Parameterised non-intrusive reduced-order model for general unsteady flow problems using artificial neural networks

Sugar-Gabor, O ORCID: https://orcid.org/0000-0001-6366-9623 2020, 'Parameterised non-intrusive reduced-order model for general unsteady flow problems using artificial neural networks' , International Journal for Numerical Methods in Fluids .

[img] PDF - Accepted Version
Restricted to Repository staff only until 23 October 2021.

Download (1MB) | Request a copy
Access Information: This is the peer reviewed version of the following article: Şugar‐Gabor, O. (2020), Parameterised non‐intrusive reduced‐order model for general unsteady flow problems using artificial neural networks. Int J Numer Meth Fluids., which has been published in final form at https://doi.org/10.1002/fld.4930. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions.

Abstract

A non-intrusive reduced-order model for nonlinear parametric flow problems is developed. It is based on extracting a reduced-order basis from high-order snapshots via proper orthogonal decomposition and using multi-layered feedforward artificial neural networks to approximate the reduced-order coefficients. The model is a generic and efficient approach for the reduction of time-dependent parametric systems, including those described by partial differential equations. Since it is non-intrusive, it is independent of the high-order computational method and can be used together with black-box solvers. Numerical studies are presented for steadystate isentropic nozzle flow with geometric parameterisation and unsteady parameterised viscous Burgers equation. An adaptive sampling strategy is proposed to increase the quality of the neural network approximation while minimising the required number of parameter samples and, as a direct consequence, the number of high-order snapshots and the size of the network training set. Results confirm the accuracy of the non-intrusive approach as well as the speed-up achieved compared with intrusive hyper reduced-order approaches.

Item Type: Article
Schools: Schools > School of Computing, Science and Engineering > Salford Innovation Research Centre
Journal or Publication Title: International Journal for Numerical Methods in Fluids
Publisher: Wiley
ISSN: 0271-2091
Related URLs:
Depositing User: O Sugar-Gabor
Date Deposited: 23 Oct 2020 07:27
Last Modified: 03 Nov 2020 13:30
URI: http://usir.salford.ac.uk/id/eprint/58622

Actions (login required)

Edit record (repository staff only) Edit record (repository staff only)

Downloads

Downloads per month over past year