Pub Date : 2026-01-01DOI: 10.1016/j.apor.2025.104915
Niels Gjøl Jacobsen
The present work proposes an analytical model for the irregular wave field in submerged and emergent canopies, and the model is based on linear superposition of the solution to the linearized momentum equation. The model naturally resolves the in-canopy velocity reduction due to vegetation, so a spectral dissipation model derived from a (more) accurate in-canopy velocity field can be derived, whereby eliminating the commonly used assumption of validity of linear wave theory within the canopy. The new dissipation model is applied for validation against 137 laboratory studies for emergent, submerged, rigid, and flexible canopies, and it is concluded that an accurate spectral wave transformation can be achieved by utilizing a single and fixed set of force coefficients (hydrodynamic drag and inertia). The model is also applied to the understanding of (i) the common hyperbolic form of closure coefficients in the literature and (ii) the transformation of single- and double-peaked wave spectra through canopies and its importance to the change in spectral wave periods.
{"title":"A theoretical approach for irregular wave attenuation through vegetation and application to spectral wave models","authors":"Niels Gjøl Jacobsen","doi":"10.1016/j.apor.2025.104915","DOIUrl":"10.1016/j.apor.2025.104915","url":null,"abstract":"<div><div>The present work proposes an analytical model for the irregular wave field in submerged and emergent canopies, and the model is based on linear superposition of the solution to the linearized momentum equation. The model naturally resolves the in-canopy velocity reduction due to vegetation, so a spectral dissipation model derived from a (more) accurate in-canopy velocity field can be derived, whereby eliminating the commonly used assumption of validity of linear wave theory within the canopy. The new dissipation model is applied for validation against 137 laboratory studies for emergent, submerged, rigid, and flexible canopies, and it is concluded that an accurate spectral wave transformation can be achieved by utilizing a single and fixed set of force coefficients (hydrodynamic drag and inertia). The model is also applied to the understanding of (i) the common hyperbolic form of closure coefficients in the literature and (ii) the transformation of single- and double-peaked wave spectra through canopies and its importance to the change in spectral wave periods.</div></div>","PeriodicalId":8261,"journal":{"name":"Applied Ocean Research","volume":"166 ","pages":"Article 104915"},"PeriodicalIF":4.4,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145921075","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}
Accurate detection of buried objects in marine sediments remains a challenging task due to the complex seafloor conditions and the low signal-to-noise ratios of target echo signals. To address this issue, this paper presents a deep learning-based approach for detecting buried objects and obtaining their information in the marine sediments. In this method, time-frequency spectrograms generated from sub-bottom profiling acoustic pressure signals are utilized as input data, and the SBONet architecture is proposed to accurately identify targets. The SBONet employs data augmentation strategies on time-frequency spectrograms to enhance deep-level feature extraction. It incorporates a Transformer encoder for global feature extraction and integrates a Multi-Layer Perceptron for multi-task classification of target material properties, geometric shapes, and burial depths. To address the limited availability of field data, this paper constructs a comprehensive simulated dataset using Finite Element Method modeling of multi-layered marine sediments containing various buried targets. The effectiveness of the proposed method is validated using field data collected from the southern waters near the mouth of Hangzhou Bay. Experimental results demonstrate that SBONet achieves superior prediction accuracy on both noise-free and Gaussian noise-augmented datasets, outperforming existing mainstream models. Additionally, the method exhibits high prediction accuracy when applied to actual sub-bottom profiling data of buried pipelines in the southern waters near the mouth of Hangzhou Bay. The research findings validate the feasibility and effectiveness of integrating physical modeling with deep learning approaches for buried objects detection in complex marine environments.
