This paper presents a modular software architecture for the autonomous navigation of surface vehicles, designed around a layered awareness, navigation, guidance, and control structure. The proposed framework separates global path management, reactive local planning for collision avoidance, and control, while situation awareness combines LiDAR perception with INS/GNSS localization to maintain an up-to-date, and realistic representation of the surrounding environment. The architecture is designed around the concepts of modularity and scalability, enabling distributed computation and the flexible integration of modules. The implementation employs a lightweight publish/subscribe protocol to enable efficient real-time communication among modules. The experimental validation of the proposed architecture in a collision avoidance test featuring a research ASV is reported and discussed. The vehicle successfully executed polygonal paths, adapting its trajectory to avoid multiple unexpected obstacles while still reaching its prescribed waypoints. These results demonstrated the reliability of the proposed framework in supporting path following and adaptive collision avoidance under realistic operating conditions.
{"title":"Experimental validation of a modular navigation architecture for marine autonomous surface vehicles with reactive collision avoidance","authors":"Raphael Zaccone, Filippo Ponzini, Michele Martelli","doi":"10.1016/j.apor.2025.104903","DOIUrl":"10.1016/j.apor.2025.104903","url":null,"abstract":"<div><div>This paper presents a modular software architecture for the autonomous navigation of surface vehicles, designed around a layered awareness, navigation, guidance, and control structure. The proposed framework separates global path management, reactive local planning for collision avoidance, and control, while situation awareness combines LiDAR perception with INS/GNSS localization to maintain an up-to-date, and realistic representation of the surrounding environment. The architecture is designed around the concepts of modularity and scalability, enabling distributed computation and the flexible integration of modules. The implementation employs a lightweight publish/subscribe protocol to enable efficient real-time communication among modules. The experimental validation of the proposed architecture in a collision avoidance test featuring a research ASV is reported and discussed. The vehicle successfully executed polygonal paths, adapting its trajectory to avoid multiple unexpected obstacles while still reaching its prescribed waypoints. These results demonstrated the reliability of the proposed framework in supporting path following and adaptive collision avoidance under realistic operating conditions.</div></div>","PeriodicalId":8261,"journal":{"name":"Applied Ocean Research","volume":"166 ","pages":"Article 104903"},"PeriodicalIF":4.4,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145921178","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}
Pub Date : 2026-01-01Epub Date: 2026-01-06DOI: 10.1016/j.apor.2025.104904
Sang-Won Lee , Ik-Soon Cho
Port terminals face increasing operational demands that often result in elevated berthing velocities and potential structural damage to berthing facilities. Traditional berthing design criteria primarily rely on static velocity thresholds without considering dynamic interactions between ship characteristics, environmental conditions, and structural response of marine infrastructure. This study presents a comprehensive berthing risk assessment framework that integrates measured berthing velocity with machine hybrid machine learning approaches to enhance port terminal safety and infrastructure protection. Field measurements were conducted over five years (2017–2022) at a major tanker terminal using laser-based Docking Aid Systems, capturing over 900 berthing events across three jetties with varying vessel capacities. The proposed methodology employs K-means clustering to establish risk levels for various influence factors, including weather conditions, ship particulars, and human factors such as pilot experience. Multiple machine learning algorithms were combined to determine optimal weight factors for each risk category, enabling comprehensive risk quantification. The developed model achieved a detection rate of approximately 79 % for high-risk situations, successfully identifying 41 out of 52 cases that exceeded safety criteria. The ensemble approach, combining multiple algorithms with performance-weighted coefficients, demonstrated superior accuracy compared to individual models. This proposed methodology enables quantitative risk evaluation before berthing operations, providing early warning capabilities and objective decision-making support for port operations.
