Applied Ocean Research最新文献_第5页

Chloride transport in estuarine ship-lock concrete structure exposed to a unique seawater corrosive environment with low chlorine-salt concentrations and high-frequency drying‒wetting cycles: Experimental study and computational model 低氯盐、高频干湿循环海水腐蚀环境下河口船闸混凝土结构氯离子运移：实验研究与计算模型

IF 4.4 2区工程技术 Q1 ENGINEERING, OCEAN

Applied Ocean Research

Pub Date : 2026-01-01 DOI: 10.1016/j.apor.2025.104912

Linjian Wu , Bingli Peng , Jichao Lei , Tingting Li , Mingwei Liu , Bo Liu , Yitao Xiao

Estuarine ship-locks are the navigational structures closest to the sea, which can form a unique seawater corrosive environment characterized by low chlorine-salt concentrations and high-frequency drying‒wetting cycles. Such an environment is caused by the downstream saltwater tides infiltrating into the ship-lock chamber and coupled with the filling and drainage water processes during the ship-lock operation. This unique seawater corrosive environment can seriously threaten the durability of estuarine ship-locks concrete structures. Currently, the chloride transport mechanisms in concrete for estuarine ship-locks under such a unique seawater corrosive environment remain elusive. Particularly, the transport characteristics of chloride invasion into estuarine ship-lock concrete structures, driven by drying-wetting cycle environmental factors, necessitate further study. This paper investigates the effects of drying‒wetting cycle environmental factors of the unique seawater corrosive environment, including the environmental chloride salt concentration, the drying‒wetting frequency, and the drying‒wetting ratio, on the chloride transport in estuarine ship-lock concrete structure by carrying out a physical experimental study. Results indicated an "M"-shaped trend in chloride concentration, surface chloride concentration, and chloride diffusion coefficient of concrete with an increasing drying-wetting ratio. Additionally, the aforementioned chloride transport parameters exhibited a positive correlation with the increase of drying-wetting frequency and environmental chlorine-salt concentration. Notably, the environmental chlorine-salt concentration had the greatest influence on chloride transport, whereas the high-frequency drying-wetting cycles can diminish the promotion degree of environmental chlorine-salt concentration on chloride transport behaviors in concrete. Furthermore, the time-varying patterns of surface chloride concentration and apparent chloride diffusion coefficient in the estuarine ship-lock concrete structures, subjected to different drying-wetting ratios under low chlorine-salt concentrations and high-frequency drying‒wetting cycles, were quantified, and the mathematical relationship between the drying-wetting ratios and ship-lock structural elevation was derived. On the basis of Fick's second law, a computational model of chloride transport in estuarine ship-lock concrete structure was developed, incorporating the influence of structural elevation, i.e., drying-wetting ratios.

河口船闸是离海最近的航行构筑物，可形成氯盐浓度低、干湿循环频率高的独特海水腐蚀环境。这种环境是由于船闸运行过程中下游咸水潮渗入船闸室，再加上进水和排水过程造成的。这种独特的海水腐蚀环境会严重威胁入海口船闸混凝土结构的耐久性。目前，在这种独特的海水腐蚀环境下，河口船闸混凝土中氯离子的输运机制尚不清楚。特别是在干湿循环环境因素的驱动下，氯离子侵入河口船闸混凝土结构的输运特性需要进一步研究。本文通过物理实验研究，探讨了独特的海水腐蚀环境中干湿循环环境因子，包括环境氯盐浓度、干湿频率和干湿比对河口船闸混凝土结构中氯离子迁移的影响。结果表明：随着干湿比的增大，混凝土氯离子浓度、表面氯离子浓度和氯离子扩散系数均呈“M”型趋势；此外，上述氯离子输运参数与干湿频率和环境氯盐浓度的增加呈正相关。环境氯盐浓度对氯离子在混凝土中的迁移行为影响最大，而高频率干湿循环会降低环境氯盐浓度对混凝土中氯离子迁移行为的促进程度。在低氯盐浓度和高频率干湿循环条件下，定量分析了不同干湿比下河口船闸混凝土结构表面氯离子浓度和表观氯离子扩散系数的时变规律，推导了干湿比与船闸结构高程之间的数学关系。在菲克第二定律的基础上，建立了考虑结构高程（干湿比）影响的河口船闸混凝土结构氯离子输运计算模型。

