Discovery of Algebraic Reynolds-Stress Models Using Sparse Symbolic Regression

Discovery of Algebraic Reynolds-Stress Models Using Sparse Symbolic Regression

Schmelzer, M. (TU Delft Aerodynamics)
Dwight, R.P. (TU Delft Aerodynamics)
Cinnella, Paola (Arts et Métiers ParisTech)

2019

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.

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

http://resolver.tudelft.nl/uuid:e736b57d-9d7e-48e2-8cd6-2b4336c87f26

https://doi.org/10.1007/s10494-019-00089-x

1386-6184

Flow, Turbulence and Combustion, 104 (2-3), 579-603

Institutional Repository

journal article

© 2019 M. Schmelzer, R.P. Dwight, Paola Cinnella