{"title":"Integrated berthing risk assessment framework using a combination of machine learning with measured berthing velocity","authors":"Sang-Won Lee , Ik-Soon Cho","doi":"10.1016/j.apor.2025.104904","DOIUrl":"10.1016/j.apor.2025.104904","url":null,"abstract":"<div><div>Port terminals face increasing operational demands that often result in elevated berthing velocities and potential structural damage to berthing facilities. Traditional berthing design criteria primarily rely on static velocity thresholds without considering dynamic interactions between ship characteristics, environmental conditions, and structural response of marine infrastructure. This study presents a comprehensive berthing risk assessment framework that integrates measured berthing velocity with machine hybrid machine learning approaches to enhance port terminal safety and infrastructure protection. Field measurements were conducted over five years (2017–2022) at a major tanker terminal using laser-based Docking Aid Systems, capturing over 900 berthing events across three jetties with varying vessel capacities. The proposed methodology employs K-means clustering to establish risk levels for various influence factors, including weather conditions, ship particulars, and human factors such as pilot experience. Multiple machine learning algorithms were combined to determine optimal weight factors for each risk category, enabling comprehensive risk quantification. The developed model achieved a detection rate of approximately 79 % for high-risk situations, successfully identifying 41 out of 52 cases that exceeded safety criteria. The ensemble approach, combining multiple algorithms with performance-weighted coefficients, demonstrated superior accuracy compared to individual models. This proposed methodology enables quantitative risk evaluation before berthing operations, providing early warning capabilities and objective decision-making support for port operations.</div></div>","PeriodicalId":8261,"journal":{"name":"Applied Ocean Research","volume":"166 ","pages":"Article 104904"},"PeriodicalIF":4.4,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145921169","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}
Pub Date : 2026-01-01Epub Date: 2025-12-15DOI: 10.1016/j.apor.2025.104894
Yongsong Li , Jinsheng Xuan , Chengmiao Liu , Gaochao Zhao , Yanming Xu
Underwater acoustic modeling plays a crucial role in marine engineering applications, especially in shallow-water environments where complex boundary interactions dominate sound propagation. Traditional numerical approaches like the finite element method often struggle with the challenges posed by unbounded domains and artificial boundary conditions. In contrast, the isogeometric boundary element method (IGABEM) offers a powerful alternative by combining the dimensionality reduction and infinite domain handling capabilities of the boundary element method with the exact geometry representation and smooth basis functions of isogeometric analysis. This paper presents a novel IGABEM framework tailored for acoustic simulations in shallow-water settings, where acoustic propagation is governed by coupled reflections from the sea surface and sea floor. Additionally, in order to enhance computational efficiency and scalability, we integrate and adapt advanced acceleration techniques, including the fast multipole method, frequency decoupling via Taylor expansion, and the second-order arnoldi algorithm. The numerical results validate the accuracy, robustness, and computational advantages of the proposed method, establishing it as a promising tool for high-fidelity underwater acoustic analysis.
{"title":"Shallow-water acoustic analysis with an accelerated isogeometric boundary element approach","authors":"Yongsong Li , Jinsheng Xuan , Chengmiao Liu , Gaochao Zhao , Yanming Xu","doi":"10.1016/j.apor.2025.104894","DOIUrl":"10.1016/j.apor.2025.104894","url":null,"abstract":"<div><div>Underwater acoustic modeling plays a crucial role in marine engineering applications, especially in shallow-water environments where complex boundary interactions dominate sound propagation. Traditional numerical approaches like the finite element method often struggle with the challenges posed by unbounded domains and artificial boundary conditions. In contrast, the isogeometric boundary element method (IGABEM) offers a powerful alternative by combining the dimensionality reduction and infinite domain handling capabilities of the boundary element method with the exact geometry representation and smooth basis functions of isogeometric analysis. This paper presents a novel IGABEM framework tailored for acoustic simulations in shallow-water settings, where acoustic propagation is governed by coupled reflections from the sea surface and sea floor. Additionally, in order to enhance computational efficiency and scalability, we integrate and adapt advanced acceleration techniques, including the fast multipole method, frequency decoupling via Taylor expansion, and the second-order arnoldi algorithm. The numerical results validate the accuracy, robustness, and computational advantages of the proposed method, establishing it as a promising tool for high-fidelity underwater acoustic analysis.</div></div>","PeriodicalId":8261,"journal":{"name":"Applied Ocean Research","volume":"166 ","pages":"Article 104894"},"PeriodicalIF":4.4,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145787731","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}
Pub Date : 2026-01-01Epub Date: 2025-12-06DOI: 10.1016/j.apor.2025.104873
Yujia Wang , Dingqi Yang , Linlin Li , Huabin Shi