{"title":"Deep learning-based detection of buried objects in marine sediments using time-frequency spectrograms from sub-bottom profiling data","authors":"Lingyi Cong , Jianglong Zheng , Luotao Zhang , Shuyue Liu , Qingjie Zhou , Xinghua Zhou , Xiaobo Zhang","doi":"10.1016/j.apor.2025.104911","DOIUrl":"10.1016/j.apor.2025.104911","url":null,"abstract":"<div><div>Accurate detection of buried objects in marine sediments remains a challenging task due to the complex seafloor conditions and the low signal-to-noise ratios of target echo signals. To address this issue, this paper presents a deep learning-based approach for detecting buried objects and obtaining their information in the marine sediments. In this method, time-frequency spectrograms generated from sub-bottom profiling acoustic pressure signals are utilized as input data, and the SBONet architecture is proposed to accurately identify targets. The SBONet employs data augmentation strategies on time-frequency spectrograms to enhance deep-level feature extraction. It incorporates a Transformer encoder for global feature extraction and integrates a Multi-Layer Perceptron for multi-task classification of target material properties, geometric shapes, and burial depths. To address the limited availability of field data, this paper constructs a comprehensive simulated dataset using Finite Element Method modeling of multi-layered marine sediments containing various buried targets. The effectiveness of the proposed method is validated using field data collected from the southern waters near the mouth of Hangzhou Bay. Experimental results demonstrate that SBONet achieves superior prediction accuracy on both noise-free and Gaussian noise-augmented datasets, outperforming existing mainstream models. Additionally, the method exhibits high prediction accuracy when applied to actual sub-bottom profiling data of buried pipelines in the southern waters near the mouth of Hangzhou Bay. The research findings validate the feasibility and effectiveness of integrating physical modeling with deep learning approaches for buried objects detection in complex marine environments.</div></div>","PeriodicalId":8261,"journal":{"name":"Applied Ocean Research","volume":"166 ","pages":"Article 104911"},"PeriodicalIF":4.4,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145921079","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 : 2026-01-01DOI: 10.1016/j.apor.2026.104920
Dana Salar, Antoine Dupuis, Jens Engström, Erik Hultman
Wave Energy Converter (WEC) technology has for a long time captured the interest of researchers, in the strive to increase and diversify the share of renewables in our global energy system. The development of WECs is however challenging due to the time-consuming and expensive open sea experiments required. Controlled wave tank testing is therefore often used, but suffer from the limited availability, scale and wave conditions that can be achieved. Another option is dry test rigs, utilizing a mechanical actuator to emulate WEC operation in ocean waves. Achieving realistic tests is however a challenge.
This work focuses on a robotized dry test rig, providing a cost-effective, industrial and flexible test concept for one-body and two-body emulation of point-absorber WECs in all six degree of freedom. A numerical linear potential flow hydrodynamic force model for simulating the motions in irregular waves is presented and evaluated against wave tank experiments, before being implemented on the robot controller. Test rig experiments based on a simulated WEC damping force and assuming a one-body system acting purely in heave are presented.
We successfully demonstrated WEC operation emulation in irregular waves with the robot test rig, and were also able to evaluate its accuracy. It can be concluded that the robot performs well in relation to the numerical model, while the numerical model performs satisfying mainly for smaller and non-steep waves. Further work is therefore suggested on expanding the emulation to several degrees of freedom and also to include a physical WEC power take-off unit.
