Feature Extraction from Turbulent Channel Flow Databases via Composite DMD Analysis
Journal of Physics: Conference Series(2020)
摘要
Abstract In this contribution we consider the Dynamic Mode Decomposition (DMD) framework as a purely data-driven tool to investigate a Reτ ≍ 950 turbulent channel database. Specifically, composite-based DMD analyses are conducted, with hybrid snapshots composed by skin friction and Reynolds stress. A small number of dynamic modes (less than 1% of the number of snapshots) is found to be able to recover accurately the DNS Reynolds stresses near the wall, with a weighted factor as an indicator for the modes selections. As a possibility of analysis large turbulent database, we conclude that composite DMD is an attractive, purely data-driven, feature extraction tool to study turbulent flows.
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关键词
turbulent channel flow databases
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