Ocean Engineering最新文献

{"title":"Spatio-temporal reconstruction of weak-texture wave fields using thermal stereo vision with active thermal texturing","authors":"Lin Yuan,&nbsp;Handi Wei,&nbsp;Longfei Xiao,&nbsp;Deyu Li,&nbsp;Meng Shan","doi":"10.1016/j.oceaneng.2026.127387","DOIUrl":"10.1016/j.oceaneng.2026.127387","url":null,"abstract":"<div><div>Accurate measurement of weak-texture wave fields in laboratory experiments remains challenging due to the scarcity of natural visual features, which can compromise stereo correspondence and three-dimensional reconstruction. This study proposed a thermal stereo vision method for spatio-temporal reconstruction of weak-texture wave fields, based on a visual enhancement strategy that introduced active thermal texturing. A small amount of hot water was gently applied to the free surface, generating controllable temperature gradients that produced dense and spatially continuous thermal patterns without using physical tracers or dyes. These enhanced features enabled reliable correspondence estimation, which was further stabilized using a normalized cross-correlation (NCC)-based stereo matching method that improved matching robustness and disparity continuity under weak-texture infrared imaging conditions. Focused waves with varying steepnesses and periods were generated in a wave flume to evaluate the feasibility and accuracy of the method. Wave time series extracted from these reconstructions showed strong agreement with resistive wave gauge measurements, with period errors below 2%, amplitude errors within 8%, and coefficients of determination exceeding 0.95. The proposed approach provides a non-contact, full-field measurement tool for capturing the spatial structure and temporal evolution of weak-texture focused-wave fields under the tested laboratory conditions.</div></div>","PeriodicalId":19403,"journal":{"name":"Ocean Engineering","volume":"365 ","pages":"Article 127387"},"PeriodicalIF":6.3,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148952699","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":"Corrigendum to “Hybrid analysis of causal factors of marine accidents in ports” [Ocean Eng. 361 (2026) 126209]","authors":"Hüseyin Tolga Sanal,&nbsp;Fatih Emre Boran,&nbsp;Serdar Yıldız,&nbsp;Özkan Uğurlu,&nbsp;Xinjian Wang,&nbsp;Jin Wang","doi":"10.1016/j.oceaneng.2026.127628","DOIUrl":"10.1016/j.oceaneng.2026.127628","url":null,"abstract":"","PeriodicalId":19403,"journal":{"name":"Ocean Engineering","volume":"365 ","pages":"Article 127628"},"PeriodicalIF":6.3,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149003975","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}

{"title":"Load-transfer evolution and redistribution in instrumented large-diameter bored piles in coastal sand–clay–rock strata: Roles of post-grouting and pile geometry","authors":"Jichao Yin,&nbsp;Xiaoyu Bai,&nbsp;Yamei Zhang,&nbsp;Nan Yan,&nbsp;Yu Cong,&nbsp;Mingyi Zhang,&nbsp;Songkui Sang","doi":"10.1016/j.oceaneng.2026.127465","DOIUrl":"10.1016/j.oceaneng.2026.127465","url":null,"abstract":"<div><div>For large-diameter bored piles in coastal and nearshore infrastructure, pile-head settlement and bearing capacity do not fully capture the effects of shaft post-grouting and pile geometry. In layered soil–rock strata, their influence extends to how axial load is mobilized and redistributed with depth. This study presents full-scale compression tests on six instrumented piles, comprising post-grouted and ungrouted piles, diameters (<em>D</em>) of 0.8 and 1.0 m, and rock-socket depths (<em>h</em>). Axial force, pile–ground relative displacement, shaft resistance, near-toe load and stratum-based load sharing were examined. Load transfer varied with stratigraphy and loading stage. Post-grouted piles mobilized higher shaft resistance at smaller displacement, with persistent differences in the upper sand, differences emerging mainly at higher loads in the middle transition zone, and limited differences in weathered rock. The contribution of the overlying soil strata increased from 41.1% to 62.1%, while the deep weathered-rock contribution decreased from 58.9% to 37.9%. The 1.0 m group showed greater weathered-rock participation than the 0.8 m group, while larger <em>h</em>/<em>D</em> was associated with farther load-transfer development toward the lower socket and near-toe region. These results show that post-grouting and pile geometry affect interconnected parts of the depth-wise load-transfer process under the tested conditions.