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引用次数: 0

Designing robust adaptive ensemble deep learning based decomposition technique for sea level variability prediction 基于鲁棒自适应集成深度学习的海平面变率预测分解技术设计

IF 4.4 2区工程技术 Q1 ENGINEERING, OCEAN

Applied Ocean Research

Pub Date : 2026-01-01 DOI: 10.1016/j.apor.2026.104925

Mohammed Diykh , Mumtaz Ali , Aitazaz Ahsan Farooque , Anwar Ali Aldhafeeri , Abdulhaleem H. Labban

Sea level variability is an urgent climate risk, threatening to vanish islands and coastal areas. Forecasting future sea level rise accurately is fundamental to support experts for flooding and erosion control. In this paper, a novel sea level variability forecast model (CG-CEEMDANHFS-AEM) is proposed integrating correlation graph (CG), a complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), Hilbert feature selection approach (HFS), adaptive ensemble model (AEM), and oppositional learning sparrow search algorithm (OLSSA). The AEM is a novel ensemble model that combines the strengths of GRU, self-attention LSTM and XGBoost models based on dynamic weights assignment strategy, adjusting to real-time changes in sea level rise by updating the weights according to the error and performance of the models. Firstly, the input data is pre-processed using correlation graph to remove lower correlated variables and fill the missing values in the data. After that the CEEMDAN technique is employed to decompose the data, followed by HFS to select the most efficient features. The selected features are then employed into the AEM model where the OLSSA is adopted to select the optimal hyper-parameters of the proposed model. To verify the efficiency of the proposed CG-CEEMDANHFS-AEM against comparing models, extensive experiments were conducted to forecast sea level variability for Hillary and Burnie stations in Australia. The results shows that the proposed model obtained the highest accuracy in terms of goodness-of-fit metrics against the state-of-the-art benchmark comparing models. The proposed model can offer a valuable tool for coastal planning and policy making under the recent climate change.

海平面变化是一个紧迫的气候风险，有可能使岛屿和沿海地区消失。准确预测未来海平面上升是支持洪水和侵蚀控制专家的基础。结合相关图（CG）、带自适应噪声的完整集合经验模态分解（CEEMDAN）、Hilbert特征选择方法（HFS）、自适应集合模型（AEM）和对立学习麻雀搜索算法（OLSSA），提出了一种新的海平面变率预测模型CG- ceemdanhfs -AEM。AEM是一种基于动态权值分配策略，结合GRU、自关注LSTM和XGBoost模型优势的新型集成模型，通过根据模型误差和性能更新权值来适应海平面上升的实时变化。首先，利用相关图对输入数据进行预处理，去除相关性较低的变量，填充数据中的缺失值。然后使用CEEMDAN技术对数据进行分解，再使用HFS选择最有效的特征。然后将所选择的特征应用到AEM模型中，其中采用OLSSA来选择所提出模型的最优超参数。为了验证所提出的CG-CEEMDANHFS-AEM与比较模式的效率，进行了大量的实验来预测澳大利亚希拉里和伯尼站的海平面变化。结果表明，所提出的模型在拟合优度指标方面与最先进的基准比较模型相比获得了最高的精度。该模型可为近期气候变化下的沿海规划和政策制定提供有价值的工具。

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引用次数: 0

Integrated berthing risk assessment framework using a combination of machine learning with measured berthing velocity 综合靠泊风险评估框架，结合机器学习和测量靠泊速度

IF 4.4 2区工程技术 Q1 ENGINEERING, OCEAN

Applied Ocean Research

Pub Date : 2026-01-01 DOI: 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.

