Application of the dynamic mode decomposition to experimental data
Application of the dynamic mode decomposition to experimental data
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DOI：
10.1007/s00348-010-0911-3
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发表时间：
2011-04-01
影响因子：
2.4
通讯作者：
Schmid, Peter J.
中科院分区：
文献类型：
--
作者：
Schmid, Peter J.
动态模式分解（DMD）是一种数据分解技术，允许从时间分辨的实验（或数值）数据中提取动态相关的流动特征。它是基于一系列的快照，从测量，随后处理的迭代Krylov技术。近似快照间映射的低维表示的特征值和特征向量然后产生描述包含在数据序列中的动态过程的流信息。这种分解技术同样适用于粒子图像测速数据和基于图像的流动可视化，并证明从一个火焰的数值模拟的基础上的变密度射流和层流轴对称水射流的实验数据的数据。在这两种情况下，检测到的主导频率和相关的空间结构被识别。
The dynamic mode decomposition (DMD) is a data-decomposition technique that allows the extraction of dynamically relevant flow features from time-resolved experimental (or numerical) data. It is based on a sequence of snapshots from measurements that are subsequently processed by an iterative Krylov technique. The eigenvalues and eigenvectors of a low-dimensional representation of an approximate inter-snapshot map then produce flow information that describes the dynamic processes contained in the data sequence. This decomposition technique applies equally to particle-image velocimetry data and image-based flow visualizations and is demonstrated on data from a numerical simulation of a flame based on a variable-density jet and on experimental data from a laminar axisymmetric water jet. In both cases, the dominant frequencies are detected and the associated spatial structures are identified.