Ocean Engineering最新文献_第3页

Dual-amplitude weighted transition dispersion entropy and its application in feature extraction of ship-radiated noise 双幅加权过渡色散熵及其在舰船辐射噪声特征提取中的应用

IF 5.5 2区工程技术

Ocean Engineering Pub Date : 2026-04-30 Epub Date: 2026-02-21 DOI: 10.1016/j.oceaneng.2026.124781

Yuxing Li, Rui Zhang

{"title":"Dual-amplitude weighted transition dispersion entropy and its application in feature extraction of ship-radiated noise","authors":"Yuxing Li, Rui Zhang","doi":"10.1016/j.oceaneng.2026.124781","DOIUrl":"10.1016/j.oceaneng.2026.124781","url":null,"abstract":"<div><div>Link dispersion entropy and its improved methods can effectively quantify the regularity and complexity of time series. However, they have two inherent limitations: (1) the patterns only focus on the amplitude information of time series, failing to take into account the differences between adjacent amplitudes simultaneously; (2) the state transition matrix is weighted in an equal-weight manner, ignoring the non-uniformity of weight allocation. To address these limitations, the dual-amplitude weighted transition dispersion entropy (DWTDE) is proposed in this paper. This method constructs a dual-amplitude coding pattern to simultaneously incorporate both amplitude information and adjacent differential information, and implements similarity-based weighting of the state transition matrix to enhance the rationality and pertinence of weight allocation. Furthermore, its multi-scale version, multi-scale DWTDE (MDWTDE), for characterizing the complexity of complex signals across different time scales. Simulation experiments demonstrate that DWTDE can accurately capture the dynamic changes of signals and exhibits superior robustness against noise. Two actual experiments based on ship-radiated noise signals (SRS) further verify that MDWTDE demonstrates superior feature extraction capability compared with the other four entropy-based methods.</div></div>","PeriodicalId":19403,"journal":{"name":"Ocean Engineering","volume":"353 ","pages":"Article 124781"},"PeriodicalIF":5.5,"publicationDate":"2026-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147386356","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

Passive range-only target motion analysis using array-invariant processing and an extended Kalman filter in shallow-water acoustic waveguides 基于阵列不变处理和扩展卡尔曼滤波的浅水声波导被动距离目标运动分析

IF 5.5 2区工程技术

Ocean Engineering Pub Date : 2026-04-30 Epub Date: 2026-02-18 DOI: 10.1016/j.oceaneng.2026.124732

Hoseok Sul , Taek Lyul Song , Jee Woong Choi

{"title":"Passive range-only target motion analysis using array-invariant processing and an extended Kalman filter in shallow-water acoustic waveguides","authors":"Hoseok Sul , Taek Lyul Song , Jee Woong Choi","doi":"10.1016/j.oceaneng.2026.124732","DOIUrl":"10.1016/j.oceaneng.2026.124732","url":null,"abstract":"<div><div>In passive sonar systems, target motion analysis (TMA) traditionally relies on bearing-angle measurements from horizontal line arrays, a technique commonly referred to as bearing-only target motion analysis (BOTMA). This paper presents an alternative TMA approach that incorporates range measurements obtained from two vertical line arrays (VLAs), here termed array invariant-based range-only target motion analysis (AI-ROTMA). The proposed method employs an extended Kalman filter to enhance target tracking accuracy, integrating source-receiver ranges derived from the array invariant method with the two-point localization results. The performance of AI-ROTMA was evaluated using ship-radiated noise data from R/V <em>Onnuri</em>, collected using two VLAs during the Shallow Water Acoustic Variability Experiment (SAVEX-15). Compared with the two-point localization method that uses only range measurements, AI-ROTMA provided significantly more reliable estimates of both range and velocity. These results highlight the potential of integrating array-invariant techniques into passive sonar TMA for enhanced tracking performance in realistic ocean environments.</div></div>","PeriodicalId":19403,"journal":{"name":"Ocean Engineering","volume":"353 ","pages":"Article 124732"},"PeriodicalIF":5.5,"publicationDate":"2026-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147386706","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

Spatiotemporal synchronization-aware cross-domain mission planning for air-sea-underwater heterogeneous unmanned swarms 空-海-水异构无人蜂群的时空同步感知跨域任务规划

IF 5.5 2区工程技术

Ocean Engineering Pub Date : 2026-04-30 Epub Date: 2026-02-20 DOI: 10.1016/j.oceaneng.2026.124694