港口码头面临着日益增长的运营需求，这往往导致靠泊速度的提高和靠泊设施的潜在结构损坏。传统的靠泊设计准则主要依赖于静态速度阈值，而没有考虑船舶特性、环境条件和海洋基础设施结构响应之间的动态相互作用。本研究提出了一个综合的靠泊风险评估框架，该框架将测量靠泊速度与机器混合机器学习方法相结合，以增强港口码头安全和基础设施保护。在2017-2022年的5年时间里，使用基于激光的停靠辅助系统在一个主要的油轮码头进行了现场测量，在三个不同船舶容量的码头捕获了900多个靠泊事件。所提出的方法采用k均值聚类来确定各种影响因素的风险水平，包括天气条件、船舶细节和人为因素（如飞行员经验）。结合多种机器学习算法来确定每个风险类别的最佳权重因子，从而实现全面的风险量化。开发的模型在高风险情况下的检测率约为79%，成功识别出52例中超过安全标准的41例。与单个模型相比，集成方法将多种算法与性能加权系数相结合，显示出更高的准确性。提出的方法能够在靠泊作业前进行定量风险评估，为港口作业提供早期预警能力和客观决策支持。

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引用次数: 0

A massive MPI parallel framework of 3D local discontinuous Galerkin method for simulating large-scale underwater explosion problems 模拟大规模水下爆炸问题的三维局部不连续Galerkin方法的海量MPI并行框架

IF 4.4 2区工程技术 Q1 ENGINEERING, OCEAN

Applied Ocean Research

Pub Date : 2026-01-01 DOI: 10.1016/j.apor.2025.104895

Zhaoxu Lian , Wenbin Wu , Moubin Liu

The discontinuous Galerkin method (DGM) has showed appealing performances in addressing underwater explosion (UNDEX) problems with strong discontinuities. However, most of the DGM are limited to the axisymmetric modeling due to the expensive computational cost required in 3D simulations. In this work, we develop a massive MPI parallel framework of 3D local discontinuous Galerkin method (LDGM) for simulating large-scale UNDEX problems with the cavitation effects. The 3D LDGM based on unstructured tetrahedron elements is adopted to solve the wave equation about dynamic pressure excited by the UNDEX and simulate the propagation of the UNDEX shock waves in the fluid domain. To accelerate the calculation of 3D LDGM, the MPI parallel framework scalable on a high-performance computer is designed. In order to improve the parallel efficiency and expand the computational scalability, some optimized techniques are incorporated into the parallel 3D LDGM model. In particular, the information for data communication is stored contiguously in the access-friendly data structures to efficiently implement concurrent non-blocking communications. The real-time memory management strategies with local parallel I/O are developed in terms of reducing latency and restraining memory utilization. Several numerical examples are performed to assess the accuracy, efficiency and scalability of the MPI-based parallel 3D LDGM model. It is demonstrated that the present model can accurately and efficiently simulate the propagation of UNDEX shock waves and well capture the evolution of the cavitation region in 3D spaces. The parallel efficiency can achieve up to 94.1 % even for 155 million tetrahedral elements on 12,000 CPU cores.

不连续伽辽金方法（DGM）在求解强不连续的水下爆炸问题中表现出了令人满意的性能。然而，由于三维模拟的计算成本昂贵，大多数DGM都局限于轴对称建模。在这项工作中，我们开发了一个三维局部不连续伽辽金方法（LDGM）的大规模MPI并行框架，用于模拟具有空化效应的大规模UNDEX问题。采用基于非结构四面体单元的三维LDGM求解UNDEX激发的动压力波动方程，模拟UNDEX激波在流体域中的传播。为了加快三维LDGM的计算速度，设计了可在高性能计算机上扩展的MPI并行框架。为了提高并行效率和扩展计算可扩展性，在三维LDGM并行模型中引入了一些优化技术。特别地，将用于数据通信的信息连续存储在易访问的数据结构中，以有效地实现并发非阻塞通信。从降低延迟和限制内存使用的角度出发，提出了具有本地并行I/O的实时内存管理策略。通过数值算例验证了基于mpi的并行三维LDGM模型的精度、效率和可扩展性。实验结果表明，该模型能够准确、高效地模拟UNDEX冲击波的传播，并能很好地捕捉空化区域在三维空间中的演化过程。即使在12,000个CPU内核上处理1.55亿个四面体元素，并行效率也可以达到94.1%。

