Gappy data reconstruction using SPOD
ORAL
Abstract
Spatio-temporal flow data, for example those obtained by time-resolved particle image velocimetry (PIV), often contain gaps or other types of undesired artifacts. To reconstruct flow data in the compromised regions, we propose a method based on spectral proper orthogonal decomposition (SPOD). The mathematical properties of SPOD make it well-suited for this task. In particular, the proposed approach leverages the temporal correlation with preceding and succeeding snapshots in time, as well as the correlation with the surrounding data in space. The algorithm involves the computation of the SPOD from the data that is not affected by any given gap and an inversion of the SPOD to reconstruct the data in the affected regions. We test the method for two data sets: the canonical example of numerical data of laminar flow past a cylinder and the more challenging (and relevant) case of PIV data of turbulent cavity flow. Three levels of gappiness, 1%, 5%, and 20% are considered.
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Presenters
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Oliver T. T Schmidt
Mechanical and Aerospace Engineering, University of California, San Diego, University of California, San Diego, University of California San Diego, UC San Diego
Authors
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Oliver T. T Schmidt
Mechanical and Aerospace Engineering, University of California, San Diego, University of California, San Diego, University of California San Diego, UC San Diego
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Akhil Nekkanti
University of California San Diego, University of California, San Diego