## 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.

Original language | English |
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Pages (from-to) | 579-603 |

Number of pages | 25 |

Journal | Flow, Turbulence and Combustion |

Volume | 104 |

Issue number | 2-3 |

DOIs | |

Publication status | E-pub ahead of print - 2019 |

## Keywords

- Data-driven
- Explicit Algebraic Reynolds-stress models
- Machine learning
- Sparse symbolic regression
- Turbulence modelling