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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 p
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This page is a summary of: Assessment of data-driven Reynolds stress tensor perturbations for uncertainty quantification of RANS turbulence models, June 2022, American Institute of Aeronautics and Astronautics (AIAA),
DOI: 10.2514/6.2022-3767.
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