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

Conference paper (2018)

Authors

M. Schmelzer Aerodynamics - Aerospace Engineering
R.P. Dwight Aerodynamics - Aerospace Engineering
Paola Cinnella Arts et Métiers ParisTech
Research Group
Aerodynamics (Aerospace Engineering) (TU Delft)
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Published Date

01-01-2018

Language

English

Reuse Rights

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Faculty

Aerospace Engineering

Department

Flow Physics and Technology

Research Group

Aerodynamics

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.

Files

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