Efficient and accurate prediction of tropical cyclone induced storm surge is critical to coastal disaster emergency response and recovery. Recently, machine learning (ML) methods have been widely applied to storm surge predictions but there is still notable room for improvement. In this paper, we focus on optimizing the generalization and accuracy of storm surge ML models by integrating hydrodynamics in data featuring and model setups, which can be extended to all categories of ML methods like recurrent neural networks and self-attention deep learning architectures rather than a specific model. First, a surge-gradient-informed data splitting strategy is proposed, in which the maximum gradient of surge level is used as a priori indicator to identify the outliers in the adopted tropical cyclones and these outliers are compulsorily included in the training dataset to improve the model generalization. It is shown that, compared with the common random data splitting, the surge-gradient-informed data splitting has a non-negligible effect on reducing the errors in the predicted rapid increases of surge levels. Further, based on shallow-water hydrodynamics in the motion of coastal water, a gradient-target setup of ML models is suggested which takes the temporal gradient of surge level, rather than directly the surge level, as the output variable of ML models. Correspondingly, according to a comparison study, the combination of historical surge gradient and meteorological-oceanographic conditions is recommended for the input features of gradient-target ML models. This gradient-target setup is tested in four categories of ML models, i.e., XGBoost, LSTM, DLinear, and Multi-head MLP. It is shown that, for forecasts with a lead time of 1-2 hours, the gradient-target setup improves the performances of all four ML models to different extents, especially those of XGBoost and LSTM models. For forecasts with a lead time of over 3 hours, the improvement of ML model performances by using the gradient-target setup diminishes with the lead time and the performances of XGBoost and MLP models are even worsen. Nevertheless, the gradient-target models capture the rapid rising and falling of surge heights more accurately than the surge-target models. The surge-gradient informed data splitting and gradient-target model setup proposed in this study provide an alternative view to incorporating physics into ML models for ocean hydrodynamic disasters.
{"title":"Gradient-informed data splitting and model setup for machine learning prediction of storm surge","authors":"Yujia Wang , Dingqi Yang , Linlin Li , Huabin Shi","doi":"10.1016/j.apor.2025.104873","DOIUrl":"10.1016/j.apor.2025.104873","url":null,"abstract":"<div><div>Efficient and accurate prediction of tropical cyclone induced storm surge is critical to coastal disaster emergency response and recovery. Recently, machine learning (ML) methods have been widely applied to storm surge predictions but there is still notable room for improvement. In this paper, we focus on optimizing the generalization and accuracy of storm surge ML models by integrating hydrodynamics in data featuring and model setups, which can be extended to all categories of ML methods like recurrent neural networks and self-attention deep learning architectures rather than a specific model. First, a surge-gradient-informed data splitting strategy is proposed, in which the maximum gradient of surge level is used as a priori indicator to identify the outliers in the adopted tropical cyclones and these outliers are compulsorily included in the training dataset to improve the model generalization. It is shown that, compared with the common random data splitting, the surge-gradient-informed data splitting has a non-negligible effect on reducing the errors in the predicted rapid increases of surge levels. Further, based on shallow-water hydrodynamics in the motion of coastal water, a gradient-target setup of ML models is suggested which takes the temporal gradient of surge level, rather than directly the surge level, as the output variable of ML models. Correspondingly, according to a comparison study, the combination of historical surge gradient and meteorological-oceanographic conditions is recommended for the input features of gradient-target ML models. This gradient-target setup is tested in four categories of ML models, i.e., XGBoost, LSTM, DLinear, and Multi-head MLP. It is shown that, for forecasts with a lead time of 1-2 hours, the gradient-target setup improves the performances of all four ML models to different extents, especially those of XGBoost and LSTM models. For forecasts with a lead time of over 3 hours, the improvement of ML model performances by using the gradient-target setup diminishes with the lead time and the performances of XGBoost and MLP models are even worsen. Nevertheless, the gradient-target models capture the rapid rising and falling of surge heights more accurately than the surge-target models. The surge-gradient informed data splitting and gradient-target model setup proposed in this study provide an alternative view to incorporating physics into ML models for ocean hydrodynamic disasters.</div></div>","PeriodicalId":8261,"journal":{"name":"Applied Ocean Research","volume":"166 ","pages":"Article 104873"},"PeriodicalIF":4.4,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145734694","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}
Pub Date : 2026-01-01Epub Date: 2026-01-16DOI: 10.1016/j.apor.2025.104907
Xin Xiong, Rudy R. Negenborn, Yusong Pang
Existing studies on multi-vessel formations rarely combine physically based models of ship–ship hydrodynamic interaction with online formation control, so that energy benefits are typically assessed offline or only approximated through artificial potentials. This paper addresses this gap by embedding a reduced-order, hydrodynamics-aware resistance model into a hierarchical formation control framework for multi vessel systems. A three degree of freedom interaction model is incorporated into the cost function, enabling the supervisory controller to adaptively optimize inter ship spacing and formation geometry in a speed dependent and hydrodynamics aware manner. The lower level MPC ensures accurate trajectory tracking and stability under the guidance of the top level optimization. Four simulation studies are conducted to evaluate the proposed method. The platooning formation is first analyzed as a reference, followed by the triangular formation, which achieves balanced tracking performance and stability. The echelon formation is then examined, demonstrating significant energy savings in medium to high speed regimes while maintaining yaw stability. Finally, an unconstrained optimization scenario is explored, where the system autonomously adapts its geometry without prescribed patterns, revealing emergent energy efficient and stable arrangements across different speed ranges. Results show that the proposed approach not only reduces resistance and improves energy efficiency but also enhances formation adaptability and robustness under varying operating conditions. These findings provide new insights into hydrodynamics aware cooperative control and the development of energy conscious fleet management strategies for future maritime transportation.
