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

2020 | OriginalPaper | Chapter

7. Data-Driven Modal Decomposition Techniques for High-Dimensional Flow Fields

Authors : Nicholas Arnold-Medabalimi, Cheng Huang, Karthik Duraisamy

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

Publisher: Springer International Publishing

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Abstract

Data-driven decomposition techniques are presented for the analysis and development of reduced-order models of complex flow dynamics. The Proper Orthogonal Decomposition (POD) produces optimal representations of the dynamics in the sense of the energy norm. Alternatively, Dynamic Mode Decomposition (DMD) efficiently extracts coherent dynamics based on eigendecompositions of linearized dynamics. An extension to the latter, the Higher Order Dynamic Mode Decomposition (HODMD) method uses time delays to develop efficient reduced models to represent complex dynamics in a nonintrusive manner. High-fidelity simulation results of a laboratory-scale single-element gas turbine combustor are used to demonstrate and evaluate the capabilities of the aforementioned decomposition techniques.

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previous chapter Higher Order Tensors for DNS Data Analysis and Compression
next chapter Dynamic Mode Decomposition: A Tool to Extract Structures Hidden in Massive Datasets
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Metadata
Title
Data-Driven Modal Decomposition Techniques for High-Dimensional Flow Fields
Authors
Nicholas Arnold-Medabalimi
Cheng Huang
Karthik Duraisamy
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_7

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