This survey investigates wall modeling in large-eddy simulations (LES) using data-driven
machine-learning (ML) techniques. To this end, we implement three ML wall models in an …

We use experimental and simulation data to recalibrate the standard Spalart–Allmaras
model. Free-shear flow, the buffer layer, the log layer, and flows with adverse pressure …

Differentiable fluid simulators are increasingly demonstrating value as useful tools for
developing data-driven models in computational fluid dynamics (CFD). Differentiable …

Generalisability and the consistency of the a posteriori results are the most critical points of
view regarding data-driven turbulence models. This study presents a progressive …

The prediction of turbulent flow by the application of machine learning (ML) algorithms to big
data is a concept currently in its infancy which requires further development. It is of special …

This paper focuses on the use of reinforcement learning (RL) as a machine-learning (ML)
modeling tool for near-wall turbulence. RL has demonstrated its effectiveness in solving high …

Abstract Machine-learned augmentations to turbulence models can be advantageous for
flows within the training dataset but can often cause harm outside. This lack of …

The traditional approach of using the Monin–Obukhov similarity theory (MOST) to model
near-surface processes in large-eddy simulations (LESs) can lead to significant errors in …

Assessing the compliance of a white-box turbulence model with known turbulent knowledge
is straightforward. It enables users to screen conventional turbulence models and identify …

In the field of data-driven turbulence modeling, the consistency of the a posteriori results and
generalizability are the most critical aspects of new models. In this study, we combine a multi …