期刊
PHYSICS OF FLUIDS
卷 26, 期 11, 页码 -出版社
AMER INST PHYSICS
DOI: 10.1063/1.4901016
关键词
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资金
- NSF [DMS-1204783]
- AFOSR
- Direct For Mathematical & Physical Scien
- Division Of Mathematical Sciences [1204783] Funding Source: National Science Foundation
We formulate a low-storage method for performing dynamic mode decomposition that can be updated inexpensively as new data become available; this formulation allows dynamical information to be extracted from large datasets and data streams. We present two algorithms: the first is mathematically equivalent to a standard batch-processed formulation; the second introduces a compression step that maintains computational efficiency, while enhancing the ability to isolate pertinent dynamical information from noisy measurements. Both algorithms reliably capture dominant fluid dynamic behaviors, as demonstrated on cylinder wake data collected from both direct numerical simulations and particle image velocimetry experiments. (C) 2014 AIP Publishing LLC.
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