{"title":"Energy efficient formation control of multi vessel systems via hydrodynamics aware configuration optimization","authors":"Xin Xiong, Rudy R. Negenborn, Yusong Pang","doi":"10.1016/j.apor.2025.104907","DOIUrl":"10.1016/j.apor.2025.104907","url":null,"abstract":"<div><div>Existing studies on multi-vessel formations rarely combine physically based models of ship–ship hydrodynamic interaction with online formation control, so that energy benefits are typically assessed offline or only approximated through artificial potentials. This paper addresses this gap by embedding a reduced-order, hydrodynamics-aware resistance model into a hierarchical formation control framework for multi vessel systems. A three degree of freedom interaction model is incorporated into the cost function, enabling the supervisory controller to adaptively optimize inter ship spacing and formation geometry in a speed dependent and hydrodynamics aware manner. The lower level MPC ensures accurate trajectory tracking and stability under the guidance of the top level optimization. Four simulation studies are conducted to evaluate the proposed method. The platooning formation is first analyzed as a reference, followed by the triangular formation, which achieves balanced tracking performance and stability. The echelon formation is then examined, demonstrating significant energy savings in medium to high speed regimes while maintaining yaw stability. Finally, an unconstrained optimization scenario is explored, where the system autonomously adapts its geometry without prescribed patterns, revealing emergent energy efficient and stable arrangements across different speed ranges. Results show that the proposed approach not only reduces resistance and improves energy efficiency but also enhances formation adaptability and robustness under varying operating conditions. These findings provide new insights into hydrodynamics aware cooperative control and the development of energy conscious fleet management strategies for future maritime transportation.</div></div>","PeriodicalId":8261,"journal":{"name":"Applied Ocean Research","volume":"166 ","pages":"Article 104907"},"PeriodicalIF":4.4,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145972754","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}
Pub Date : 2026-01-01Epub Date: 2025-12-04DOI: 10.1016/j.apor.2025.104830
Lulu Liu , Ian A. Milne , Hugh A. Wolgamot , Wenhua Zhao , Raúl Guanche
Floating offshore wind energy represents a promising frontier in marine renewable energy, enabling deployment in deeper waters. Among the various solutions for floating wind energy, semi-submersible platforms have emerged as the most viable option. The success of this technology depends on their hydrodynamic performance. Current engineering practice for design and operability assessment has primarily focused on short wave excitation, with less attention to long-period waves. Swells carry energy near the natural periods of these structures and are more likely to induce resonant responses in heave, in which scenario the prediction is challenging and less understood. This may lead to over-conservative designs and thus unnecessarily high cost. To address this gap, this study examines resonant responses driven by long-period waves through linear processes. In resonance, viscous effects play a critical role, e.g., in determining response amplitudes. However, estimating viscous effects is challenging as a result of their underlying complex physics and nonlinearity. To better understand the viscous effects and their impact on floating wind turbines, a series of scaled model tests was analysed for a 10-MW floating wind energy platform. To facilitate the interpretation of the experimental results, inviscid flow calculations were also performed. The results indicate that viscous damping is positively correlated with the Keulegan–Carpenter (KC) number that characterizes the relative velocity between the floating system and the surrounding water particles. A striking observation is that viscosity can significantly alter the added mass, which is key to the estimation of the natural frequency of the floating system.
