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An uncertainty-quantification framework for assessing accuracy, sensitivity, and robustness in computational fluid dynamics
KTH, School of Engineering Sciences (SCI), Centres, Linné Flow Center, FLOW. KTH, School of Engineering Sciences (SCI), Engineering Mechanics, Fluid Mechanics and Engineering Acoustics. KTH, Centres, SeRC - Swedish e-Science Research Centre.ORCID iD: 0000-0002-9610-9910
KTH, School of Engineering Sciences (SCI), Centres, Linné Flow Center, FLOW. KTH, Centres, SeRC - Swedish e-Science Research Centre. KTH, School of Engineering Sciences (SCI), Engineering Mechanics, Fluid Mechanics and Engineering Acoustics.ORCID iD: 0000-0001-9627-5903
2022 (English)In: Journal of Computational Science, ISSN 1877-7503, E-ISSN 1877-7511, Vol. 62, article id 101688Article in journal (Refereed) Published
Abstract [en]

Combining different existing uncertainty quantification (UQ) techniques, a framework is obtained to assess a set of metrics in computational physics problems, in general, and computational fluid dynamics (CFD), in particular. The metrics include accuracy, sensitivity and robustness of the simulator's outputs with respect to uncertain inputs and parameters. These inputs and parameters are divided into two groups: based on the variation of the first group (e.g. numerical/computational parameters such as grid resolution), a computer experiment is designed, the data of which may become uncertain due to the parameters of the second group (e.g. finite time-averaging). To construct a surrogate model based on uncertain data, Gaussian process regression (GPR) with observation-dependent (heteroscedastic) noise is used. To estimate the propagated uncertainties in the simulator's outputs from the first group of parameters, a probabilistic version of the polynomial chaos expansion (PCE) is employed Global sensitivity analysis is performed using probabilistic Sobol indices. To illustrate its capabilities, the framework is applied to the scale-resolving simulations of turbulent channel and lid-driven cavity flows using the open-source CFD solver Nek5000. It is shown that at wall distances where the time-averaging uncertainty is high, the quantities of interest are also more sensitive to numerical/computational parameters. In particular for high-fidelity codes such as Nek5000, a thorough assessment of the results' accuracy and reliability is crucial. The detailed analyses and the resulting conclusions can enhance our insight into the influence of different factors on physics simulations, in particular the simulations of high-Reynolds-number turbulent flows including wall turbulence.

Place, publisher, year, edition, pages
Elsevier BV , 2022. Vol. 62, article id 101688
Keywords [en]
Uncertainty quantification, Computational fluid dynamics, Combined uncertainties, Polynomial chaos expansion, Gaussian process regression
National Category
Fluid Mechanics Computational Mathematics
Identifiers
URN: urn:nbn:se:kth:diva-314829DOI: 10.1016/j.jocs.2022.101688ISI: 000802753900003Scopus ID: 2-s2.0-85129923488OAI: oai:DiVA.org:kth-314829DiVA, id: diva2:1676888
Note

QC 20220627

Available from: 2022-06-27 Created: 2022-06-27 Last updated: 2025-02-09Bibliographically approved

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Rezaeiravesh, SalehSchlatter, Philipp

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Linné Flow Center, FLOWFluid Mechanics and Engineering AcousticsSeRC - Swedish e-Science Research Centre
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