Data-driven deterministic symbolic regression of nonlinear stress-strain relation for RANS turbulence modelling

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Abstract

This work presents developments towards a deterministic symbolic regression method to derive algebraic Reynolds-stress models for the Reynolds-Averaged Navier-Stokes (RANS) equations. The models are written as tensor polynomials, for which optimal coefficients are found using Bayesian inversion. These coefficient fields are the targets for the symbolic regression. A method is presented based on a regularisation strategy in order to promote sparsity of the inferred models and is applied to high-fidelity data. By being data-driven the method reduces the assumptions commonly made in the process of model development in order to increase the predictive fidelity of algebraic models.

Original languageEnglish
Title of host publication2018 Fluid Dynamics Conference
PublisherAmerican Institute of Aeronautics and Astronautics Inc. (AIAA)
Number of pages13
ISBN (Electronic)9781624105531
DOIs
Publication statusPublished - 1 Jan 2018
Event48th AIAA Fluid Dynamics Conference, 2018 - Atlanta, United States
Duration: 25 Jun 201829 Jun 2018

Conference

Conference48th AIAA Fluid Dynamics Conference, 2018
CountryUnited States
CityAtlanta
Period25/06/1829/06/18

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  • Cite this

    Schmelzer, M., Dwight, R., & Cinnella, P. (2018). Data-driven deterministic symbolic regression of nonlinear stress-strain relation for RANS turbulence modelling. In 2018 Fluid Dynamics Conference [AIAA 2018-2900] American Institute of Aeronautics and Astronautics Inc. (AIAA). https://doi.org/10.2514/6.2018-2900