Reynolds-averaged Navier–Stokes simulations represent a cost-effective option for practical
engineering applications, but are facing ever-growing demands for more accurate …

Data-driven machine learning algorithms, random forests and artificial neural network
(ANN), are used to establish the subgrid-scale (SGS) model for large-eddy simulation. A …

We present a new approach for constructing data-driven subgrid stress models for large
eddy simulation of turbulent flows using anisotropic grids. The key to our approach is a …

We study the error scaling properties of large-eddy simulation (LES) in the outer region of
wall-bounded turbulence at moderately high Reynolds numbers. In order to avoid the …

A kinematic simulation with an approximate deconvolution (KSAD) hybrid model is proposed
to predict the Lagrangian relative dispersion of fluid particles in a large eddy simulation …

The anisotropic minimum-dissipation (AMD) model for large-eddy simulation (LES) has
been recently developed, and here the model performance is examined in stratified plane …

We study the numerical errors of large-eddy simulation (LES) in isotropic and wall-bounded
turbulence. A direct-numerical-simulation (DNS)-aided LES formulation, where the subgrid …

A method for finding a nearly optimal computational grid or, more generally, filter-width
distribution for a large eddy simulation is proposed and assessed. The core idea is that the …

The subgrid-scale modelling of a low Mach number strongly anisothermal turbulent flow is
investigated using direct numerical simulations. The study is based on the filtering of the low …

This paper presents a new approach for mitigating the unphysical delay in the Reynolds-
averaged Navier–Stokes (RANS) to large-eddy simulation (LES) transition, often referred to …