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引用次数: 0

Experimental validation of a modular navigation architecture for marine autonomous surface vehicles with reactive collision avoidance 基于响应式避碰的海上自主水面车辆模块化导航体系结构的实验验证

IF 4.4 2区工程技术 Q1 ENGINEERING, OCEAN

Applied Ocean Research

Pub Date : 2026-01-01 DOI: 10.1016/j.apor.2025.104903

Raphael Zaccone, Filippo Ponzini, Michele Martelli

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.

本文提出了一种基于分层感知、导航、制导和控制结构的地面车辆自主导航模块化软件体系结构。提出的框架分离了全局路径管理、避免碰撞的反应性局部规划和控制，而态势感知将激光雷达感知与INS/GNSS定位相结合，以保持对周围环境的最新和真实的表示。该体系结构是围绕模块化和可伸缩性的概念设计的，支持分布式计算和模块的灵活集成。该实现采用轻量级发布/订阅协议来实现模块之间的高效实时通信。本文报道并讨论了该架构在一辆研究型自动驾驶汽车避碰测试中的实验验证。车辆成功地执行多边形路径，调整其轨迹以避免多个意外障碍物，同时仍然到达指定的路径点。这些结果证明了该框架在实际操作条件下支持路径跟踪和自适应避碰的可靠性。

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引用次数: 0

Landslide impacts on built environment: Numerical analysis of the forces exerted by granular material collapsing on dry and submerged conditions 滑坡对建筑环境的影响：干燥和淹没条件下颗粒状物质崩塌力的数值分析

IF 4.4 2区工程技术 Q1 ENGINEERING, OCEAN

Applied Ocean Research

Pub Date : 2026-01-01 DOI: 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.

滑坡产生的影响对沿海和水库基础设施构成了严重的危害，但由于湍流、自由表面流动的复杂性，人们对其潜在的流体动力学知之甚少。在这项工作中，OpenFOAM®用于研究颗粒状滑坡对表面造成的突然影响。首先，用三组实验验证了数值框架：干颗粒流对倾斜水槽壁面的冲击，碎片雪崩对桥墩的冲击以及流体对垂直圆柱体的冲击溃坝相互作用。对于每一种情况，数值预测与实验在冲击力方面进行了比较，为求解器可以重现由颗粒和流体质量引起的突然载荷提供了信心。在此基础上，采用验证的数值模拟方法研究了滑坡体对细长圆柱体的冲击，捕捉了颗粒状滑坡体与自由表面的相互作用，解析了大尺度的螺旋结构及其在能量传递和耗散中的作用。结果强调了该现象的两个不同阶段：(i)初始撞击和射流形成阶段，以及（ii）湍流耗散和再循环阶段。该分析提供了对速度场、压力分布和湍流强度的定量见解，并确定了导致能量损失的关键机制。这些发现有助于加深对滑坡影响的物理认识，并为改进危害评估和防护结构的工程设计提供基础。

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引用次数: 0

POD-Galerkin reduced-order modeling of the El Niño-Southern Oscillation (ENSO) El Niño-Southern振荡（ENSO）的POD-Galerkin降阶模型

