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A domain decomposition non-intrusive reduced order model for turbulent flows

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Title: A domain decomposition non-intrusive reduced order model for turbulent flows
Authors: Xiao, D
Heaney, CE
Fang, F
Mottet, L
Hu, R
Bistrian, DA
Aristodemou, E
Navon, IM
Pain, CC
Item Type: Journal Article
Abstract: In this paper, a new Domain Decomposition Non-Intrusive Reduced Order Model (DDNIROM) is developed for turbulent flows. The method works by partitioning the computational domain into a number of subdomains in such a way that the summation of weights associated with the finite element nodes within each subdomain is approximately equal, and the communication between subdomains is minimised. With suitably chosen weights, it is expected that there will be approximately equal accuracy associated with each subdomain. This accuracy is maximised by allowing the partitioning to occur through areas of the domain that have relatively little flow activity, which, in this case, is characterised by the pointwise maximum Reynolds stresses. A Gaussian Process Regression (GPR) machine learning method is used to construct a set of local approximation functions (hypersurfaces) for each subdomain. Each local hypersurface represents not only the fluid dynamics over the subdomain it belongs to, but also the interactions of the flow dynamics with the surrounding subdomains. Thus, in this way, the surrounding subdomains may be viewed as providing boundary conditions for the current subdomain. We consider a specific example of turbulent air flow within an urban neighbourhood at a test site in London and demonstrate the effectiveness of the proposed DDNIROM.
Issue Date: 30-Mar-2019
Date of Acceptance: 14-Feb-2019
URI: http://hdl.handle.net/10044/1/66644
DOI: https://doi.org/10.1016/j.compfluid.2019.02.012
ISSN: 0045-7930
Publisher: Elsevier
Start Page: 15
End Page: 27
Journal / Book Title: Computers and Fluids
Volume: 182
Copyright Statement: © 2019 Published by Elsevier Ltd. All rights reserved. This manuscript is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Licence http://creativecommons.org/licenses/by-nc-nd/4.0/.
Sponsor/Funder: Engineering & Physical Science Research Council (E
Funder's Grant Number: RG80519
Keywords: Science & Technology
Computer Science, Interdisciplinary Applications
Computer Science
Non-intrusive reduced order modelling
Domain decomposition
Machine learning
Gaussian process regression
Urban flows
Turbulent flows
Finite element method
Applied Mathematics
0102 Applied Mathematics
0915 Interdisciplinary Engineering
0913 Mechanical Engineering
Publication Status: Published
Online Publication Date: 2019-02-15
Appears in Collections:Faculty of Engineering
Earth Science and Engineering

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