</div></div>","PeriodicalId":19403,"journal":{"name":"Ocean Engineering","volume":"365 ","pages":"Article 127465"},"PeriodicalIF":6.3,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148952700","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":"Nonlinear coupled dynamic response of TLP wind turbine with redundant mooring under complex environmental loads and multi-tendon failures","authors":"Mingyang Wei,&nbsp;Jianhua Zhang,&nbsp;Ke Sun,&nbsp;Yian Chen,&nbsp;Zaibin Lin,&nbsp;Yan Geng,&nbsp;Hongwen Liu,&nbsp;Yanghua Ou","doi":"10.1016/j.oceaneng.2026.127565","DOIUrl":"10.1016/j.oceaneng.2026.127565","url":null,"abstract":"<div><div>The nonlinear coupled hydrodynamic performance of floating wind systems under extreme sea loads is strongly affected by tendon stiffness degradation and failure-induced restoring-force redistribution. Tendon failure can threaten the stability and safety of tension-leg platform (TLP) wind turbines, particularly in non-redundant mooring configurations. This study investigates the NREL 5 MW wind turbine mounted on the UMaine-TLP platform and proposes a redundant tendon system to separate the platform natural frequencies from the rotor excitation range. A fully coupled aero-hydro-servo-control dynamic model is established in SESAM and validated against OpenFAST. The effects of wind-wave-current misalignment and single- and double-tendon failure on platform motions, tendon tension redistribution, and transient responses are examined under operating and shutdown conditions. Results show that the redundant tendon system maintains dynamic stability after tendon failure. Tendon failure has limited influence on horizontal motion but significantly amplifies rotational responses owing to stiffness degradation and asymmetric restoring-force redistribution. Under operating conditions, windward tendon failure increases resultant horizontal displacement by only 12.91%, whereas the maximum rotational response increases by more than 100%. Adjacent tendon tensions reach 2.06 and 2.12 times the intact values under operating and shutdown conditions, respectively, while the minimum safety factor remains 1.95.</div></div>","PeriodicalId":19403,"journal":{"name":"Ocean Engineering","volume":"365 ","pages":"Article 127565"},"PeriodicalIF":6.3,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148952698","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":"Gain scheduling-based course-keeping control for ships in rough seas","authors":"Guoshuai Li,&nbsp;Weikang Zhang,&nbsp;Shimiao Wang,&nbsp;Xianku Zhang","doi":"10.1016/j.oceaneng.2026.127586","DOIUrl":"10.1016/j.oceaneng.2026.127586","url":null,"abstract":"<div><div>Fixed-parameter controllers suffer from poor course-keeping accuracy under dynamic ship speed in rough seas, as maneuvering indices fluctuate in real time. To address this issue, an adaptive PID control strategy based on real-time speed mapping is proposed. Focusing on the speed variation characteristics under wave-added resistance, a dynamic functional relationship between Nomoto model parameters and real-time ship speed is established, revealing the dynamic response law of maneuverability under speed changes. A ship speed-<em>KT</em> index feedforward loop is constructed to estimate maneuvering indices in real time, and the scheduling law for PID parameters that adaptively optimizes with speed is derived. This method achieves online adaptive tracking of controller parameters to speed variations. Simulation results show that, compared with the nonlinear compound function embedded proportional-derivative closed-loop gain shaping algorithm, the proposed controller significantly reduces the maximum course deviation during the steady-state keeping phase—from 16.2° to 3.6°. This resolves the parameter mismatch issue caused by speed variation, significantly improves course-keeping accuracy, and provides a highly reliable engineering solution for intelligent ship course control.</div></div>","PeriodicalId":19403,"journal":{"name":"Ocean Engineering","volume":"365 ","pages":"Article 127586"},"PeriodicalIF":6.3,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148935918","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":"Hybrid machine learning framework for predicting soil-caisson interaction under seismic loading using XGBoost and xLSTM","authors":"Sohail Ahmad,&nbsp;Tianbo Peng,&nbsp;Athar Zaman,&nbsp;Hisham Alabduljabbar,&nbsp;Mohammed Jameel,&nbsp;Hassan Ayaz","doi":"10.1016/j.oceaneng.2026.127618","DOIUrl":"10.1016/j.oceaneng.2026.127618","url":null,"abstract":"<div><div>The soil-caisson interaction under seismic loading is an important consideration in design and safety assessment of deep foundation systems especially in seismic prone areas. Traditional finite element method (FEM) and boundary element method (BEM) cannot accurately simulate the time-dependent and nonlinear characteristics of soil-caisson systems under dynamic loading, primarily due to the need for making assumptions and using high calculation accuracy when taking into account the fully nonlinear characteristics of soil. To solve these problems, a three-step approach is developed in this study: (1) Establishment of the finite element model of soil-caisson interaction in ABAQUS; (2) validation and verification of the finite element model using shaking table test data, and (3) training and optimization of a hybrid machine learning framework, combining Extreme Gradient Boosting (XGBoost) with eXtreme Long Short-Term Memory (xLSTM) networks with Particle Swarm Optimization (PSO). This framework integrates the effectiveness of ensemble learning and deep learning for temporal modeling to forecast seismic responses like acceleration and stress distribution. Both the hybrid models were found to be highly successful in reproducing the dynamic soil-caisson behavior with both visual and statistical comparisons. The proposed framework was found to have better prediction accuracy and computational efficiency than the FEM and BEM approaches. The results provide insights into the promise of combining numerical modeling with experimental testing and advanced machine learning approaches to improve seismic design, risk assessment, and infrastructure resilience in geotechnical earthquake engineering.