Discovery of Algebraic Reynolds-Stress Models Using Sparse Symbolic Regression

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 ( R e =10595), converging-diverging channel ( R e =12600) and curved backward-facing step ( R e =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 R e =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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