Assessment of data-driven Reynolds stress tensor perturbations for uncertainty quantification of RANS turbulence models
Assessment of data-driven Reynolds stress tensor perturbations for uncertainty quantification of RANS turbulence models
- New search for: Matha, Marcel
- Further information on Matha, Marcel:
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https://orcid.org/0000-0001-8101-7303
- New search for: Kucharczyk, Karsten
- New search for: Morsbach, Christian
- Further information on Morsbach, Christian:
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https://orcid.org/0000-0002-6254-6979
- New search for: Matha, Marcel
- Further information on Matha, Marcel:
-
https://orcid.org/0000-0001-8101-7303
- New search for: Kucharczyk, Karsten
- New search for: Morsbach, Christian
- Further information on Morsbach, Christian:
-
https://orcid.org/0000-0002-6254-6979
In order to achieve a more simulation-based design and certification process of jet engines in the aviation industry, the uncertainty bounds for computational fluid dynamics have to be known. This work shows the application of machine learning to support the quantification of epistemic uncertainties of turbulence models. The underlying method in order to estimate the uncertainty bounds is based on eigenspace perturbations of the Reynolds stress tensor in combination with random forests.
Document information
Assessment of data-driven Reynolds stress tensor perturbations for uncertainty quantification of RANS turbulence models
2022 ; Chicago, Illinois, USA
2022-06-21
Conference paper
Electronic Resource
English
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| German Aerospace Center (DLR) | 2022