{"title":"Viscosity and nonlinear resonant heave response of a semi–submersible floating wind energy platform","authors":"Lulu Liu , Ian A. Milne , Hugh A. Wolgamot , Wenhua Zhao , Raúl Guanche","doi":"10.1016/j.apor.2025.104830","DOIUrl":"10.1016/j.apor.2025.104830","url":null,"abstract":"<div><div>Floating offshore wind energy represents a promising frontier in marine renewable energy, enabling deployment in deeper waters. Among the various solutions for floating wind energy, semi-submersible platforms have emerged as the most viable option. The success of this technology depends on their hydrodynamic performance. Current engineering practice for design and operability assessment has primarily focused on short wave excitation, with less attention to long-period waves. Swells carry energy near the natural periods of these structures and are more likely to induce resonant responses in heave, in which scenario the prediction is challenging and less understood. This may lead to over-conservative designs and thus unnecessarily high cost. To address this gap, this study examines resonant responses driven by long-period waves through linear processes. In resonance, viscous effects play a critical role, e.g., in determining response amplitudes. However, estimating viscous effects is challenging as a result of their underlying complex physics and nonlinearity. To better understand the viscous effects and their impact on floating wind turbines, a series of scaled model tests was analysed for a 10-MW floating wind energy platform. To facilitate the interpretation of the experimental results, inviscid flow calculations were also performed. The results indicate that viscous damping is positively correlated with the Keulegan–Carpenter (<em>KC</em>) number that characterizes the relative velocity between the floating system and the surrounding water particles. A striking observation is that viscosity can significantly alter the added mass, which is key to the estimation of the natural frequency of the floating system.</div></div>","PeriodicalId":8261,"journal":{"name":"Applied Ocean Research","volume":"166 ","pages":"Article 104830"},"PeriodicalIF":4.4,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145683707","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}
Pub Date : 2026-01-01Epub Date: 2025-12-06DOI: 10.1016/j.apor.2025.104879
Xinxing Tan , Dehua Wang , Pu Sun , Tian Lan
Ship carbon emissions have become a global concern, yet significant challenges persist in precise quantification, real-time assessment, and operational optimization. This study proposes an integrated "direct measurement-emission prediction-speed optimization" framework to bridge these gaps. Firstly, a shipboard CO₂ direct measurement system is deployed to collect high-frequency dynamic data. Through synergistic application of domain expertise, statistical methods, and HDBSCAN clustering, erroneous values, anomalies, and outliers are effectively identified and eliminated, yielding a high-confidence fine-grained dataset for microscopic emission analysis and operational decisions. Secondly, four metaheuristic algorithms are incorporated into a dual-adaptive prediction architecture, expanding its mechanistic boundaries to construct a highly reliable and precise CO₂ prediction model. Finally, an Enhanced Equilibrium Optimizer (EEO) with Cauchy mutation strategy demonstrates superior performance in carbon-oriented speed optimization. During a 6-day coastal voyage, EEO algorithm achieves direct carbon reduction of 12.27 tons and 4.11% energy efficiency improvement, realizing synergistic gains in decarbonization and Energy Efficiency Operational Indicator (EEOI). This research provides a holistic technical framework and granular decision support for shipping decarbonization.