Yishuo Meng , Songyan Xu , Xiaolong Liang , Shilin Zeng , Zhiwen Yao , Chengsi Bian , Lei Wang , Changting Zhong , Xinwei Wang

{"title":"Spatiotemporal synchronization-aware cross-domain mission planning for air-sea-underwater heterogeneous unmanned swarms","authors":"Yishuo Meng , Songyan Xu , Xiaolong Liang , Shilin Zeng , Zhiwen Yao , Chengsi Bian , Lei Wang , Changting Zhong , Xinwei Wang","doi":"10.1016/j.oceaneng.2026.124694","DOIUrl":"10.1016/j.oceaneng.2026.124694","url":null,"abstract":"<div><div>Facing the increasing complexity of multi-dimensional maritime operations, cross-domain heterogeneous unmanned swarms provide an appealing paradigm for comprehensive ocean perception. However, platform disparities and strict spatiotemporal synchronization requirements challenge mission planning where task assignment and path planning are tightly coupled. To address this, we design a single-stage cooperative planning framework tailored for air-sea-underwater swarms. First, a recursive temporal deduction mechanism is introduced to accurately quantify the cooperative waiting costs induced by platform heterogeneity. Subsequently, a Q-learning enhanced Genetic Algorithm integrating chaotic initialization and Opposition-Based Learning, named COGAQ, is proposed for rapid solution. This algorithm adopts a feasible coalition set index encoding strategy to limit the search space and integrates variable neighborhood search to rectify temporal asynchrony. Comparative experiments against mainstream algorithms validate the effectiveness of the designed framework, highlighting the optimum-seeking capability of COGAQ in handling tightly coupled constraints. Furthermore, a large-scale scenario containing 55 heterogeneous platforms and 102 targets verifies the algorithm's scalability. This paper provides a precise and efficient solution for air-sea-underwater heterogeneous temporal coordination problems, presenting an appealing approach for cooperative mission planning in complex scenarios like maritime search and rescue, ocean resource monitoring, and joint anti-submarine operations.</div></div>","PeriodicalId":19403,"journal":{"name":"Ocean Engineering","volume":"353 ","pages":"Article 124694"},"PeriodicalIF":5.5,"publicationDate":"2026-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147387259","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 investigation of submarine surface scattering and scaling effects under point forces excitation 点力激励下潜艇表面散射和尺度效应的数值研究

IF 5.5 2区工程技术

Ocean Engineering Pub Date : 2026-04-30 Epub Date: 2026-02-18 DOI: 10.1016/j.oceaneng.2026.124740

Xiaoshen Ning , Xiaorui Bai , Xusheng Li , Lin Li , Jianxin Ren , Qing Wang , Jian Hu

{"title":"Numerical investigation of submarine surface scattering and scaling effects under point forces excitation","authors":"Xiaoshen Ning , Xiaorui Bai , Xusheng Li , Lin Li , Jianxin Ren , Qing Wang , Jian Hu","doi":"10.1016/j.oceaneng.2026.124740","DOIUrl":"10.1016/j.oceaneng.2026.124740","url":null,"abstract":"<div><div>Underwater radiated noise (URN) is a critical factor in submarine stealth performance, with hydrodynamic loading acting as a major excitation source during high-speed cruising. This study investigates the role of non-compact hull surface scattering in shaping acoustic directivity patterns using a frequency-domain boundary element method (BEM). The method is validated against analytical solutions and applied to a DARPA Suboff subjected to multiple dipole excitations at the stern. Simulations were conducted across a range of Helmholtz numbers to assess frequency-dependent spatial directivity, and scaling analyses were performed to examine geometric similarity conditions. Results reveal that at low frequencies, directivity resembles the classical dipole figure-eight pattern, but when the wavelength becomes comparable to the hull dimensions, the non-compact boundary induces significant side lobes and compresses the main radiation lobe. Finally, these scattering effects persist into the far field in multi-scale configurations. The study confirms that spatial scale similarity can be preserved when source strength is scaled with the square of the geometric factor, but fixed-frequency evaluations expose scale-induced distortions in acoustic fields. These findings underscore the necessity of incorporating non-compact surface scattering into URN predictions, as neglecting such effects may result in inaccurate assessments of noise directivity in full-scale stealth evaluations.</div></div>","PeriodicalId":19403,"journal":{"name":"Ocean Engineering","volume":"353 ","pages":"Article 124740"},"PeriodicalIF":5.5,"publicationDate":"2026-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147387095","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 human reliability in reactive cargo handling for chemical tanker ships 化学品船反应性货物装卸人员可靠性评估

