Dynamic mode decomposition: a tool to extract structures hidden in massive datasets

Grenga, T. and Mueller, M. E. (2020) Dynamic mode decomposition: a tool to extract structures hidden in massive datasets. In, Data Analysis for Direct Numerical Simulations of Turbulent Combustion: From Equation-Based Analysis to Machine Learning. Springer, pp. 157-176. (doi:10.1007/978-3-030-44718-2_8).

Record type: Book Section

Abstract

Dynamic Mode Decomposition (DMD) is able to decompose flow field data into coherent modes and determine their oscillatory frequencies and growth/decay rates, allowing for the investigation of unsteady and dynamic phenomena unlike conventional statistical analyses. The decomposition can be applied for the analysis of data having a broad range of temporal and spatial scales since it identifies structures that characterize the physical phenomena independently from their energy content. In this work, a DMD algorithm specifically created for the analysis of massive databases is used to analyze three-dimensional Direct Numerical Simulation of spatially evolving turbulent planar premixed hydrogen/air jet flames at varying Karlovitz number. The focus of this investigation is the identification of the most important modes and frequencies for the physical phenomena, specifically heat release and turbulence, governing the flow field evolution.

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