Symbolic regression of algebraic stress-strain relation for RANS turbulence closure

Conference paper (2020)

Authors

M. Schmelzer Aerodynamics - Aerospace Engineering

R.P. Dwight Aerodynamics - Aerospace Engineering

Research Group

Aerodynamics (Aerospace Engineering) (TU Delft)

Machine Learning RANS Turbulence Modelling Deterministic Symbolic Regression Explicit Algebraic Reynolds-stress Models Regularised Least-Square Regression

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http://resolver.tudelft.nl/uuid:33d678ea-c92f-4824-bbcf-d8bf578105d7

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Published Date

01-01-2020

Language

English

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Faculty

Aerospace Engineering

Department

Flow Physics and Technology

Research Group

Aerodynamics

Abstract

In this work recent advancements are presented in utilising deterministic symbolic regression to infer algebraic models for turbulent stress-strain relation with sparsity-promoting regression techniques. The goal is to build a functional expression from a set of candidate functions in order to represent the target data most accurately. Targets are the coefficients of a polynomial tensor basis, which are identified from high-fidelity data using regularised least-square regression. The method successfully identified a correction term for the benchmark test case of flow over periodic hills in 2D at Reh = 10595.

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