most citedLog-law recovery through reinforcement-learning wall model for large-eddy simulation

25 citations · 26 across the 2 of their papers we have counts for

collaborators

5 papers

physics.flu-dyn2024

Wall-modeled large-eddy simulation of turbulent smooth body separation using the OpenFOAM flow solver

Christoffer Hansen, Xiang I. A. Yang, Mahdi Abkar

This work investigates the current wall-modeled large-eddy simulation (WMLES) capabilities of the open-source computational fluid dynamics solver OpenFOAM, which is used widely in…

physics.soc-ph2024

Computational Fluid Dynamics: its Carbon Footprint and Role in Carbon Emission Reduction

Xiang I A Yang, Wen Zhang, Mahdi Abkar +1

Turbulent flow physics regulates the aerodynamic properties of lifting surfaces, the thermodynamic efficiency of vapor power systems, and exchanges of natural and anthropogenic qua…

physics.flu-dyn20231 cited

A priori screening of data-enabled turbulence models

Peng E S Chen, Yuanwei Bin, Xiang I A Yang +3

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 a…

physics.flu-dyn202325 cited

Log-law recovery through reinforcement-learning wall model for large-eddy simulation

Aurélien Vadrot, Xiang I. A. Yang, H. Jane Bae +1

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 hig…

physics.flu-dyn20191 cited

A nonlinear subgrid-scale model for large-eddy simulations of rotating turbulent flows

Maurits H. Silvis, H. Jane Bae, F. Xavier Trias +2

Rotating turbulent flows form a challenging test case for large-eddy simulation (LES). We, therefore, propose and validate a new subgrid-scale (SGS) model for such flows. The propo…