, and pile-head rotation. To address the substantial variability and skewness inherent in offshore datasets, multiple machine learning algorithms are systematically evaluated, including ensemble methods, Gaussian process regression, multilayer perceptrons, kernel regression, decision trees and support vector machines. The resulting surrogate models achieve rapid and reliable response predictions, offering a practical alternative to computationally demanding FE simulations. Their demonstrated accuracy and robustness support integration into real-time risk assessment and decision-support workflows for high-value offshore assets. Validation on unseen offshore scenarios within the sampled design space further confirms the models’ potential to enhance the efficiency and scalability of offshore structural analysis.</div></div>","PeriodicalId":19403,"journal":{"name":"Ocean Engineering","volume":"353 ","pages":"Article 124800"},"PeriodicalIF":5.5,"publicationDate":"2026-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147386609","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}

{"title":"On the non-breaking solitary wave runup on a smooth slope using Synolakis solution and ballistic model","authors":"Lin Luo,&nbsp;Haijiang Liu","doi":"10.1016/j.oceaneng.2026.124788","DOIUrl":"10.1016/j.oceaneng.2026.124788","url":null,"abstract":"<div><div>In this study, the non-breaking solitary wave uprush process is comprehensively investigated using both the Synolakis (1987) nonlinear solution (S87) and the ballistic model (BM). Based on S87, the time history of shoreline moving velocity exhibits the feature of an initial acceleration (characterized with an increasing then decreasing acceleration) followed by a deceleration. S87 demonstrates that the spatial distribution of the fluid velocity <span><math><mrow><mo>∂</mo><mi>u</mi><mo>/</mo><mo>∂</mo><mi>x</mi><mo>&lt;</mo><mn>0</mn></mrow></math></span> initially, while as swash progresses, position of the spatially maximum flow velocity gradually shifts forward, and <span><math><mrow><mo>∂</mo><mi>u</mi><mo>/</mo><mo>∂</mo><mi>x</mi><mo>&gt;</mo><mn>0</mn></mrow></math></span> comes to dominate in the entire uprush body until the runup maximum. <span><math><mrow><mo>∂</mo><mi>u</mi><mo>/</mo><mo>∂</mo><mi>x</mi><mo>&gt;</mo><mn>0</mn><mspace></mspace><mrow><mo>(</mo><mrow><mo>&lt;</mo><mn>0</mn></mrow><mo>)</mo></mrow></mrow></math></span> indicates that the swash fluid parcel tends to stretch (compress), thus the water surface profile becomes gentle (steep), leading to the decrease (increase) in the shoreline moving acceleration. Furthermore, <span><math><mrow><mo>|</mo><mrow><mo>∂</mo><mi>u</mi><mo>/</mo><mo>∂</mo><mi>x</mi></mrow><mo>|</mo></mrow></math></span> at the moving shoreline is the largest, indicating the most rapid change of the water surface slope. After applying S87, BM could reproduce the initial acceleration of the shoreline movement, which could not be considered in the existing BMs. When describing the shoreline movement, it is confirmed that discrepancies between S87 and BM correlate with the spatial distribution of hydrostatic pressure gradient. Such discrepancies become weaken if the incident wave height decreases or the bed slope increases.</div></div>","PeriodicalId":19403,"journal":{"name":"Ocean Engineering","volume":"353 ","pages":"Article 124788"},"PeriodicalIF":5.5,"publicationDate":"2026-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147386610","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}