Please use this identifier to cite or link to this item: http://dx.doi.org/10.14279/depositonce-12373
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Main Title: Efficient Wildland Fire Simulation via Nonlinear Model Order Reduction
Author(s): Black, Felix
Schulze, Philipp
Unger, Benjamin
Type: Article
Language Code: en
Abstract: We propose a new hyper-reduction method for a recently introduced nonlinear model reduction framework based on dynamically transformed basis functions and especially well-suited for transport-dominated systems. Furthermore, we discuss applying this new method to a wildland fire model whose dynamics feature traveling combustion waves and local ignition and is thus challenging for classical model reduction schemes based on linear subspaces. The new hyper-reduction framework allows us to construct parameter-dependent reduced-order models (ROMs) with efficient offline/online decomposition. The numerical experiments demonstrate that the ROMs obtained by the novel method outperform those obtained by a classical approach using the proper orthogonal decomposition and the discrete empirical interpolation method in terms of run time and accuracy.
URI: https://depositonce.tu-berlin.de/handle/11303/13586
http://dx.doi.org/10.14279/depositonce-12373
Issue Date: 11-Aug-2021
Date Available: 14-Sep-2021
DDC Class: 510 Mathematik
Subject(s): nonlinear model order reduction
transport-dominated phenomena
hyper-reduction
wildland fire
Sponsor/Funder: DFG, 200291049, SFB 1029: TurbIn - Signifikante Wirkungsgradsteigerung durch gezielte, interagierende Verbrennungs- und Strömungsinstationaritäten in Gasturbinen
DFG, 239904186, TRR 154: Mathematische Modellierung, Simulation und Optimierung am Beispiel von Gasnetzwerken
DFG, 390740016, EXC 2075: Daten-integrierte Simulationswissenschaft (SimTech)
License: https://creativecommons.org/licenses/by/4.0/
Journal Title: Fluids
Publisher: MDPI
Publisher Place: Basel
Volume: 6
Issue: 8
Article Number: 280
Publisher DOI: 10.3390/fluids6080280
EISSN: 2311-5521
Appears in Collections:FG Numerische Mathematik » Publications

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