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Data-driven Bayesian inference of turbulence model closure coefficients incorporating epistemic uncertainty

Maruyama, Daigo und Bekemeyer, Philipp und Görtz, Stefan und Coggon, Simon und Sharma S, Sanjiv (2021) Data-driven Bayesian inference of turbulence model closure coefficients incorporating epistemic uncertainty. Acta Mechanica Sinica, 37, Seiten 1812-1838. Springer. doi: 10.1007/s10409-021-01152-5. ISSN 0567-7718.

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Offizielle URL: https://link.springer.com/article/10.1007/s10409-021-01152-5

Kurzfassung

We introduce a framework for statistical inference of the closure coefficients using machine learning methods. The objective of this framework is to quantify the epistemic uncertainty associated with the closure model by using experimental data via Bayesian statistics. The framework is tailored towards cases for which a limited amount of experimental data is available. It consists of two components. First, by treating all latent variables (non-observed variables) in the model as stochastic variables, all sources of uncertainty of the probabilistic closure model are quantified by a fully Bayesian approach. The probabilistic model is defined to consist of the closure coefficients as parameters and other parameters incorporating noise. Then, the uncertainty associated with the closure coefficients is extracted from the overall uncertainty by considering the noise being zero. The overall uncertainty is rigorously evaluated by using Markov-Chain Monte Carlo sampling assisted by surrogate models. We apply the framework to the Spalart-Allmars one-equation turbulence model. Two test cases are considered, including an industrially relevant full aircraft model at transonic flow conditions, the Airbus XRF1. Eventually, we demonstrate that epistemic uncertainties in the closure coefficients result into uncertainties in flow quantities of interest which are prominent around, and downstream, of the shock occurring over the XRF1 wing. This data-driven approach could help to enhance the predictive capabilities of computational fluid dynamics (CFD) in terms of reliable turbulence modeling at extremes of the flight envelope if measured data is available, which is important in the context of robust design and towards virtual aircraft certification. The plentiful amount of information about the uncertainties could also assist when it comes to estimating the influence of the measured data on the inferred model coefficients. Finally, the developed framework is flexible and can be applied to different test cases and to various turbulence models.

elib-URL des Eintrags:https://elib.dlr.de/186003/
Dokumentart:Zeitschriftenbeitrag
Titel:Data-driven Bayesian inference of turbulence model closure coefficients incorporating epistemic uncertainty
Autoren:
AutorenInstitution oder E-Mail-AdresseAutoren-ORCID-iDORCID Put Code
Maruyama, DaigoDaigo.Maruyama (at) dlr.deNICHT SPEZIFIZIERTNICHT SPEZIFIZIERT
Bekemeyer, PhilippPhilipp.Bekemeyer (at) dlr.deNICHT SPEZIFIZIERTNICHT SPEZIFIZIERT
Görtz, StefanStefan.Goertz (at) dlr.deNICHT SPEZIFIZIERTNICHT SPEZIFIZIERT
Coggon, SimonSimon.Coggon (at) airbus.comNICHT SPEZIFIZIERTNICHT SPEZIFIZIERT
Sharma S, SanjivNICHT SPEZIFIZIERTNICHT SPEZIFIZIERTNICHT SPEZIFIZIERT
Datum:Dezember 2021
Erschienen in:Acta Mechanica Sinica
Referierte Publikation:Ja
Open Access:Ja
Gold Open Access:Nein
In SCOPUS:Ja
In ISI Web of Science:Ja
Band:37
DOI:10.1007/s10409-021-01152-5
Seitenbereich:Seiten 1812-1838
Verlag:Springer
ISSN:0567-7718
Status:veröffentlicht
Stichwörter:Turbulence modeling, Uncertainty quantification, Parameter calibration, Bayesian statistics, Surrogate-assisted methods, Spalart-Allmaras one-equation turbulence model, Large-scale industrial aircraft use-case
HGF - Forschungsbereich:Luftfahrt, Raumfahrt und Verkehr
HGF - Programm:Luftfahrt
HGF - Programmthema:Effizientes Luftfahrzeug
DLR - Schwerpunkt:Luftfahrt
DLR - Forschungsgebiet:L EV - Effizientes Luftfahrzeug
DLR - Teilgebiet (Projekt, Vorhaben):L - Digitale Technologien
Standort: Braunschweig
Institute & Einrichtungen:Institut für Aerodynamik und Strömungstechnik > CASE, BS
Hinterlegt von: Görtz, Stefan
Hinterlegt am:04 Apr 2022 09:00
Letzte Änderung:28 Jun 2023 11:22

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