Data from experiments and direct simulations of turbulence have historically been used to
calibrate simple engineering models such as those based on the Reynolds-averaged Navier …

In computational fluid dynamics simulations of industrial flows, models based on the
Reynolds-averaged Navier–Stokes (RANS) equations are expected to play an important …

Gaining and understanding flow dynamics have much importance in a wide range of
disciplines, eg, astrophysics, geophysics, biology, mechanical engineering, and biomedical …

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

We propose a data-driven, closure model for Reynolds-averaged Navier-Stokes (RANS)
simulations that incorporates aleatoric, model uncertainty. The proposed closure consists of …

Data assimilation can reduce the model-form errors of RANS simulations. A spatially
distributed corrective parameter field can be introduced into the closure model, whose …

Inverse problems are common and important in many applications in computational physics
but are inherently ill-posed with many possible model parameters resulting in satisfactory …

A CFD-driven deterministic symbolic identification algorithm for learning explicit algebraic
Reynolds-stress models (EARSM) from high-fidelity data is developed building on the frozen …

It is well known that the Boussinesq turbulent-viscosity hypothesis can introduce uncertainty
in predictions for complex flow features such as separation, reattachment, and laminar …

The application of machine learning (ML) algorithms to turbulence modeling has shown
promise over the last few years, but their application has been restricted to eddy viscosity …