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

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

Schmelzer, M. (TU Delft Aerodynamics)
Dwight, R.P. (TU Delft Aerodynamics)
Cinnella, Paola (151 Boulevard de l'Hospital)

Owen, Roger (editor)
de Borst, Rene (editor)
Reese, Jason (editor)
Pearce, Chris (editor)

2020-01-01

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 Re_h = 10595.

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

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

International Centre for Numerical Methods in Engineering, CIMNE

9788494731167

Proceedings of the 6th European Conference on Computational Mechanics: Solids, Structures and Coupled Problems, ECCM 2018 and 7th European Conference on Computational Fluid Dynamics, ECFD 2018

6th ECCOMAS European Conference on Computational Mechanics: Solids, Structures and Coupled Problems, ECCM 2018 and 7th ECCOMAS European Conference on Computational Fluid Dynamics, ECFD 2018, 2018-06-11 → 2018-06-15, Glasgow, United Kingdom

Proceedings of the 6th European Conference on Computational Mechanics: Solids, Structures and Coupled Problems, ECCM 2018 and 7th European Conference on Computational Fluid Dynamics, ECFD 2018

Institutional Repository

conference paper

© 2020 M. Schmelzer, R.P. Dwight, Paola Cinnella