IF 4.4 2区工程技术 Q1 ENGINEERING, OCEAN

Applied Ocean Research

Pub Date : 2025-12-19 DOI: 10.1016/j.apor.2025.104893

Yusuf Aydogdu , Navaratnam Sri Namachchivaya

Reduced-order modeling (ROM) aims to mitigate computational complexity by reducing the size of a high-dimensional state space. In this study, we demonstrate the efficiency, accuracy, and stability of proper orthogonal decomposition (POD)-Galerkin ROM when applied to the El Niño-Southern Oscillation model, which integrates coupled atmosphere, ocean, and sea surface temperature (SST) mechanisms in the equatorial Pacific. While POD identifies the most energetic modes of a system from simulation data, the Galerkin projection maps the governing equations onto these reduced modes to derive a simplified dynamical system. Leveraging the unique coupling properties of the model, we propose a novel approach to formulate a reduced-order model derived from Galerkin projection. Our approach achieves remarkable computational efficiency, requiring only four POD modes. The results provide highly stable and accurate solutions over 95% compared to the high-dimensional full-order model (FOM), highlighting the potential of POD-Galerkin reduction for efficient and accurate climate simulations.

降阶建模（ROM）旨在通过减小高维状态空间的大小来降低计算复杂度。在这项研究中，我们证明了适当正交分解(POD)-Galerkin ROM应用于El Niño-Southern振荡模式时的效率、准确性和稳定性，该模式集成了赤道太平洋大气、海洋和海表温度（SST）耦合机制。当POD从模拟数据中识别系统的最高能量模式时，伽辽金投影将控制方程映射到这些简化模式上，从而推导出简化的动力系统。利用该模型独特的耦合特性，提出了一种基于伽辽金投影的降阶模型的构造方法。我们的方法实现了显著的计算效率，只需要四个POD模式。与高维全阶模型（FOM）相比，结果提供了95%以上的高度稳定和精确的解，突出了POD-Galerkin约简在高效和准确的气候模拟中的潜力。

引用次数: 0

Stern pressure identification using CNN-BiLSTM model based on bayesian optimisation and attention mechanism 基于贝叶斯优化和注意机制的CNN-BiLSTM模型艉部压力识别

IF 4.4 2区工程技术 Q1 ENGINEERING, OCEAN

Applied Ocean Research

Pub Date : 2025-12-18 DOI: 10.1016/j.apor.2025.104897

Weizhe Ren , Yao Wang , Xianqiang Qu , Hongbing Liu , Yuan Liu , Kai Liu , Yongxin Zhou

Accurate identification of the stern pressure field holds significant engineering implications for controlling hull structure vibrations and propeller excitation effects. In order to address the accuracy defects of the traditional distributed load inversion method when applied to complex surface structures, a baseline neural network model has been introduced and improved. Furthermore, a method of intelligent identification of stern pressure based on the Bayesian optimization algorithm and the attention mechanism of the CNN- BiLSTM model has been proposed. Taking the KCS container ship as the research object, we constructed a multi-physical-field coupled dataset containing 65 stern pressure measurement points and 9 structural strain measurement points through CFD simulations under five propeller rotational speed conditions. CNN and BiLSTM are used to extract spatio-temporal features from the dataset. Dynamic feature weight allocation is achieved through an attention mechanism, while Bayesian optimisation determines hyperparameter values to reduce bias. Results demonstrate that compared with basic models (CNN-BiLSTM, GRU, etc.), BOA-CNN-BiLSTM achieves optimal performance across MAE, MSE, RMSE, and MAPE metrics, with R² reaching 0.987. This method achieves high-precision and high-reliability reconstruction of multi-point pressure data at the stern through finite strain monitoring, providing an effective solution for distributed load identification in complex curved structures.

