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

Journal article


Xiao, D, Heaney, CE, Fang, F, Mottet, L, Hu, R, Bistrian, DA, Aristodemou, E, Navon, IM and Pain, CC (2019). A domain decomposition non-intrusive reduced order model for turbulent flows. Computers and Fluids. 182, pp. 15-27.
AuthorsXiao, D, Heaney, CE, Fang, F, Mottet, L, Hu, R, Bistrian, DA, Aristodemou, E, Navon, IM and Pain, CC
Abstract

© 2019 Elsevier Ltd 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.

Year2019
JournalComputers and Fluids
Journal citation182, pp. 15-27
ISSN0045-7930
Digital Object Identifier (DOI)doi:10.1016/j.compfluid.2019.02.012
Publication dates
Print30 Mar 2019
Publication process dates
Deposited23 Apr 2019
Accepted14 Feb 2019
Accepted author manuscript
License
CC BY-NC-ND 4.0
Permalink -

https://openresearch.lsbu.ac.uk/item/86718

Accepted author manuscript

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