Successful propagation of information from high-fidelity sources (ie, direct numerical
simulations and large-eddy simulations) into Reynolds-averaged Navier–Stokes (RANS) …

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 …

For developing a reliable data-driven Reynold stress tensor (RST) model, successful
reconstruction of the mean velocity field based on high-fidelity information (ie, direct …

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 …

The mean velocity follows a logarithmic scaling in the surface layer when normalized by the
friction velocity, ie a velocity scale derived from the wall-shear stress. The same logarithmic …

Insights gained from modal analysis are invoked for predictive large-eddy simulation (LES)
wall modelling. Specifically, we augment the law of the wall (LoW) by an additional mode …

A stratified wake has multiple flow regimes, and exhibits different behaviors in these regimes
due to the competing physical effects of momentum and buoyancy. This work aims at …

Background As the impact of wildfires has become increasingly more severe over the last
decades, there is continued need for improvements in our ability to predict wildland fire …

The goal of this work is to investigate the feasibility of constructing data-driven dynamical
system models of roughness-induced secondary flows in thermally stratified turbulent …

Although scaling laws and coherent structures have been well-studied in non-neutral
boundary layer flows over a long period of time, a potential connection between them is still …