Discovery of Algebraic Reynolds-Stress Models Using Sparse Symbolic Regression

Research output: Contribution to journalArticleScientificpeer-review

6 Citations (Scopus)
25 Downloads (Pure)


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 languageEnglish
Number of pages25
JournalFlow, Turbulence and Combustion
Publication statusE-pub ahead of print - 2019


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

Fingerprint Dive into the research topics of 'Discovery of Algebraic Reynolds-Stress Models Using Sparse Symbolic Regression'. Together they form a unique fingerprint.

Cite this