Reduced-order modelling of parameterised incompressible and compressible unsteady flow problems using deep neural networks

Sugar-Gabor, O

Abstract

A non-intrusive reduced-order model for nonlinear parametric flow
problems is developed. It is based on extracting a reduced-order basis from
full-order snapshots via proper orthogonal decomposition and using both deep
and shallow neural network architectures to learn the reduced-order
coefficients variation in time and over the parameter space. Even though the
focus of the paper lies in developing a reduced-order methodology for
approximating fluid flow problems, the methodology is generic and can be
used for the order reduction of arbitrary time-dependent parametric systems.
Since it is non-intrusive, it is independent of the full-order computational
method and can be used together with black-box commercial solvers. An
adaptive sampling strategy is proposed to increase the quality of the neural
network predictions while minimising the required number of parameter
samples. Numerical studies are presented for two canonical test cases, namely
unsteady incompressible laminar flow around a circular cylinder and
transonic inviscid flow around a pitching NACA 0012 aerofoil. Results show
that the proposed methodology can be used as a predictive tool for unsteady
parameter-dependent flow problems.

Citation

Sugar-Gabor, O. (2021). Reduced-order modelling of parameterised incompressible and compressible unsteady flow problems using deep neural networks. International Journal of Computer Applications in Technology, 66(1), 36-50. https://doi.org/10.1504/IJCAT.2021.119603

• Journal Article Type: Article
• Acceptance Date: Nov 11, 2020
• Online Publication Date: Nov 29, 2021
• Publication Date: Dec 1, 2021
• Deposit Date: Dec 7, 2020
• Publicly Available Date: Nov 29, 2022
• Journal: International Journal of Computer Applications in Technology (IJCAT)
• Print ISSN: 0952-8091
• Publisher: Inderscience
• Volume: 66
• Issue: 1
• Pages: 36-50
• DOI: https://doi.org/10.1504/IJCAT.2021.119603
• Publisher URL: https://doi.org/10.1504/IJCAT.2021.119603
• Related Public URLs: http://www.inderscience.com/jhome.php?jcode=IJCAT