{"title":"A triad framework for ship carbon reduction: Direct CO2 measurement, multi-intelligence fusion prediction, and Cauchy-enhanced speed optimization","authors":"Xinxing Tan , Dehua Wang , Pu Sun , Tian Lan","doi":"10.1016/j.apor.2025.104879","DOIUrl":"10.1016/j.apor.2025.104879","url":null,"abstract":"<div><div>Ship carbon emissions have become a global concern, yet significant challenges persist in precise quantification, real-time assessment, and operational optimization. This study proposes an integrated \"direct measurement-emission prediction-speed optimization\" framework to bridge these gaps. Firstly, a shipboard CO₂ direct measurement system is deployed to collect high-frequency dynamic data. Through synergistic application of domain expertise, statistical methods, and HDBSCAN clustering, erroneous values, anomalies, and outliers are effectively identified and eliminated, yielding a high-confidence fine-grained dataset for microscopic emission analysis and operational decisions. Secondly, four metaheuristic algorithms are incorporated into a dual-adaptive prediction architecture, expanding its mechanistic boundaries to construct a highly reliable and precise CO₂ prediction model. Finally, an Enhanced Equilibrium Optimizer (EEO) with Cauchy mutation strategy demonstrates superior performance in carbon-oriented speed optimization. During a 6-day coastal voyage, EEO algorithm achieves direct carbon reduction of 12.27 tons and 4.11% energy efficiency improvement, realizing synergistic gains in decarbonization and Energy Efficiency Operational Indicator (EEOI). This research provides a holistic technical framework and granular decision support for shipping decarbonization.</div></div>","PeriodicalId":8261,"journal":{"name":"Applied Ocean Research","volume":"166 ","pages":"Article 104879"},"PeriodicalIF":4.4,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145683709","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}
Pub Date : 2026-01-01Epub Date: 2026-01-06DOI: 10.1016/j.apor.2025.104902
Gabriel Barajas , Javier L. Lara , Alessandro Romano , Eduard Puig Montellà
Landslide-generated impacts represent a critical hazard for coastal and reservoir infrastructures, yet their underlying fluid dynamics remain poorly understood due to the complexity of turbulent, free-surface flows. In this work, OpenFOAM® is used to investigate sudden impacts on surfaces caused by granular landslides. First, three sets of experiments are used to validate the numerical framework: a dry granular flow impact on a wall in an inclined flume, a debris avalanche impacting a pier and a dam-break interaction of a fluid impact on a vertical cylinder. For each case, numerical predictions are compared with experiments in terms of impact forces, providing confidence that the solver can reproduce sudden loads caused by granular and fluid masses. Then, the validated numerical setup is used to study submerged landslide impacts on slender cylinders, capturing the interaction between the granular slide and the free surface, resolving large-scale vortical structures and their role in energy transfer and dissipation. Results highlight two distinct stages of the phenomenon: (i) the initial impact and jet formation, and (ii) turbulent dissipation and recirculation. The analysis provides quantitative insights into velocity fields, pressure distributions, and turbulence intensities, and identifies key mechanisms driving energy loss. These findings contribute to a deeper physical understanding of landslide–impacts and offer a basis for improved hazard assessment and engineering design of protective structures.
{"title":"Landslide impacts on built environment: Numerical analysis of the forces exerted by granular material collapsing on dry and submerged conditions","authors":"Gabriel Barajas , Javier L. Lara , Alessandro Romano , Eduard Puig Montellà","doi":"10.1016/j.apor.2025.104902","DOIUrl":"10.1016/j.apor.2025.104902","url":null,"abstract":"<div><div>Landslide-generated impacts represent a critical hazard for coastal and reservoir infrastructures, yet their underlying fluid dynamics remain poorly understood due to the complexity of turbulent, free-surface flows. In this work, OpenFOAM® is used to investigate sudden impacts on surfaces caused by granular landslides. First, three sets of experiments are used to validate the numerical framework: a dry granular flow impact on a wall in an inclined flume, a debris avalanche impacting a pier and a dam-break interaction of a fluid impact on a vertical cylinder. For each case, numerical predictions are compared with experiments in terms of impact forces, providing confidence that the solver can reproduce sudden loads caused by granular and fluid masses. Then, the validated numerical setup is used to study submerged landslide impacts on slender cylinders, capturing the interaction between the granular slide and the free surface, resolving large-scale vortical structures and their role in energy transfer and dissipation. Results highlight two distinct stages of the phenomenon: (i) the initial impact and jet formation, and (ii) turbulent dissipation and recirculation. The analysis provides quantitative insights into velocity fields, pressure distributions, and turbulence intensities, and identifies key mechanisms driving energy loss. These findings contribute to a deeper physical understanding of landslide–impacts and offer a basis for improved hazard assessment and engineering design of protective structures.</div></div>","PeriodicalId":8261,"journal":{"name":"Applied Ocean Research","volume":"166 ","pages":"Article 104902"},"PeriodicalIF":4.4,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145921172","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}
Pub Date : 2026-01-01Epub Date: 2025-12-06DOI: 10.1016/j.apor.2025.104890
Tao Peng , Rongwu Xu , Jiarui Zhang , Jinwei Liu , Zhenyu Yao
Accurately separating and quantifying the contributions of individual mechanical equipment from coupled noise in ships is critical for further reducing radiated noise from onboard machinery. Transfer Path Analysis (TPA) and Operational Transfer Path Analysis (OTPA) are widely adopted for vibration-noise transfer characterization; however, their efficiency and accuracy in shipboard noise testing scenarios remain limited. To address this challenge, this paper proposes a Reverse Transfer Path Analysis (RTPA) method based on pseudo-force and reciprocity testing principles. This method eliminates the need for machinery disassembly or installation of large-scale exciters, enabling in-situ measurements of equipment excitation forces and frequency response functions, thereby significantly improving testing efficiency. Validation through underwater noise experiments on a scaled ship model demonstrates the effectiveness of RTPA in decoupling machinery noise contributions. Results show excellent agreement with measured data: shaft-related tonal frequencies and amplitudes fully align with ground-truth values, and the broadband noise level error is only 3.1 dB, compared to a 10.3 dB error for the OTPA method. These findings confirm the enhanced accuracy and effectiveness of the RTPA method in resolving coupled noise separation challenges for shipboard machinery, offering robust support for vibration/noise reduction strategies and acoustic optimization in ship design.