{"title":"Emulating Wave Energy Converter operation in irregular waves using a robotized dry test rig","authors":"Dana Salar, Antoine Dupuis, Jens Engström, Erik Hultman","doi":"10.1016/j.apor.2026.104920","DOIUrl":"10.1016/j.apor.2026.104920","url":null,"abstract":"<div><div>Wave Energy Converter (WEC) technology has for a long time captured the interest of researchers, in the strive to increase and diversify the share of renewables in our global energy system. The development of WECs is however challenging due to the time-consuming and expensive open sea experiments required. Controlled wave tank testing is therefore often used, but suffer from the limited availability, scale and wave conditions that can be achieved. Another option is dry test rigs, utilizing a mechanical actuator to emulate WEC operation in ocean waves. Achieving realistic tests is however a challenge.</div><div>This work focuses on a robotized dry test rig, providing a cost-effective, industrial and flexible test concept for one-body and two-body emulation of point-absorber WECs in all six degree of freedom. A numerical linear potential flow hydrodynamic force model for simulating the motions in irregular waves is presented and evaluated against wave tank experiments, before being implemented on the robot controller. Test rig experiments based on a simulated WEC damping force and assuming a one-body system acting purely in heave are presented.</div><div>We successfully demonstrated WEC operation emulation in irregular waves with the robot test rig, and were also able to evaluate its accuracy. It can be concluded that the robot performs well in relation to the numerical model, while the numerical model performs satisfying mainly for smaller and non-steep waves. Further work is therefore suggested on expanding the emulation to several degrees of freedom and also to include a physical WEC power take-off unit.</div></div>","PeriodicalId":8261,"journal":{"name":"Applied Ocean Research","volume":"166 ","pages":"Article 104920"},"PeriodicalIF":4.4,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145921077","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 : 2026-01-01DOI: 10.1016/j.apor.2025.104910
Yang Liu, Xiaolong He, Jianmin Zhang
Aeration is a widely adopted and effective approach for mitigating cavitation erosion in hydraulic engineering. The erosion-mitigating effect of aeration depends on the interaction between cavitation bubbles and air bubbles, particularly on the microscopic dynamics of shock wave emission during the collapse of cavitation bubbles, which plays a vital role in determining the severity of cavitation. This study investigates the interaction between cavitation and air bubbles using a three-phase 74compressible phase-change model. The results show that shock wave effects depend critically on the relative sizes and separation distances of the bubbles. The dimensionless Kelvin impulse (anisotropy parameter ) is introduced to analyze the relationship between bubble impulse, shock wave energy, and emission timing/location. As increases from 0 to 0.3, the proportion of energy released by the cavitation bubble initially decreases and then increases, reaching a minimum at approximately ≈ 0.15, where the energy contribution is around 75%. When 0 < < 0.15, the position of the shock wave release exhibits a linear relationship with . Further analysis demonstrates the following: Small air bubbles generate an attractive force on cavitation bubbles, steering the micro-jet toward the air bubble. When the air bubble size is 1–2 times that of the vapor bubble, a power-law relationship emerges between and the interaction strength parameter . During the initial oscillation cycle of the vapor bubble, the air bubble generally possesses a positive , indicating repulsion from the vapor bubble, whereas the vapor bubble exhibits a negative , indicating attraction toward the air bubble.
{"title":"Characteristics of the pressure wave between compressible vapor bubble and air bubble in an infinite domain","authors":"Yang Liu, Xiaolong He, Jianmin Zhang","doi":"10.1016/j.apor.2025.104910","DOIUrl":"10.1016/j.apor.2025.104910","url":null,"abstract":"<div><div>Aeration is a widely adopted and effective approach for mitigating cavitation erosion in hydraulic engineering. The erosion-mitigating effect of aeration depends on the interaction between cavitation bubbles and air bubbles, particularly on the microscopic dynamics of shock wave emission during the collapse of cavitation bubbles, which plays a vital role in determining the severity of cavitation. This study investigates the interaction between cavitation and air bubbles using a three-phase 74compressible phase-change model. The results show that shock wave effects depend critically on the relative sizes and separation distances of the bubbles. The dimensionless Kelvin impulse (anisotropy parameter <span><math><mrow><mi>ζ</mi></mrow></math></span>) is introduced to analyze the relationship between bubble impulse, shock wave energy, and emission timing/location. As <span><math><mrow><mi>ζ</mi></mrow></math></span> increases from 0 to 0.3, the proportion of energy released by the cavitation bubble initially decreases and then increases, reaching a minimum