IF 5.5 2区工程技术

Ocean Engineering Pub Date : 2026-04-30 Epub Date: 2026-02-23 DOI: 10.1016/j.oceaneng.2026.124780

Esma Uflaz , Muhittin Orhan , M. Fatih Gulen , Furkan Gumus , Ozcan Arslan

{"title":"Assessing human reliability in reactive cargo handling for chemical tanker ships","authors":"Esma Uflaz , Muhittin Orhan , M. Fatih Gulen , Furkan Gumus , Ozcan Arslan","doi":"10.1016/j.oceaneng.2026.124780","DOIUrl":"10.1016/j.oceaneng.2026.124780","url":null,"abstract":"<div><div>This study assesses human reliability in reactive cargo handling on chemical tankers and proposes a hybrid Dempster-Shafer (DS)-extended Success Likelihood Index Methodology (SLIM) approach to enhance process safety. A hierarchical task analysis decomposes the operation, DS theory aggregates expert data to address subjectivity and uncertainty, and SLIM derives task/subtask HEPs. Human error probabilities are aggregated at the system level to assess overall system reliability. The novelty lies in assessing an under-researched high-risk shipboard operation with a novel hybrid methodology and offering practical risk mitigation strategies. Key findings show task complexity as the most influential PSF, followed by training/experience. Inhibitor quantity calculations, continuous monitoring procedures, and thermal management operations are revealed as the most critical tasks. Consequently, the overall human reliability is calculated as 8.20E-01. The findings provide valuable insights for maritime stakeholders, suggesting targeted interventions in calculation protocols, monitoring systems, heat management strategies, and specialised crew training to enhance process safety in handling reactive cargo onboard chemical tankers.</div></div>","PeriodicalId":19403,"journal":{"name":"Ocean Engineering","volume":"353 ","pages":"Article 124780"},"PeriodicalIF":5.5,"publicationDate":"2026-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147386611","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

Fusing SWAN priors with tucker decomposition: A training-free framework for sparse wave field reconstruction 融合SWAN先验与tucker分解：稀疏波场重建的无训练框架

IF 5.5 2区工程技术

Ocean Engineering Pub Date : 2026-04-30 Epub Date: 2026-02-21 DOI: 10.1016/j.oceaneng.2026.124786

Shanxun Sun , Zhicong Yang , Zihan Cai , Lianfang Ye , Yanni Kou , Ting He

{"title":"Fusing SWAN priors with tucker decomposition: A training-free framework for sparse wave field reconstruction","authors":"Shanxun Sun , Zhicong Yang , Zihan Cai , Lianfang Ye , Yanni Kou , Ting He","doi":"10.1016/j.oceaneng.2026.124786","DOIUrl":"10.1016/j.oceaneng.2026.124786","url":null,"abstract":"<div><div>Reconstructing complete multi-parameter wave fields from sparse observations is essential for marine energy applications and engineering safety. This study presents a training-free, physics-constrained approach that integrates SWAN-informed historical priors with Tucker low-rank decomposition, sparsity-based inversion, and temporal fusion to jointly reconstruct significant wave height, period, and direction. An objective PCA-weighted composite error metric is introduced to evaluate cross-variable performance. Using the South China Sea as the test area, multilinear ranks are optimized via Gaussian-process Bayesian search, and the sensor layout is designed to balance accuracy with cost-effectiveness. Using an optimized 140-sensor layout, month-long reconstructions in July 2023 achieved RMSE/MAE/R<sup>2</sup> of 0.1085/0.0609/0.9570 for Hsig (m), 0.2946/0.1686/0.9294 for Tm01 (s), and 0.1405/0.0705/0.9144 for Dir (rad). Under Typhoon Saola (2309), the method preserved robust directional reconstruction (RMSE = 0.1474 rad, R<sup>2</sup> = 0.7865) under rapid regime shifts. Compared with CNN, LSTM, TCN, and Transformer baselines in both swell-dominated and typhoon-dominated regimes, the proposed method yields lower composite errors and more stable performance, with particularly robust directional reconstruction enabled by circular encoding. These results demonstrate strong accuracy, stability, and adaptability under sparse deployments, providing practical insights for observing-system design and multi-source data fusion in ocean engineering.</div></div>","PeriodicalId":19403,"journal":{"name":"Ocean Engineering","volume":"353 ","pages":"Article 124786"},"PeriodicalIF":5.5,"publicationDate":"2026-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147386354","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