{"title":"A study on the separation method of radiation noise contributions from multiple ship equipment based on reverse transfer path analysis","authors":"Tao Peng , Rongwu Xu , Jiarui Zhang , Jinwei Liu , Zhenyu Yao","doi":"10.1016/j.apor.2025.104890","DOIUrl":"10.1016/j.apor.2025.104890","url":null,"abstract":"<div><div>Accurately separating and quantifying the contributions of individual mechanical equipment from coupled noise in ships is critical for further reducing radiated noise from onboard machinery. Transfer Path Analysis (TPA) and Operational Transfer Path Analysis (OTPA) are widely adopted for vibration-noise transfer characterization; however, their efficiency and accuracy in shipboard noise testing scenarios remain limited. To address this challenge, this paper proposes a Reverse Transfer Path Analysis (RTPA) method based on pseudo-force and reciprocity testing principles. This method eliminates the need for machinery disassembly or installation of large-scale exciters, enabling in-situ measurements of equipment excitation forces and frequency response functions, thereby significantly improving testing efficiency. Validation through underwater noise experiments on a scaled ship model demonstrates the effectiveness of RTPA in decoupling machinery noise contributions. Results show excellent agreement with measured data: shaft-related tonal frequencies and amplitudes fully align with ground-truth values, and the broadband noise level error is only 3.1 dB, compared to a 10.3 dB error for the OTPA method. These findings confirm the enhanced accuracy and effectiveness of the RTPA method in resolving coupled noise separation challenges for shipboard machinery, offering robust support for vibration/noise reduction strategies and acoustic optimization in ship design.</div></div>","PeriodicalId":8261,"journal":{"name":"Applied Ocean Research","volume":"166 ","pages":"Article 104890"},"PeriodicalIF":4.4,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145734797","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}
Pub Date : 2025-12-01Epub Date: 2025-10-16DOI: 10.1016/j.apor.2025.104792
Hany Qoshirotur Rif’atin , Ikha Magdalena , Muhammad Syahril Badri Kusuma , Alamsyah Kurniawan
Coastal erosion, increasingly driven by human activities and climate change, poses escalating threats to shoreline stability and nearby communities. While coastal vegetation, especially mangroves, plays a crucial role in mitigating erosion, these ecosystems themselves are under threat from wave impacts and sediment loss. Grey infrastructure, such as breakwaters, is often proposed to enhance coastal resilience and protect vegetation. However, the interactions among wave dynamics, structural interventions, and sediment transport, which is crucial for vegetation survival, are frequently overlooked. This study develops a one-dimensional coupled hydrodynamic–morphodynamic model to simulate wave propagation and sediment evolution in coastal areas featuring hybrid coastal defense, which incorporates both green (mangrove) and grey (breakwater) protection strategies. The model combines the Nonlinear Shallow Water Equations (NSWE) for hydrodynamics with the Exner equation for morphodynamics, solved numerically using the Staggered Finite Volume Method. The NSWE is used due to its lower computational cost compared to other more complex models, yet it still deliver sufficient accuracy for simulating wave dynamics. Validation against diverse experimental datasets confirms the model’s accuracy in capturing wave and sediment dynamics. The validated model is then applied to investigate the effectiveness of multiple configurations of hybrid structure in reducing wave height and erosion. Additionally, sensitivity analyses explore the effects of several key parameters, offering insights to inform the development of more effective and adaptive coastal protection strategies. Further analysis is also conducted to observe how the erosion-related parameters change at field scale under storm conditions.
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