at approximately <span><math><mrow><mi>ζ</mi></mrow></math></span> ≈ 0.15, where the energy contribution is around 75%. When 0 < <span><math><mrow><mi>ζ</mi></mrow></math></span> < 0.15, the position of the shock wave release exhibits a linear relationship with <span><math><mrow><mi>ζ</mi></mrow></math></span>. Further analysis demonstrates the following: Small air bubbles generate an attractive force on cavitation bubbles, steering the micro-jet toward the air bubble. When the air bubble size is 1–2 times that of the vapor bubble, a power-law relationship emerges between <span><math><mrow><mi>ζ</mi></mrow></math></span> and the interaction strength parameter <span><math><mi>γ</mi></math></span>. During the initial oscillation cycle of the vapor bubble, the air bubble generally possesses a positive <span><math><mrow><mi>ζ</mi></mrow></math></span>, indicating repulsion from the vapor bubble, whereas the vapor bubble exhibits a negative <span><math><mrow><mi>ζ</mi></mrow></math></span>, indicating attraction toward the air bubble.</div></div>","PeriodicalId":8261,"journal":{"name":"Applied Ocean Research","volume":"166 ","pages":"Article 104910"},"PeriodicalIF":4.4,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145921067","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 : 2026-01-01DOI: 10.1016/j.apor.2025.104899
Hengxu Liu , Yupeng Duan , Hailong Chen , Hongru Liu , Chongfei Sun
Estimating mooring tension on offshore floating photovoltaic (OFPV) platforms is critical for ensuring the safety of the platform and has significant implications for its operation and maintenance. This study develops machine learning models for predicting mooring tensions in OFPV systems based on three neural network architectures (backpropagation neural networks (BP), gated recurrent units (GRU), and long short-term memory networks (LSTM)). The training data were computationally generated using OrcaFlex software, where the motion data of the OFPV platforms and corresponding mooring tensions served as training datasets for the three machine learning models. Through comparative analysis of prediction accuracy under various environmental parameters, the LSTM model demonstrated optimal performance in both computational efficiency and training economy. This comparative study provides valuable references for mooring tension prediction in OFPV array.
{"title":"Evaluation study of predicting the dynamic mooring tension of offshore floating photovoltaic array using machine learning","authors":"Hengxu Liu , Yupeng Duan , Hailong Chen , Hongru Liu , Chongfei Sun","doi":"10.1016/j.apor.2025.104899","DOIUrl":"10.1016/j.apor.2025.104899","url":null,"abstract":"<div><div>Estimating mooring tension on offshore floating photovoltaic (OFPV) platforms is critical for ensuring the safety of the platform and has significant implications for its operation and maintenance. This study develops machine learning models for predicting mooring tensions in OFPV systems based on three neural network architectures (backpropagation neural networks (BP), gated recurrent units (GRU), and long short-term memory networks (LSTM)). The training data were computationally generated using OrcaFlex software, where the motion data of the OFPV platforms and corresponding mooring tensions served as training datasets for the three machine learning models. Through comparative analysis of prediction accuracy under various environmental parameters, the LSTM model demonstrated optimal performance in both computational efficiency and training economy. This comparative study provides valuable references for mooring tension prediction in OFPV array.</div></div>","PeriodicalId":8261,"journal":{"name":"Applied Ocean Research","volume":"166 ","pages":"Article 104899"},"PeriodicalIF":4.4,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145921176","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 : 2026-01-01DOI: 10.1016/j.apor.2026.104931
Qian Yang , Mai Cui , Jianmin Zhang , Shicheng Li , Xiaolong He
In the present study, an improved thermal multi-component multiphase (MCMP) lattice Boltzmann model is proposed by introducing a non-orthogonal transformation matrix and multi-range inter- and intra-particle interaction forces to enhance numerical stability. The model successfully captures multiple oscillation cycles of a vapor bubble with non-condensable gas (NCG) and resolves the immiscibility problem between vapor and air commonly observed in macroscopic MCMP bubble models. Additionally, the model is applied to investigate bubble dynamics near a solid wall, with a focus on the effects of NCG content on collapse intensity. Results show that higher NCG content leads to increased initial internal pressure, resulting in a larger maximum radius and prolonged collapse time. However, the compressibility of the bubble during the collapse stage decreases, weakening the collapse strength. The NCG mass inside the bubble exhibits a decrease–increase–decrease trend during the first oscillation cycle, which is influenced by interfacial mass transfer. Besides, the existence of the NCG concentration ensures non-zero vapor content at the bubble’s minimum radius, significantly affecting the phase change behavior during the bubble evolution process.
