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首页> 外文期刊>International Journal of Heat and Fluid Flow >Data-driven feature identification and sparse representation of turbulent flows
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Data-driven feature identification and sparse representation of turbulent flows

机译：湍流流动的数据驱动特征识别和稀疏表示

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摘要

Identifying coherent structures in fluid flows is of great importance for reduced order modelling and flow control. However, extracting such structures from experimental or numerical data obtained from a turbulent flow can be challenging. A number of modal decomposition algorithms have been proposed in recent years which decompose time-resolved snapshots of data into spatial modes, each associated with a single frequency and growth-rate. Most prominently among them is dynamic mode decomposition (DMD). However, DMD-like algorithms create an arbitrary number of modes. It is common practice to then choose a smaller subset of these modes, for the purpose of model reduction and analysis, based on some measure of significance. In this work, we present a method of post-processing DMD modes for extracting a small number of dynamically relevant modes. We achieve this through an iterative approach based on the graph-theoretic notion of maximal cliques to identify clusters of modes and representing each cluster with a single representative mode.
机译：识别流体流中的相干结构对于减少阶阶建筑和流量控制具有重要意义。然而，从从湍流获得的实验或数值数据中提取这种结构可能是具有挑战性的。近年来已经提出了许多模态分解算法，该算法将数据分解为空间模式的时间被分解为空间模式，每个与单个频率和生长速率相关联。其中最突出的是动态模式分解（DMD）。但是，DMD样算法创建了任意数量的模式。对于模型减少和分析的目的，常见的做法是选择这些模式的较小的这些模式的子集，基于一些意义的衡量标准。在这项工作中，我们介绍了一种用于提取少量动态相关模式的DMD模式的方法。我们通过基于最大派系的图形理论概念来识别模式的迭代方法来实现这一目标，以识别模式集群并用单个代表模式表示每个群集。

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