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Published in:
Analysis of Flame Topology and Burning Rates Read chapter Read first chapter

2020 | OriginalPaper | Chapter

8. Dynamic Mode Decomposition: A Tool to Extract Structures Hidden in Massive Datasets

Authors : T. Grenga, M. E. Mueller

Published in: Data Analysis for Direct Numerical Simulations of Turbulent Combustion

Publisher: Springer International Publishing

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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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previous chapter Data-Driven Modal Decomposition Techniques for High-Dimensional Flow Fields
next chapter Physics-Informed Data-Driven Prediction of Turbulent Reacting Flows with Lyapunov Analysis and Sequential Data Assimilation
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Metadata
Title
Dynamic Mode Decomposition: A Tool to Extract Structures Hidden in Massive Datasets
Authors
T. Grenga
M. E. Mueller
Copyright Year
2020
Book
Data Analysis for Direct Numerical Simulations of Turbulent Combustion

Print ISBN: 978-3-030-44717-5

Electronic ISBN: 978-3-030-44718-2

Copyright Year: 2020

DOI
https://doi.org/10.1007/978-3-030-44718-2_8

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