Sparse Bayesian Learning of Explicit Algebraic Reynolds-Stress models for turbulent separated flows

A novel Sparse Bayesian Learning (SBL) framework is introduced for generating stochastic Explicit Algebraic Reynolds Stress (EARSM) closures for the Reynolds-Averaged Navier–Stokes (RANS) equations from high-fidelity data. Building on the recently proposed SpaRTA (Sparse Regression of Turbulent Stre...

Ausführliche Beschreibung

Bibliographische Detailangaben

Veröffentlicht in:	The International journal of heat and fluid flow 2022-12, Vol.98, p.109047, Article 109047
Hauptverfasser:	Cherroud, Soufiane, Merle, Xavier, Cinnella, Paola, Gloerfelt, Xavier
Format:	Artikel
Sprache:	eng
Schlagworte:	Engineering Sciences Explicit Algebraic Reynolds Stress models Fluids mechanics Machine learning Mechanics Sensitivity analysis Separated flows Sparse Bayesian Learning Turbulence modeling
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!