</div></div>","PeriodicalId":19403,"journal":{"name":"Ocean Engineering","volume":"365 ","pages":"Article 127618"},"PeriodicalIF":6.3,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148952697","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}

{"title":"Unsupervised expectancy-based anomaly detection in maritime vessel encounters","authors":"Berk Adali,&nbsp;Kaan Buyukkivanc,&nbsp;Ceren Besiktas,&nbsp;Mateusz Gil,&nbsp;Jakub Montewka,&nbsp;Przemysław Krata,&nbsp;Yigit Can Altan","doi":"10.1016/j.oceaneng.2026.127467","DOIUrl":"10.1016/j.oceaneng.2026.127467","url":null,"abstract":"<div><div>Vessel encounters that evolve in unexpected patterns present a critical safety challenge in busy maritime waterways, yet most detection methods assess geometric proximity at a single moment, without tracking how the interaction evolves or what is regionally normal. An encounter is classified as abnormal when its temporal evolution, how two vessels approach over time, deviates from what is behaviorally normal for that part of the waterway.</div><div>This study proposes an unsupervised framework that uses linear regression to measure how consistently two vessels converge and how their courses evolve over time, combining these into a Behavioral Index of encounter irregularity, a continuous behavioral descriptor against which cluster-specific anomaly thresholds are applied. Rather than applying a single global threshold, which conflates behaviorally distinct regions, the framework partitions the waterway into homogeneous regions via the SKATER algorithm, evaluating each encounter against its local norm.</div><div>Applied to AIS data from the Strait of Istanbul over 30–92-day periods, the framework identifies 4.5%–11.5% of encounters as anomalous by region, consistently across temporal scales. Unlike prior methods, it simultaneously identifies both erratic and unusually stable encounters, providing vessel traffic service operators with a behavioral context layer that geometric risk metrics alone cannot supply.</div></div>","PeriodicalId":19403,"journal":{"name":"Ocean Engineering","volume":"365 ","pages":"Article 127467"},"PeriodicalIF":6.3,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148935917","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":"Physics-informed spatial-temporal graph attention network for vessel traffic flow prediction","authors":"Jinxin Wu,&nbsp;Yuxiang Zhang,&nbsp;Zexian Wei","doi":"10.1016/j.oceaneng.2026.127175","DOIUrl":"10.1016/j.oceaneng.2026.127175","url":null,"abstract":"<div><div>Accurate vessel traffic flow prediction (VTFP) is essential for improving navigation safety, waterborne transportation efficiency, and port scheduling reliability. However, existing data-driven methods often show limited performance because of they insufficiently capture multi-scale spatial-temporal dependencies and rarely incorporate physical traffic-flow evolution laws. To address these limitations, a physics-informed spatial-temporal graph attention network (PISTGAN) is proposed for VTFP. Specifically, a functional similarity graph is first constructed using dynamic time warping to describe correlations among waterway regions. An adaptive multi-scale graph attention module is then developed to learn hourly, daily, and monthly scale spatial dependencies, while an adaptive bidirectional temporal convolution module captures forward and backward temporal dynamics. Furthermore, physical features derived from the traffic-flow conservation law are embedded into the model to enhance its consistency with vessel traffic dynamics. Experiments on two real-world inland vessel traffic flow datasets from the Xijiang River and the Yangtze River show that PISTGAN outperforms traditional models and advanced spatial-temporal graph neural networks in terms of MAE, MAPE, and RMSE. Ablation results further confirm the effectiveness of the multi-scale spatial learning, bidirectional temporal modeling, and physics-informed embedding modules. The proposed method provides a novel and reliable solution for intelligent waterborne traffic management and port operation optimization.