7 citations · 14 across the 3 of their papers we have counts for
6 papers · 1 filter
Obtaining the mean fields with known Reynolds stresses at steady state
Xianwen Guo, Zhenhua Xia, Heng Xiao +2
With the rising of modern data science, data--driven turbulence modeling with the aid of machine learning algorithms is becoming a new promising field. Many approaches are able to…
Flows Over Periodic Hills of Parameterized Geometries: A Dataset for Data-Driven Turbulence Modeling From Direct Simulations
Heng Xiao, Jin-Long Wu, Sylvain Laizet +1
Computational fluid dynamics models based on Reynolds-averaged Navier--Stokes equations with turbulence closures still play important roles in engineering design and analysis. Howe…
RANS Equations with Explicit Data-Driven Reynolds Stress Closure Can Be Ill-Conditioned
Jin-Long Wu, Heng Xiao, Rui Sun +1
Reynolds-averaged Navier--Stokes (RANS) simulations with turbulence closure models continue to play important roles in industrial flow simulations. However, the commonly used linea…
Data-Driven, Physics-Based Feature Extraction from Fluid Flow Fields
Carlos Michelén Ströfer, Jinlong Wu, Heng Xiao +1
Feature identification is an important task in many fluid dynamics applications and diverse methods have been developed for this purpose. These methods are based on a physical unde…
Representation of Reynolds Stress Perturbations with Application in Machine-Learning-Assisted Turbulence Modeling
Jinlong Wu, Rui Sun, Sylvain Laizet +1
Numerical simulations based on Reynolds-Averaged Navier--Stokes (RANS) equations are widely used in engineering design and analysis involving turbulent flows. However, RANS simulat…
Quantifying Model Form Uncertainty in RANS Simulation of Wing-Body Junction Flow
Jin-Long Wu, Jian-Xun Wang, Heng Xiao
Wing-body junction flows occur when a boundary layer encounters an airfoil mounted on the surface. The corner flow near the trailing edge is challenging for the linear eddy viscosi…