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Discovery of Algebraic Reynolds-Stress Models Using Sparse Symbolic Regression

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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https://hal-cnam.archives-ouvertes.fr/hal-03710939
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Submitted on : Friday, July 1, 2022 - 9:26:45 AM
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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, Springer Verlag (Germany), 2019, 104 (2-3), pp.579 - 603. ⟨10.1007/s10494-019-00089-x⟩. ⟨hal-03710939⟩

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