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Journal Articles Flow, Turbulence and Combustion Year : 2019

Discovery of Algebraic Reynolds-Stress Models Using Sparse Symbolic Regression

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Abstract

A novel deterministic symbolic regression method SpaRTA (Sparse Regression of Turbulent Stress Anisotropy) is introduced to infer algebraic stress models for the closure of RANS equations directly from high-fidelity LES or DNS data. The models are written as tensor polynomials and are built from a library of candidate functions. The machine-learning method is based on elastic net regularisation which promotes sparsity of the inferred models. By being data-driven the method relaxes assumptions commonly made in the process of model development. Model-discovery and cross-validation is performed for three cases of separating flows, i.e. periodic hills (Re=10595), converging-diverging channel (Re=12600) and curved backward-facing step (Re=13700). The predictions of the discovered models are significantly improved over the k-ω SST also for a true prediction of the flow over periodic hills at Re=37000. This study shows a systematic assessment of SpaRTA for rapid machine-learning of robust corrections for standard RANS turbulence models.
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Dates and versions

hal-03710939 , version 1 (01-07-2022)

Licence

Attribution - CC BY 4.0

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Martin Schmelzer, Richard P Dwight, Paola Cinnella. Discovery of Algebraic Reynolds-Stress Models Using Sparse Symbolic Regression. Flow, Turbulence and Combustion, 2019, 104 (2-3), pp.579 - 603. ⟨10.1007/s10494-019-00089-x⟩. ⟨hal-03710939⟩
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