The predictive capabilities of turbulent flow simulations, critical for aerodynamic design and
weather prediction, hinge on the choice of turbulence models. The abundance of data from …

A major drawback of Boussinesq-type subgrid-scale stress models used in large-eddy
simulations is the inherent assumption of alignment between large-scale strain rates and …

The present paper discusses the physical reliability of turbulence modeling in an adverse
pressure gradient wall flow setup at moderate/high Reynolds number by comparing wall …

The current work presents an innovative numerical technique for high-Reynolds/high-Mach
number compressible flow simulations in complex configurations. In particular, the research …

Turbulent signals are intermittent with large instantaneous fluctuations. Such large
fluctuations lead to small Kolmogorov scales that are hard to resolve in numerical …

This study proposes a physics-informed machine learning to enable using the erroneous
flow data at near-wall grid points as the input to the wall model in a wall-modeled large-eddy …

We develop a wall model for large-eddy simulation (LES) that takes into account various
pressure-gradient effects using multi-agent reinforcement learning (MARL). The model is …

In the present study, a priori assessment is performed on the ability of the convolutional
neural network (CNN) for wall-modeling in large eddy simulation. The data used for the …

Large-eddy simulations (LES) are widely used for computing high Reynolds number
turbulent flows. Spatial filtering theory for LES is not without its shortcomings, including how …

In the framework of wall-modeled large-eddy simulation (WMLES), the problem of combining
sub-grid scale (SGS) models with the standard wall law is commonly acknowledged and …