{"title":"Simulation of near-wall explosion bubble with non-condensable gas evolution via a modified multicomponent and multiphase lattice Boltzmann model","authors":"Qian Yang , Mai Cui , Jianmin Zhang , Shicheng Li , Xiaolong He","doi":"10.1016/j.apor.2026.104931","DOIUrl":"10.1016/j.apor.2026.104931","url":null,"abstract":"<div><div>In the present study, an improved thermal multi-component multiphase (MCMP) lattice Boltzmann model is proposed by introducing a non-orthogonal transformation matrix and multi-range inter- and intra-particle interaction forces to enhance numerical stability. The model successfully captures multiple oscillation cycles of a vapor bubble with non-condensable gas (NCG) and resolves the immiscibility problem between vapor and air commonly observed in macroscopic MCMP bubble models. Additionally, the model is applied to investigate bubble dynamics near a solid wall, with a focus on the effects of NCG content on collapse intensity. Results show that higher NCG content leads to increased initial internal pressure, resulting in a larger maximum radius and prolonged collapse time. However, the compressibility of the bubble during the collapse stage decreases, weakening the collapse strength. The NCG mass inside the bubble exhibits a decrease–increase–decrease trend during the first oscillation cycle, which is influenced by interfacial mass transfer. Besides, the existence of the NCG concentration ensures non-zero vapor content at the bubble’s minimum radius, significantly affecting the phase change behavior during the bubble evolution process.</div></div>","PeriodicalId":8261,"journal":{"name":"Applied Ocean Research","volume":"166 ","pages":"Article 104931"},"PeriodicalIF":4.4,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145972748","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 : 2026-01-01DOI: 10.1016/j.apor.2026.104933
Kai Yin, Yingxiang Lu, Sudong Xu, Weikai Tan, Chunyu Liu
Wave attenuation by flexible vegetation is attracting increasing scholarly attention due to its coastal protection and ecological benefits. Although satisfactory progress has been made in understanding flexible vegetation dynamics and the resulting wave attenuation, existing numerical studies are primarily limited to the assumption of submerged vegetation. This study set out to establish a numerical simulation method for wave attenuation by emergent flexible vegetation and to investigate the corresponding wave attenuation characteristics. To this end, the XBeach phase-averaged wave model was extended by incorporating the emergent flexible vegetation dynamic model. The performance of this extended model in simulating wave attenuation by emergent flexible vegetation was validated against conducted flume experiments. Experimental results indicated that increasing wave steepness, drag-to-stiffness ratio, relative wave height (wave height/water depth), and relative vegetation height (stem length/water depth) generally resulted in higher damping coefficients. A new drag coefficient formula accounting for vegetation flexibility and relative vegetation height was developed using a genetic programming algorithm. Within the parameters utilized in this investigation, simulation results demonstrated a positive relationship between the damping coefficient and the relative vegetation height, and this relationship was stronger under vegetation conditions with higher stiffness. These findings expand the applicability of numerical models for vegetation–wave interactions while contributing to a better understanding of wave attenuation by emergent flexible vegetation.
