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

Turbulence closure models are central to a good deal of applied computational fluid
dynamical analysis. Closure modeling endures as a productive area of research. This …

The hybridization of Reynolds-averaged Navier-Stokes (RANS) and large eddy simulation
(LES) methods is seen to be the most promising way to efficiently deal with separated …

Spatial artificial neural network (ANN) models are developed for subgrid-scale (SGS) forces
in the large eddy simulation (LES) of turbulence. The input features are based on the first …

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

In this work, the subgrid-scale (SGS) stress and the SGS heat flux of compressible isotropic
turbulence are modeled by an artificial neural network (ANN) mixed model (ANNMM), which …

The possible ill conditioning of the Reynolds average Navier–Stokes (RANS) equations
when an explicit data-driven Reynolds stress tensor closure is employed is a discussion of …

Coarse graining is an important ingredient in many multi-scale continuum–discrete solvers
such as CFD–DEM (computational fluid dynamics–discrete element method) solvers for …

The prediction and investigation of very high Reynolds number turbulent wall flows pose a
significant challenge: experimental studies and large eddy simulation (LES) are often …

Turbulent flow simulations based on the Reynolds-averaged Navier–Stokes (RANS)
equations continue to be the workhorse approach for industrial flow problems. However, due …