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Dynamic Mode Decomposition on pressure flow field analysis: Flow field reconstruction, accuracy, and practical significance
Journal of Wind Engineering and Industrial Aerodynamics ( IF 4.8 ) Pub Date : 2020-10-01 , DOI: 10.1016/j.jweia.2020.104278
Cruz Y. Li , Tim K.T. Tse , Gang Hu

Abstract This study applies the Dynamic Mode Decomposition (DMD) technique to a prototypical wind engineering problem of flow past a square prism at a Reynolds number of 22,000 to investigate the DMD’s accuracy and practical values in pressure flow field analysis. In reconstructing the original pressure field obtained by Large-Eddy-Simulations (LES), a full-order DMD model achieves a stellar accuracy within 0.1% mean error. The model also provides a computationally simplistic alternative in lieu of the strenuous POD reconstruction. Spatiotemporal analysis reveals two types of reconstruction errors: Spatial error arising from DMD’s inherent linear approximations on nonlinear phenomena, and temporal error arising from DMD’s assumption on temporal behaviors of flow mechanisms. Additionally, reduced-order DMD models achieve an acceptable accuracy within 0.9% mean error, and aptly capture macroscopic flow features, while reducing the size of the essential data needed for flow field construction by as much as 25 times. In wind engineering applications, where flow field datasets are extensive but microscopic flow features are unnecessary, the usage of reduced-order DMD models greatly alleviates the intense computational burden on flow field post-analysis. With an adequate balance between model size and reconstruction accuracy, DMD proves an accurate and practically beneficial technique for wind engineering applications.

中文翻译:

压力流场分析的动态模式分解:流场重构、精度和实际意义

摘要 本研究将动态模态分解 (DMD) 技术应用于流经方棱柱、雷诺数为 22,000 的典型风工程问题,以研究 DMD 在压力流场分析中的准确性和实用价值。在重建通过大涡模拟 (LES) 获得的原始压力场时,全阶 DMD 模型实现了平均误差在 0.1% 以内的恒星精度。该模型还提供了一种计算上简单的替代方案,以代替费力的 POD 重建。时空分析揭示了两种类型的重建误差:由 DMD 对非线性现象的固有线性近似引起的空间误差,以及由 DMD 对流动机制的时间行为的假设引起的时间误差。此外,降阶 DMD 模型在 0.9% 平均误差内达到可接受的精度,并恰当地捕捉宏观流动特征,同时将流场构建所需的基本数据的大小减少多达 25 倍。在风工程应用中,流场数据集很广泛,但不需要微观流动特征,使用降阶 DMD 模型大大减轻了流场后分析的繁重计算负担。通过在模型大小和重建精度之间取得足够的平衡,DMD 证明了一种准确且实用的风工程应用技术。在流场数据集广泛但不需要微观流动特征的情况下,降阶 DMD 模型的使用大大减轻了流场后分析的繁重计算负担。通过在模型大小和重建精度之间取得足够的平衡,DMD 证明了一种准确且实用的风工程应用技术。在流场数据集广泛但不需要微观流动特征的情况下,降阶 DMD 模型的使用大大减轻了流场后分析的繁重计算负担。通过在模型大小和重建精度之间取得足够的平衡,DMD 证明了一种准确且实用的风工程应用技术。
更新日期:2020-10-01
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