{"title":"Experimental and numerical investigation of wave attenuation by emergent flexible vegetation","authors":"Kai Yin, Yingxiang Lu, Sudong Xu, Weikai Tan, Chunyu Liu","doi":"10.1016/j.apor.2026.104933","DOIUrl":"10.1016/j.apor.2026.104933","url":null,"abstract":"<div><div>Wave attenuation by flexible vegetation is attracting increasing scholarly attention due to its coastal protection and ecological benefits. Although satisfactory progress has been made in understanding flexible vegetation dynamics and the resulting wave attenuation, existing numerical studies are primarily limited to the assumption of submerged vegetation. This study set out to establish a numerical simulation method for wave attenuation by emergent flexible vegetation and to investigate the corresponding wave attenuation characteristics. To this end, the XBeach phase-averaged wave model was extended by incorporating the emergent flexible vegetation dynamic model. The performance of this extended model in simulating wave attenuation by emergent flexible vegetation was validated against conducted flume experiments. Experimental results indicated that increasing wave steepness, drag-to-stiffness ratio, relative wave height (wave height/water depth), and relative vegetation height (stem length/water depth) generally resulted in higher damping coefficients. A new drag coefficient formula accounting for vegetation flexibility and relative vegetation height was developed using a genetic programming algorithm. Within the parameters utilized in this investigation, simulation results demonstrated a positive relationship between the damping coefficient and the relative vegetation height, and this relationship was stronger under vegetation conditions with higher stiffness. These findings expand the applicability of numerical models for vegetation–wave interactions while contributing to a better understanding of wave attenuation by emergent flexible vegetation.</div></div>","PeriodicalId":8261,"journal":{"name":"Applied Ocean Research","volume":"166 ","pages":"Article 104933"},"PeriodicalIF":4.4,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145972749","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 : 2026-01-01DOI: 10.1016/j.apor.2026.104927
Do-Soo Kwon , Chungkuk Jin , MooHyun Kim , Sung-Jae Kim
This study presents a machine learning (ML) framework for inverse estimation of parameters of multi-directional waves from moored FPSO (floating production storage offloading) motion-sensor synthetic data. A time-domain hull/mooring/riser coupled dynamics numerical simulation program was used to generate realistic vessel-response time series under varying wind–wave–current conditions, from which motion-statistical features up to 122 were extracted. These statistical features served as inputs to two different ML models, artificial neural networks (ANNs) and transformer-based ensemble (TBE) model. Then, different combinations of motion-statistical features were selected as inputs to the ML models to estimate key spectral parameters of multi-directional-waves including significant wave height, peak period, mean wave direction, spectral enhancement (peakedness) factor, and directional spreading, and the results were systematically compared. A more advanced ML method, the transformer architecture, combined with an ensemble approach, demonstrated improved robustness and generality across complex sea states. The systematic comparisons of ML performances with measured wave parameters versus artificially-generated wave parameters provided insights into how the hidden intrinsic correlations among wave parameters can improve the overall performance of the ML-based inverse wave estimation. The results highlight the potential of FPSOs as near-real-time wave-sensing devices. The estimated parameters can serve as crucial inputs for optimizing dynamic positioning (DP) systems and other active controls, as well as for digital-twin and smart-ship/platform applications, reducing reliance on external measurement systems.
{"title":"Transformers and neural networks for estimation of parameters of multi-directional waves from rich statistics of FPSO motion signals","authors":"Do-Soo Kwon , Chungkuk Jin , MooHyun Kim , Sung-Jae Kim","doi":"10.1016/j.apor.2026.104927","DOIUrl":"10.1016/j.apor.2026.104927","url":null,"abstract":"<div><div>This study presents a machine learning (ML) framework for inverse estimation of parameters of multi-directional waves from moored FPSO (floating production storage offloading) motion-sensor synthetic data. A time-domain hull/mooring/riser coupled dynamics numerical simulation program was used to generate realistic vessel-response time series under varying wind–wave–current conditions, from which motion-statistical features up to 122 were extracted. These statistical features served as inputs to two different ML models, artificial neural networks (ANNs) and transformer-based ensemble (TBE) model. Then, different combinations of motion-statistical features were selected as inputs to the ML models to estimate key spectral parameters of multi-directional-waves including significant wave height, peak period, mean wave direction, spectral enhancement (peakedness) factor, and directional spreading, and the results were systematically compared. A more advanced ML method, the transformer architecture, combined with an ensemble approach, demonstrated improved robustness and generality across complex sea states. The systematic comparisons of ML performances with measured wave parameters versus artificially-generated wave parameters provided insights into how the hidden intrinsic correlations among wave parameters can improve the overall performance of the ML-based inverse wave estimation. The results highlight the potential of FPSOs as near-real-time wave-sensing devices. The estimated parameters can serve as crucial inputs for optimizing dynamic positioning (DP) systems and other active controls, as well as for digital-twin and smart-ship/platform applications, reducing reliance on external measurement systems.</div></div>","PeriodicalId":8261,"journal":{"name":"Applied Ocean Research","volume":"166 ","pages":"Article 104927"},"PeriodicalIF":4.4,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145972751","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 : 2026-01-01DOI: 10.1016/j.apor.2025.104913
Changpeng Zhang , Xin Zhao
The integration of advanced wave scattering physics into operational forecast systems like WAVEWATCH III is often hindered by the computational complexity of high-fidelity models. While the diffusion approximation framework of Zhao and Shen (2016) offers a promising alternative to the full Boltzmann equation, its requirement to solve for multiple coupled auxiliary variables (e.g., transmitted and scattered components) presents a significant barrier to practical implementation. To overcome this challenge, this study proposes a novel algorithmic simplification that enhances the model's computational efficiency and tractability. Our key innovation is the introduction of an effective mean action density variable, , formed by combining the transmitted energy and the isotropically redistributed scattered energy. This unification reduces the system's dimensionality, eliminating one prognostic equation and streamlining numerical integration. Validation against benchmark solutions demonstrates that the proposed model accurately captures the directional spreading of wave energy while offering a more computationally efficient pathway. By providing a streamlined and operationally viable framework, this work bridges a critical gap between theoretically rigorous scattering models and the demands of large-scale forecasting.