A CNN-LSTM-Attention model for risk prediction of offshore wind turbines 海上风电机组风险预测的CNN-LSTM-Attention模型

IF 5.5 2区工程技术

Ocean Engineering Pub Date : 2026-04-30 Epub Date: 2026-02-18 DOI: 10.1016/j.oceaneng.2026.124589

Jia-Xuan Li , Da-Gang Lu , Jia-Wei Ding , Yong Ding , Hao Liu , Libo Chen , Wenbing Wu

{"title":"A CNN-LSTM-Attention model for risk prediction of offshore wind turbines","authors":"Jia-Xuan Li , Da-Gang Lu , Jia-Wei Ding , Yong Ding , Hao Liu , Libo Chen , Wenbing Wu","doi":"10.1016/j.oceaneng.2026.124589","DOIUrl":"10.1016/j.oceaneng.2026.124589","url":null,"abstract":"<div><div>Offshore wind turbines are exposed to complex environmental loads such as wind, waves and ocean currents, which can lead to progressive structural degradation and increased failure risk over time. To achieve accurate risk prediction and intelligent structural health management, this paper proposes a hybrid deep learning model—CNN-LSTM-Attention—that integrates Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM) networks, and an Attention mechanism. The model leverages CNN to extract local spatial features, LSTM to capture temporal dynamics, and Attention to enhance the learning of critical patterns, thereby improving overall prediction accuracy. The proposed model is trained and validated on datasets representing four critical limit states of offshore wind turbine structures. Comparative experiments with traditional LSTM and CNN-LSTM models demonstrate that the CNN-LSTM-Attention model achieves superior performance across multiple evaluation metrics, including Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and the coefficient of determination (R<sup>2</sup>). Furthermore, the predicted probability distributions of structural failure reveal clear risk trends under different limit conditions, offering meaningful insights for risk-based maintenance planning and operational decision-making.</div></div>","PeriodicalId":19403,"journal":{"name":"Ocean Engineering","volume":"353 ","pages":"Article 124589"},"PeriodicalIF":5.5,"publicationDate":"2026-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147386433","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

A practical duct design to improve the performance of Darrieus hydrokinetic turbine kept in river flows 一种实用的水轮机保流风道设计

IF 5.5 2区工程技术

Ocean Engineering Pub Date : 2026-04-30 Epub Date: 2026-02-23 DOI: 10.1016/j.oceaneng.2026.124801

S. Madhankumar , Atul Kumar Soti

{"title":"A practical duct design to improve the performance of Darrieus hydrokinetic turbine kept in river flows","authors":"S. Madhankumar , Atul Kumar Soti","doi":"10.1016/j.oceaneng.2026.124801","DOIUrl":"10.1016/j.oceaneng.2026.124801","url":null,"abstract":"<div><div>This study investigates the performance enhancement of an H-type Darrieus vertical-axis hydrokinetic turbine augmented with a convergent-throat-divergent duct operating under realistic open-domain river flow conditions. Two-dimensional computational fluid dynamics simulations were performed using the SST <span><math><mrow><mi>k</mi><mo>−</mo><mi>ω</mi></mrow></math></span> turbulence model to accurately resolve the unsteady flow behaviour around the duct-turbine system. The effects of duct Contraction Ratio (CR) and throat length with different Tip Speed Ratios (TSRs) on the turbine performance were systematically analyzed. The results show that duct augmentation significantly improves energy extraction compared to the unducted turbine by accelerating the incoming flow and enhancing torque generation at the rotor plane. The ducted turbine achieves a maximum power coefficient (<span><math><mrow><msub><mi>C</mi><mi>p</mi></msub></mrow></math></span>) of 1.29 for a CR of 3.33 at TSR 2.5. Notably, the <span><math><mrow><msub><mi>C</mi><mi>p</mi></msub></mrow></math></span> exceeds the conventional Betz limit, which is permissible due to flow concentration effects induced by the duct in an open domain. At lower TSRs, intermediate CRs demonstrated better performance, highlighting the importance of matching duct geometry to flow conditions. Additional analyses of torque generation and velocity distribution confirm that the duct effectively accelerates flow through the rotor plane, enabling greater energy extraction. Variations in throat length show only a marginal impact on overall performance. Overall, these findings highlight that practical convergent-throat-divergent duct augmentation in an open-domain environment is a powerful strategy to enhance the turbine's performance in low-velocity river flow environments.</div></div>","PeriodicalId":19403,"journal":{"name":"Ocean Engineering","volume":"353 ","pages":"Article 124801"},"PeriodicalIF":5.5,"publicationDate":"2026-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147386613","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

