Surrogate modelling of dike wave overtopping hydrodynamics using an adapted deep learning Vision Transformer

IF 4.5 2区 工程技术 Q1 ENGINEERING, CIVIL
Wouter P. Schrama , Vera M. van Bergeijk , Patricia Mares-Nasarre , Joost P. den Bieman , Marcel R.A. van Gent , Juan P. Aguilar-López
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引用次数: 0

Abstract

Sea level rise can compromise the safety of coastal flood defences, as wave overtopping events are becoming more frequent and severe. This increasing threat emphasizes the need for accurate assessment of wave overtopping hydrodynamics over dikes, which is essential for evaluating flood safety. The currently available methods do not combine computational efficiency, detailed results and general applicability, which limits their use in modelling wave overtopping and the resulting dike erosion. To address these limitations, this study introduces the Wave Overtopping Surrogate Model (WOSM), a novel method for rapidly generating high-quality two-dimensional simulations of wave overtopping over the dike crest and landward slope. The foundation of the WOSM is the Vision Transformer Image to Image (ViTI2I), a new deep learning model that combines an adapted Vision Transformer with a convolutional decoder for next-frame prediction. Trained on CFD wave overtopping simulations, the WOSM accurately reproduces the overtopping hydrodynamics such as flow velocities, water depths, overtopping duration and vertical velocity profiles, including both spatial and temporal variations. The scope of the training data limits the applicability of the WOSM and its ability to consistently capture complex phenomena such as flow separation and reattachment, both of which could be improved by enriching the dataset. Its low computational demand makes it suitable for exploring additional applications, such as probabilistic design or simulating wave overtopping with evolving dike profiles for erosion assessment. Additionally, this study serves as a proof of concept that the WOSM framework could benefit other fields encountering comparable modelling constraints.
采用自适应深度学习视觉转换器的堤波过顶流体动力学代理建模
海平面上升可能危及沿海防洪堤的安全,因为海浪超过水面的事件变得越来越频繁和严重。这种日益增加的威胁强调了对堤防上浪漫过水动力进行准确评估的必要性,这对于评估洪水安全至关重要。目前可用的方法没有将计算效率、详细结果和一般适用性结合起来,这限制了它们在模拟波浪漫顶和由此产生的堤防侵蚀方面的应用。为了解决这些限制,本研究引入了波浪漫溢代理模型(WOSM),这是一种快速生成高质量波浪漫溢堤顶和向陆坡二维模拟的新方法。WOSM的基础是视觉转换图像到图像(ViTI2I),这是一种新的深度学习模型,将自适应视觉转换器与卷积解码器相结合,用于下一帧预测。经过CFD波浪过顶模拟的训练,WOSM可以精确地再现过顶流体动力学,如流速、水深、过顶持续时间和垂直速度分布,包括空间和时间变化。训练数据的范围限制了WOSM的适用性及其持续捕获复杂现象(如流动分离和再附着)的能力,这两者都可以通过丰富数据集来改进。它的低计算需求使其适合于探索其他应用,例如概率设计或模拟波浪漫过与不断变化的堤防剖面进行侵蚀评估。此外,本研究证明了WOSM框架可以使遇到类似建模约束的其他领域受益。
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来源期刊
Coastal Engineering
Coastal Engineering 工程技术-工程:大洋
CiteScore
9.20
自引率
13.60%
发文量
0
审稿时长
3.5 months
期刊介绍: Coastal Engineering is an international medium for coastal engineers and scientists. Combining practical applications with modern technological and scientific approaches, such as mathematical and numerical modelling, laboratory and field observations and experiments, it publishes fundamental studies as well as case studies on the following aspects of coastal, harbour and offshore engineering: waves, currents and sediment transport; coastal, estuarine and offshore morphology; technical and functional design of coastal and harbour structures; morphological and environmental impact of coastal, harbour and offshore structures.
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