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

Martin Schmelzer; Richard P. Dwight; Paola Cinnella

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

Martin Schmelzer^*, Richard P. Dwight, Paola Cinnella

^*Corresponding author for this work

Aerodynamics

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

1 Citation (Scopus)

42 Downloads (Pure)

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 Re_h = 10595.

Original language	English
Title of host publication	Proceedings of the 6th European Conference on Computational Mechanics
Subtitle of host publication	Solids, Structures and Coupled Problems, ECCM 2018 and 7th European Conference on Computational Fluid Dynamics, ECFD 2018
Editors	Roger Owen, Rene de Borst, Jason Reese, Chris Pearce
Publisher	International Centre for Numerical Methods in Engineering, CIMNE
Pages	1789-1795
Number of pages	7
ISBN (Electronic)	9788494731167
Publication status	Published - 1 Jan 2020
Event	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 - Glasgow, United Kingdom Duration: 11 Jun 2018 → 15 Jun 2018 Conference number: 6

Publication series

Name	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

Conference

Conference	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
Abbreviated title	ECFD 2018
Country/Territory	United Kingdom
City	Glasgow
Period	11/06/18 → 15/06/18

Keywords

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

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2018_Paper_ECCM-ECFD_MSchmelzerAccepted author manuscript, 1.34 MB

Cite this

Schmelzer, M., Dwight, R. P., & Cinnella, P. (2020). Symbolic regression of algebraic stress-strain relation for RANS turbulence closure. In R. Owen, R. de Borst, J. Reese, & C. Pearce (Eds.), 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 (pp. 1789-1795). (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). International Centre for Numerical Methods in Engineering, CIMNE.

Schmelzer, Martin ; Dwight, Richard P. ; Cinnella, Paola. / Symbolic regression of algebraic stress-strain relation for RANS turbulence closure. 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. editor / Roger Owen ; Rene de Borst ; Jason Reese ; Chris Pearce. International Centre for Numerical Methods in Engineering, CIMNE, 2020. pp. 1789-1795 (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).

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title = "Symbolic regression of algebraic stress-strain relation for RANS turbulence closure",

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.",

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

author = "Martin Schmelzer and Dwight, {Richard P.} and Paola Cinnella",

year = "2020",

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series = "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",

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pages = "1789--1795",

editor = "Roger Owen and {de Borst}, Rene and Jason Reese and Chris Pearce",

booktitle = "Proceedings of the 6th European Conference on Computational Mechanics",

note = "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, ECFD 2018 ; Conference date: 11-06-2018 Through 15-06-2018",

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Schmelzer, M , Dwight, RP & Cinnella, P 2020, Symbolic regression of algebraic stress-strain relation for RANS turbulence closure. in R Owen, R de Borst, J Reese & C Pearce (eds), 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. 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, International Centre for Numerical Methods in Engineering, CIMNE, pp. 1789-1795, 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, Glasgow, United Kingdom, 11/06/18.

Symbolic regression of algebraic stress-strain relation for RANS turbulence closure. / Schmelzer, Martin ; Dwight, Richard P.; Cinnella, Paola.
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. ed. / Roger Owen; Rene de Borst; Jason Reese; Chris Pearce. International Centre for Numerical Methods in Engineering, CIMNE, 2020. p. 1789-1795 (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).

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

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T1 - Symbolic regression of algebraic stress-strain relation for RANS turbulence closure

AU - Schmelzer, Martin

AU - Dwight, Richard P.

AU - Cinnella, Paola

N1 - Conference code: 6

PY - 2020/1/1

Y1 - 2020/1/1

N2 - 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.

AB - 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.

KW - Deterministic Symbolic Regression

KW - Explicit Algebraic Reynolds-stress Models

KW - Machine Learning

KW - RANS

KW - Regularised Least-Square Regression

KW - Turbulence Modelling

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SP - 1789

EP - 1795

BT - Proceedings of the 6th European Conference on Computational Mechanics

A2 - Owen, Roger

A2 - de Borst, Rene

A2 - Reese, Jason

A2 - Pearce, Chris

PB - International Centre for Numerical Methods in Engineering, CIMNE

T2 - 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

Y2 - 11 June 2018 through 15 June 2018

ER -

Schmelzer M , Dwight RP, Cinnella P. Symbolic regression of algebraic stress-strain relation for RANS turbulence closure. In Owen R, de Borst R, Reese J, Pearce C, editors, 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. International Centre for Numerical Methods in Engineering, CIMNE. 2020. p. 1789-1795. (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).

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

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