</div></div>","PeriodicalId":19403,"journal":{"name":"Ocean Engineering","volume":"363 ","pages":"Article 127175"},"PeriodicalIF":6.3,"publicationDate":"2026-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148731359","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":"Performance and mechanism of natural gas-assisted alternating flooding in heavy oil reservoirs","authors":"Yifei Gao,&nbsp;Jinxiang Liu,&nbsp;Xianpei Yin,&nbsp;Haoyu Yuan,&nbsp;Xiaoyan Wang,&nbsp;Wei Wang,&nbsp;Xiangguo Lu,&nbsp;Baogang Zhao,&nbsp;Zilong Liu","doi":"10.1016/j.oceaneng.2026.127193","DOIUrl":"10.1016/j.oceaneng.2026.127193","url":null,"abstract":"<div><div>Conventional waterflooding faces severe challenges in enhancing oil recovery for high water-cut heavy oil reservoirs. In this work, a 200 cm long-core physical simulation system equipped with multiple pressure monitoring points was adopted to investigate fluid flow characteristics and displacement mechanisms of gas-chemical composite flooding. We comparatively evaluated the oil displacement performance of natural gas-assisted and viscosity reducer-assisted polymer-surfactant binary systems under continuous and multi-round alternating injection modes. Dynamic changes of injection pressure, water cut, produced oil composition, crude oil viscosity and interfacial properties were systematically analyzed to reveal the synergistic mass transfer and enhanced oil recovery (EOR) mechanisms. The experimental results show that the natural gas-assisted scheme achieves better EOR performance in all test groups: its recovery increment is 3.52% higher than the viscosity reducer group under continuous injection, and the difference rises to 7.18% for one-round alternating injection. With the increase of alternating rounds, the recovery increment of natural gas-assisted flooding increases from 20.99% to 27.64%. After three rounds of alternating injection, the content of C10–C20 light hydrocarbon components in the natural gas group is 5.17% lower than that of the viscosity reducer group, which verifies that natural gas can realize in-situ upgrading of heavy oil via component extraction. Natural gas flooding integrates multiple advantages including light component extraction, oil dissolution and swelling, and formation energy supplementation. Multi-round alternating injection effectively weakens the degradation of chemical agents, reconstructs displacement fronts and expands sweep efficiency. Furthermore, this study proposes that the increased surface viscosity of produced oil can serve as a reverse indicator to evaluate the action intensity of natural gas. This work provides solid experimental data and mechanistic references for the parameter design and injection strategy optimization of gas-chemical composite flooding in heavy oil reservoirs.</div></div>","PeriodicalId":19403,"journal":{"name":"Ocean Engineering","volume":"363 ","pages":"Article 127193"},"PeriodicalIF":6.3,"publicationDate":"2026-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148731361","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":"YOLOv11n-SSS: A dynamic dual-branch attention detection model for sea urchins in marine ranching","authors":"Lijie Qiao,&nbsp;Ji Wang","doi":"10.1016/j.oceaneng.2026.127181","DOIUrl":"10.1016/j.oceaneng.2026.127181","url":null,"abstract":"<div><div>Accurate detection of underwater sea urchins is important for intelligent marine ranching, but it remains challenging because radial spine textures are easily confused with complex backgrounds, fine-grained features of juvenile individuals are often degraded during down-sampling, and dense aggregation can cause occlusions and missed detections. Existing general-purpose detectors rarely model the morphological characteristics of sea urchins or the spatial relationships among densely overlapping individuals. To address these challenges, a lightweight detection model, YOLOv11n-SSS, is developed for underwater sea urchin detection. First, a Spine-Aware Background-Filtering module is designed, comprising an edge-enhancement branch and a global background-filtering branch, to enhance spine-related responses while reducing interference from visually similar underwater backgrounds. Second, Space-to-Depth Convolution is selectively integrated into shallow down-sampling stages, transferring spatial information into the channel dimension and thereby mitigating the loss of fine-grained features in juvenile sea urchins. Third, a Scale-Aware Dynamic Weighted Loss is proposed to assign greater importance to small targets and introduce additional localization penalties for densely overlapping instances based on the spatial overlap relationships. Ablation experiments indicate that these three components yield complementary improvements. Compared with the YOLOv11n baseline, YOLOv11n-SSS improves precision, recall, mAP@50, and mAP@50:95 by 1.7, 1.1, 1.4, and 1.2 percentage points, respectively, while adding only 0.29 M parameters and 2.4 GFLOPs. These results indicate that YOLOv11n-SSS achieves a favorable balance between detection accuracy and computational efficiency, providing a practical approach for real-time sea urchin monitoring in intelligent marine ranching. Our code is available at <span><span>https://github.com/Qiao12-pixel/YOLOv11n-SSS</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":19403,"journal":{"name":"Ocean Engineering","volume":"363 ","pages":"Article 127181"},"PeriodicalIF":6.3,"publicationDate":"2026-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148731364","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}