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

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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.

Original languageEnglish
Title of host publicationProceedings of the 6th European Conference on Computational Mechanics
Subtitle of host publicationSolids, Structures and Coupled Problems, ECCM 2018 and 7th European Conference on Computational Fluid Dynamics, ECFD 2018
EditorsRoger Owen, Rene de Borst, Jason Reese, Chris Pearce
PublisherInternational Centre for Numerical Methods in Engineering, CIMNE
Pages1789-1795
Number of pages7
ISBN (Electronic)9788494731167
Publication statusPublished - 1 Jan 2020
Event6th ECCOMAS European Conference on Computational Mechanics: Solids, Structures and Coupled Problems, ECCM 2018 and 7th ECCOMAS European Conference on Computational Fluid Dynamics, ECFD 2018 - Glasgow, United Kingdom
Duration: 11 Jun 201815 Jun 2018
Conference number: 6

Conference

Conference6th ECCOMAS European Conference on Computational Mechanics: Solids, Structures and Coupled Problems, ECCM 2018 and 7th ECCOMAS European Conference on Computational Fluid Dynamics, ECFD 2018
Abbreviated titleECFD 2018
CountryUnited Kingdom
CityGlasgow
Period11/06/1815/06/18

Keywords

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

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