船尾压力场的准确识别对于控制船体结构振动和螺旋桨激励效应具有重要的工程意义。为了解决传统分布载荷反演方法在复杂表面结构中精度不足的问题，引入并改进了基线神经网络模型。在此基础上，提出了一种基于贝叶斯优化算法和CNN- BiLSTM模型注意机制的舰尾压力智能识别方法。以KCS集装箱船为研究对象，通过CFD模拟，构建了5种螺旋桨转速工况下包含65个尾压测点和9个结构应变测点的多物理场耦合数据集。使用CNN和BiLSTM从数据集中提取时空特征。动态特征权重分配通过注意机制实现，而贝叶斯优化确定超参数值以减少偏差。结果表明，与基本模型（CNN-BiLSTM、GRU等）相比，BOA-CNN-BiLSTM在MAE、MSE、RMSE和MAPE指标上的性能最优，R2达到0.987。该方法通过有限应变监测实现了艉部多点压力数据的高精度、高可靠性重构，为复杂弯曲结构的分布载荷识别提供了有效的解决方案。

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引用次数: 0

Shallow-water acoustic analysis with an accelerated isogeometric boundary element approach 用加速等几何边界元方法进行浅水声学分析

IF 4.4 2区工程技术 Q1 ENGINEERING, OCEAN

Applied Ocean Research

Pub Date : 2025-12-15 DOI: 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.

水声建模在海洋工程应用中起着至关重要的作用，特别是在复杂的边界相互作用主导声音传播的浅水环境中。传统的数值方法，如有限元法，经常受到无界域和人工边界条件的挑战。而等高边界元法（IGABEM）将边界元法的降维和无限域处理能力与等高分析的精确几何表示和光滑基函数相结合，提供了一种强大的替代方法。本文提出了一种新的IGABEM框架，专门用于浅水环境下的声学模拟，在浅水环境下，声音传播受海面和海底的耦合反射控制。此外，为了提高计算效率和可扩展性，我们集成并采用了先进的加速技术，包括快速多极子方法、Taylor展开频率解耦和二阶arnoldi算法。数值结果验证了该方法的精度、鲁棒性和计算优势，表明该方法是一种很有前途的高保真水声分析工具。

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引用次数: 0

A data-driven model for combined current-wave induced boundary layers 复合流波边界层的数据驱动模型

IF 4.4 2区工程技术 Q1 ENGINEERING, OCEAN

Applied Ocean Research

Pub Date : 2025-12-11 DOI: 10.1016/j.apor.2025.104877

Guang Yin , Muk Chen Ong , Svein Sævik , Janne Kristin Økland Gjøsteen , Egil Giertsen , Naiquan Ye

This study introduces a novel data-driven framework for efficiently predicting the time-varying velocity profiles of combined current-wave induced boundary layers, which are crucial for analyzing the on-bottom stability of near-seabed pipelines and cables. The characteristics of the combined wave-current boundary layer are strongly dependent on various parameters such as the wave period, the wave semi-excursion and the seabed roughness. Furthermore, there are also interactions between the current part and the wave part of the boundary layer, which influence the behaviors of their velocity profiles. To address these challenges, a data-driven model integrating one-dimensional Computational Fluid Dynamics (CFD) simulations and parametric mathematical expressions is proposed to predict the velocity profiles of the current and the first harmonic of the wave part for the boundary layer. CFD simulations are performed to generate the velocity profiles under different flow conditions and the database for the variables in the mathematical expression is built. An interpolation methodology based on the database is used to obtain the corresponding variables under a new given flow parameter set. This framework is then validated against numerical simulations and experimental data.

该研究提出了一种新的数据驱动框架，用于有效预测组合流波边界层的时变速度剖面，这对于分析近海底管道和电缆的海底稳定性至关重要。波流联合边界层的特征与波浪周期、波浪半偏移和海底粗糙度等参数密切相关。此外，边界层的电流部分和波部分之间还存在相互作用，影响了它们的速度分布特征。为了解决这些问题，提出了一种将一维计算流体力学（CFD）模拟和参数化数学表达式相结合的数据驱动模型，用于预测边界层的电流速度分布和波部分的一次谐波。通过CFD仿真得到了不同流动条件下的速度分布，并建立了数学表达式中变量的数据库。采用基于数据库的插值方法，在给定的流量参数集下得到相应的变量。然后通过数值模拟和实验数据验证了该框架。

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引用次数: 0