ConvNeXt-PIV: A modern convolutional framework for particle image velocimetry ConvNeXt-PIV：一个用于粒子图像测速的现代卷积框架

IF 5.5 2区工程技术

Ocean Engineering Pub Date : 2026-04-30 Epub Date: 2026-02-19 DOI: 10.1016/j.oceaneng.2026.124633

Zhifeng Xiao, Bo Hong, Liang Shan

{"title":"ConvNeXt-PIV: A modern convolutional framework for particle image velocimetry","authors":"Zhifeng Xiao, Bo Hong, Liang Shan","doi":"10.1016/j.oceaneng.2026.124633","DOIUrl":"10.1016/j.oceaneng.2026.124633","url":null,"abstract":"<div><div>In recent years, deep learning–based Particle Image Velocimetry (PIV) models have made remarkable progress in estimating velocity fields of complex flows. Conventional CNN-based PIV networks, relying on small convolutional kernels and pooling layers, are limited in capturing global structures and large-scale motions. Transformer-based models mitigate this limitation but incur substantial computational and memory costs. To address these issues, we propose ConvNeXt-PIV, an encoder built on the ConvNeXt module, where depthwise convolution serves as the main feature extractor and large kernels expand the receptive field. By redesigning the feature hierarchy and raising the output to one quarter of the input resolution, the network better captures particle spacing and velocity-gradient details. Experimental results on Problem Class I demonstrate that ConvNeXt-PIV educes the average endpoint error (AEE) by 50.0%, 68.6%, and 37.6% on the Backstep, JHTDB Channel, and DNS Turbulence datasets, respectively, compared with the baseline method. Meanwhile, the number of model parameters is reduced to 1.7M parameters while maintaining a comparable computational cost, highlighting a favorable balance between accuracy and efficiency. These results verify the potential of modern convolutional architectures for fluid-motion estimation and provide a new design paradigm for lightweight and high-precision PIV modeling.</div></div>","PeriodicalId":19403,"journal":{"name":"Ocean Engineering","volume":"353 ","pages":"Article 124633"},"PeriodicalIF":5.5,"publicationDate":"2026-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147387252","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

A rolling multi-objective ship speed optimization strategy for energy efficiency and operating cost control in dynamic waterway conditions 动态航道条件下船舶能效与运营成本的滚动多目标航速优化策略

IF 5.5 2区工程技术

Ocean Engineering Pub Date : 2026-04-30 Epub Date: 2026-02-18 DOI: 10.1016/j.oceaneng.2026.124738

Yupeng Yuan , Xiaodong Guo , Zhe Xiong , Daogui Tang , Liang Tong , Chi Zhang , Zhengmao Li , Joshua Y. Kim

{"title":"A rolling multi-objective ship speed optimization strategy for energy efficiency and operating cost control in dynamic waterway conditions","authors":"Yupeng Yuan , Xiaodong Guo , Zhe Xiong , Daogui Tang , Liang Tong , Chi Zhang , Zhengmao Li , Joshua Y. Kim","doi":"10.1016/j.oceaneng.2026.124738","DOIUrl":"10.1016/j.oceaneng.2026.124738","url":null,"abstract":"<div><div>The push to decarbonization for ships is driving the development of supporting tools for operations. This study develops a rolling, voyage-scale multi-objective decision-support framework for speed management on inland waterways. The framework simultaneously minimizes the Energy Efficiency Operational Indicator (EEOI) and total operating cost by adjusting ship speed leg by leg under time-varying environmental conditions. Within each decision window, it solves a nonlinear bi-objective optimization problem using updated environmental and operating information to generate a practical compromise speed recommendation. A case study on a 7000 t bulk carrier across 18 legs shows that, compared with as-operated records, the proposed framework lowers total cost by 7.5% and shortens voyage time by 9.5%, while producing a smoother speed profile that is more compatible with ship propulsion and power-system control. These results indicate that rolling multi-objective speed optimization offers an effective engineering approach for voyage planning and operational speed management under time-varying environmental conditions, with clear potential for inland waterway decision-support applications.</div></div>","PeriodicalId":19403,"journal":{"name":"Ocean Engineering","volume":"353 ","pages":"Article 124738"},"PeriodicalIF":5.5,"publicationDate":"2026-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147386708","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