A hybrid approach combining DNS and RANS simulations to quantify uncertainties in turbulence modelling
File(s)
Accepted version
Author(s)
Voet, Laurens
Ahlfeld, Richard
Gaymann, Audrey
Laizet, Sylvain
Montomoli, Francesco
Type
Journal Article
Abstract
Uncertainty quantification (UQ) has recently become an important part of the design process of countless engineering applications. However, up to now in computational fluid dynamics (CFD) the errors introduced by the turbulent viscosity models in Reynolds-Averaged Navier Stokes (RANS) models have often been neglected in UQ studies. Although Direct Numerical Simulations (DNS) are physically correct, obtaining a large enough set of DNS data for UQ studies is currently computationally intractable. UQ based only on RANS simulations or on DNS thus leads to physical and statistical inaccuracies in the output probability distribution functions (PDF). Therefore, three hybrid methods combining both RANS simulations and DNS to perform non-intrusive UQ are suggested in this work. Low-fidelity RANS simulations and high-fidelity DNS are combined to give an approximation of an output PDF using the advantages of both data sets: the physical accuracy via the DNS and the statistical accuracy via the RANS simulations. The hybrid methods are applied to the flow over 2D periodically arranged hills. It is shown that the Gaussian CoKriging (GCK) method is the best hybrid method and that a non-intrusive hybrid UQ approach combining both DNS and RANS simulations is possible, with both physically more accurate and statistically better PDF.
Date Issued
2021-01
Date Acceptance
2020-07-30
Citation
Applied Mathematical Modelling: simulation and computation for engineering and environmental systems, 2021, 89 (Part 1), pp.885-906
ISSN
0307-904X
Publisher
Elsevier
Start Page
885
End Page
906
Journal / Book Title
Applied Mathematical Modelling: simulation and computation for engineering and environmental systems
Volume
89
Issue
Part 1
Copyright Statement
2020 © 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
Engineering & Physical Science Research Council (EPSRC)
Identifier
https://www.sciencedirect.com/science/article/pii/S0307904X20304212?via%3Dihub
Grant Number
EP/R023926/1
Subjects
Mechanical Engineering & Transports
0102 Applied Mathematics
0103 Numerical and Computational Mathematics
0801 Artificial Intelligence and Image Processing
Publication Status
Published
Date Publish Online
2020-08-07