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

Martin Schmelzer; Richard Dwight; Paola Cinnella

doi:10.2514/6.2018-2900

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

Martin Schmelzer, Richard Dwight, Paola Cinnella

Aerodynamics

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

5 Citations (SciVal)

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.

Original language	English
Title of host publication	2018 Fluid Dynamics Conference
Publisher	American Institute of Aeronautics and Astronautics Inc. (AIAA)
Number of pages	13
ISBN (Electronic)	9781624105531
DOIs	https://doi.org/10.2514/6.2018-2900
Publication status	Published - 1 Jan 2018
Event	48th AIAA Fluid Dynamics Conference, 2018 - Atlanta, United States Duration: 25 Jun 2018 → 29 Jun 2018

Conference

Conference	48th AIAA Fluid Dynamics Conference, 2018
Country/Territory	United States
City	Atlanta
Period	25/06/18 → 29/06/18

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title = "Data-driven deterministic symbolic regression of nonlinear stress-strain relation for RANS turbulence modelling",

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.",

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Schmelzer, M, Dwight, R & Cinnella, P 2018, Data-driven deterministic symbolic regression of nonlinear stress-strain relation for RANS turbulence modelling. in 2018 Fluid Dynamics Conference., AIAA 2018-2900, American Institute of Aeronautics and Astronautics Inc. (AIAA), 48th AIAA Fluid Dynamics Conference, 2018, Atlanta, United States, 25/06/18. https://doi.org/10.2514/6.2018-2900

Data-driven deterministic symbolic regression of nonlinear stress-strain relation for RANS turbulence modelling. / Schmelzer, Martin; Dwight, Richard; Cinnella, Paola.
2018 Fluid Dynamics Conference. American Institute of Aeronautics and Astronautics Inc. (AIAA), 2018. AIAA 2018-2900.

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

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AU - Dwight, Richard

AU - Cinnella, Paola

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N2 - 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.

AB - 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.

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M3 - Conference contribution

AN - SCOPUS:85051295030

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ER -

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

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