{"title":"A practical diffusion approximation model for wave scattering by Ice Floes","authors":"Changpeng Zhang , Xin Zhao","doi":"10.1016/j.apor.2025.104913","DOIUrl":"10.1016/j.apor.2025.104913","url":null,"abstract":"<div><div>The integration of advanced wave scattering physics into operational forecast systems like WAVEWATCH III is often hindered by the computational complexity of high-fidelity models. While the diffusion approximation framework of Zhao and Shen (2016) offers a promising alternative to the full Boltzmann equation, its requirement to solve for multiple coupled auxiliary variables (e.g., transmitted and scattered components) presents a significant barrier to practical implementation. To overcome this challenge, this study proposes a novel algorithmic simplification that enhances the model's computational efficiency and tractability. Our key innovation is the introduction of an effective mean action density variable, <span><math><msub><mi>N</mi><mrow><mi>e</mi><mi>f</mi><mi>f</mi></mrow></msub></math></span>, formed by combining the transmitted energy and the isotropically redistributed scattered energy. This unification reduces the system's dimensionality, eliminating one prognostic equation and streamlining numerical integration. Validation against benchmark solutions demonstrates that the proposed model accurately captures the directional spreading of wave energy while offering a more computationally efficient pathway. By providing a streamlined and operationally viable framework, this work bridges a critical gap between theoretically rigorous scattering models and the demands of large-scale forecasting.</div></div>","PeriodicalId":8261,"journal":{"name":"Applied Ocean Research","volume":"166 ","pages":"Article 104913"},"PeriodicalIF":4.4,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145921082","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 : 2026-01-01DOI: 10.1016/j.apor.2026.104921
David Okushemiya , Curtis J. Rusch , Bryson Robertson , Zhe Zhang
Rigorous incremental testing and validation are essential to advancing wave energy converter (WEC) technology. Although laboratory wave tank testing remains common, it poses challenges in scaling hydrodynamic responses and power take-off (PTO) dynamics. These issues are more pronounced for WECs with tethered heave plates due to complex interactions between the structure, tether, heave plate, and PTO; all of which often exceed tank depth and scaling limits. Field testing enables full-system evaluation but introduces practical limitations, including environmental variability, limited sensing, and measurement uncertainty. A knowledge gap remains in how to overcome these limitations to extract meaningful insights and validate WEC numerical models using field test data. Moreover, full-scale PTOs exhibit significant nonlinearities, such as generator inertia, internal losses, and inefficiencies across the full energy conversion chain, that are not captured in current PTO models. This highlights the need for improved modeling techniques to realistically estimate useful power and energy output. This study uses a field-deployed WEC with a tethered heave plate to demonstrate how combining statistical and spectral analyses enables comprehensive insight and validation of WEC models using field data. It also advances PTO modeling by incorporating generator inertia and fitting a parametric relationship between shaft speed and useful power based on PTO dynamometer test data. This approach predicted power and energy within 9% of field measurements, whereas conventional models overestimated these output by up to a factor of 3. The improved PTO modeling yields more realistic levelized cost of energy (LCOE) estimates to better guide future full-scale WEC development.
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