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 …

Computational fluid dynamics models based on Reynolds-averaged Navier–Stokes
equations with turbulence closures still play important roles in engineering design and …

The recent surge in machine learning augmented turbulence modelling is a promising
approach for addressing the limitations of Reynolds-averaged Navier-Stokes (RANS) …

A novel machine learning algorithm is presented, serving as a data-driven turbulence
modeling tool for Reynolds Averaged Navier-Stokes (RANS) simulations. This machine …

Abstract A novel Sparse Bayesian Learning (SBL) framework is introduced for generating
stochastic Explicit Algebraic Reynolds Stress (EARSM) closures for the Reynolds-Averaged …

Abstract Although Reynolds-Averaged Navier–Stokes (RANS) equations are still the
dominant tool for engineering design and analysis applications involving turbulent flows …

We present an approach to sensor placement for turbulent mean flow data assimilation in
the context of Reynolds-averaged Navier–Stokes (RANS) simulations. It entails generating a …

A machine-learning approach for data-driven Reynolds-Averaged Navier–Stokes (RANS)
predictions of turbulent flows including estimates of turbulence modelling uncertainties is …

A direct numerical simulation of a turbulent channel flow with a lower curved wall is
performed at Reynolds number Reτ≈ 600. Low-speed streak structures are extracted from …

In order to achieve a virtual certification process and robust designs for turbomachinery, the
uncertainty bounds for Computational Fluid Dynamics have to be known. The formulation of …