ABSTRACT

Foredunes play an important role in protecting coastal communities and their assets. The use of Computational Fluid Dynamics (CFD) to simulate wind flow over foredunes has great potential because it can enable three-dimensional visualisations of the flow field, critical to predicting sediment pathways. Generalised conceptual models of foredune evolution and maintenance can then be built and revised over time as more evidence from the field becomes available. Obtaining field data is, however, time consuming, costly, and weather dependent. CFD is ideally suited to explore what happens when wind transitions across the beach and encounters the stoss face of the foredune. A simple dune shape is used in CFD simulations to tease out the influence of various dune parameters under varying wind conditions. However, it is computationally expensive to run CFD simulations for all combinations of parameters. Representative data were used to train machine learning algorithms, and the results were compared to predicted CFD simulations. The machine learning algorithms were able to identify the cases when recirculation vortices were present and to some extent their relative scales and locations, allowing the exploration and identification of key parameters related to wind flow and dune geomorphology that are associated with turbulent flow structures.

摘要

禁运在保护沿海社区及其财产方面发挥着重要作用。使用计算流体动力学（CFD）来模拟过早的风具有巨大的潜力，因为它可以实现流场的三维可视化，这对于预测沉积物路径至关重要。然后，随着越来越多的现场证据的出现，可以建立和修改前言演化和维护的通用概念模型。然而，获取现场数据是耗时的，昂贵的且取决于天气的。CFD非常适合探索当风在整个海滩上过渡并遇到前缘的虚张声势时所发生的情况。在CFD仿真中，使用简单的沙丘形状来梳理在变化的风况下各种沙丘参数的影响。然而，对于所有参数组合运行CFD仿真在计算上是昂贵的。代表性数据用于训练机器学习算法，并将结果与​​预测的CFD模拟进行比较。机器学习算法能够识别出存在回旋涡流的情况，并在一定程度上确定了其相对尺度和位置，从而能够探索和识别与湍流结构相关的与风流和沙丘地貌有关的关键参数。