Dynamic mode decomposition for non-uniformly sampled data
Dynamic mode decomposition for non-uniformly sampled data
复制标题
非均匀采样数据的动态模式分解
DOI：
10.1007/s00348-016-2165-1
复制
发表时间：
2016
影响因子：
2.4
通讯作者：
L. Cordier
中科院分区：
文献类型：
--
作者：
R. Leroux;L. Cordier
提出了一种从非均匀采样数据中估计动态模式分解(DMD)模式的新方法。所提出的策略分三个步骤处理时间分辨的流快照序列。首先，通过适当的正交分解对非缺失数据进行降阶建模，得到状态空间的低阶描述。其次，通过将线性动态状态空间模型与期望最大化算法相结合，以最大似然法确定缺失数据。最后，用一种称为正交化偏最小二乘回归的多元线性回归方法对重建数据进行DMD模式估计。对NACA0012翼型在迎角为20°、雷诺数为103的情况下的流动进行了评估。用时间分辨粒子图像测速仪获得了流动测量数据，并在不同的缺失率下进行了人工亚采样。结果表明，对于50%和75%的缺失数据，该方法可以再现主要的DMD模式和主要的流场结构。
We propose an original approach to estimate dynamic mode decomposition (DMD) modes from non-uniformly sampled data. The proposed strategy processes a time-resolved sequence of flow snapshots in three steps. First, a reduced-order modeling of the non-missing data is made by proper orthogonal decomposition to obtain a low-order description of the state space. Second, the missing data are determined with maximum likelihood by coupling a linear dynamical state-space model with the Expectation-Maximization algorithm. Third, the DMD modes are finally estimated on the reconstructed data with a multiple linear regression method called orthonormalized partial least squares regression. This methodology is assessed for the flow past a NACA0012 airfoil at 20° of angle of attack and a Reynolds number of 103. The flow measurements are obtained with time-resolved particle image velocimetry and artificially subsampled at different ratios of missing data. The results show that the proposed method can reproduce the dominant DMD modes and the main structures of the flow fields for 50 and 75 % of missing data.
影响因子：
3.7
作者：
A. Wynn;D. Pearson;B. Ganapathisubramani;P. Goulart
通讯作者：
A. Wynn;D. Pearson;B. Ganapathisubramani;P. Goulart