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Towards multifidelity models with calibration for turbulent flows
KTH, School of Engineering Sciences (SCI), Engineering Mechanics, Fluid Mechanics and Engineering Acoustics. KTH, School of Engineering Sciences (SCI), Centres, Linné Flow Center, FLOW. KTH, Centres, SeRC - Swedish e-Science Research Centre.ORCID iD: 0000-0002-9610-9910
KTH, School of Engineering Sciences (SCI), Engineering Mechanics, Fluid Mechanics and Engineering Acoustics. KTH, School of Engineering Sciences (SCI), Centres, Linné Flow Center, FLOW. KTH, Centres, SeRC - Swedish e-Science Research Centre.ORCID iD: 0000-0001-6570-5499
KTH, School of Engineering Sciences (SCI), Engineering Mechanics, Fluid Mechanics and Engineering Acoustics. KTH, School of Engineering Sciences (SCI), Centres, Linné Flow Center, FLOW. KTH, Centres, SeRC - Swedish e-Science Research Centre.ORCID iD: 0000-0001-9627-5903
2021 (English)In: 14th World Congress of Computational Mechanics and ECCOMAS Congress, WCCM-ECCOMAS 2020, Scipedia, S.L. , 2021, Vol. 800, p. 1-12Conference paper, Published paper (Refereed)
Abstract [en]

High-fidelity scale-resolving simulations of turbulent flows can be prohibitively expensive, especially at high Reynolds numbers. Therefore, multifidelity models (MFM) can be highly relevant for constructing predictive models for flow quantities of interest (QoIs), uncertainty quantification, and optimization. For numerical simulation of turbulence, there is a hierarchy of methodologies. On the other hand, there are calibration parameters in each of these methods which control the predictive accuracy of the resulting outputs. Compatible with these, the hierarchical MFM strategy which allows for simultaneous calibration of the model parameters as developed by Goh et al. [7] within a Bayesian framework is considered in the present study. The multifidelity model is applied to two cases related to wall-bounded turbulent flows. The examples are the prediction of friction at different Reynolds numbers in turbulent channel flow, and the prediction of aerodynamic coefficients for a range of angles of attack of a standard airfoil. In both cases, based on a few high-fidelity datasets, the MFM leads to accurate predictions of the QoIs as well as an estimation of uncertainty in the predictions.

Place, publisher, year, edition, pages
Scipedia, S.L. , 2021. Vol. 800, p. 1-12
Series
World Congress in Computational Mechanics and ECCOMAS Congress, ISSN 2696-6999 ; 800
Keywords [en]
Calibration, Hierarchical Multifidelity Models, Turbulent Flows, Uncertainty Quantification
National Category
Other Physics Topics
Identifiers
URN: urn:nbn:se:kth:diva-313239DOI: 10.23967/wccm-eccomas.2020.348Scopus ID: 2-s2.0-85118831996OAI: oai:DiVA.org:kth-313239DiVA, id: diva2:1662683
Conference
14th World Congress of Computational Mechanics and ECCOMAS Congress, WCCM-ECCOMAS 2020, 11 January 2021 through 15 January 2021, Virtual
Note

QC 20220601

Available from: 2022-06-01 Created: 2022-06-01 Last updated: 2022-06-25Bibliographically approved

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Rezaeiravesh, SalehVinuesa, RicardoSchlatter, Philipp

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Fluid Mechanics and Engineering AcousticsLinné Flow Center, FLOWSeRC - Swedish e-Science Research Centre
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