632 citations · 2.8k across the 17 of their papers we have counts for
17 papers
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…
Enforcing Deterministic Constraints on Generative Adversarial Networks for Emulating Physical Systems
Zeng Yang, Jin-Long Wu, Heng Xiao
Generative adversarial networks (GANs) were initially proposed to generate images by learning from a large number of samples. Recently, GANs have been used to emulate complex physi…
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…
Enforcing Statistical Constraints in Generative Adversarial Networks for Modeling Chaotic Dynamical Systems
Jin-Long Wu, Karthik Kashinath, Adrian Albert +3
Simulating complex physical systems often involves solving partial differential equations (PDEs) with some closures due to the presence of multi-scale physics that cannot be fully…
Seeing Permeability From Images: Fast Prediction with Convolutional Neural Networks
Jin-Long Wu, Xiao-Long Yin, Heng Xiao
Fast prediction of permeability directly from images enabled by image recognition neural networks is a novel pore-scale modeling method that has a great potential. This article pre…
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…
Physics-Informed Machine Learning Approach for Augmenting Turbulence Models: A Comprehensive Framework
Jin-Long Wu, Heng Xiao, Eric Paterson
Reynolds-averaged Navier-Stokes (RANS) equations are widely used in engineering turbulent flow simulations. However, RANS predictions may have large discrepancies due to the uncert…
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…
A Comprehensive Physics-Informed Machine Learning Framework for Predictive Turbulence Modeling
Jian-Xun Wang, Jinlong Wu, Julia Ling +2
Although an increased availability of computational resources has enabled high-fidelity simulations of turbulent flows, the RANS models are still the dominant tools for industrial…
A Priori Assessment of Prediction Confidence for Data-Driven Turbulence Modeling
Jin-Long Wu, Jian-Xun Wang, Heng Xiao +1
Although Reynolds-Averaged Navier-Stokes (RANS) equations are still the dominant tool for engineering design and analysis applications involving turbulent flows, standard RANS mode…
Are Discrepancies in RANS Modeled Reynolds Stresses Random?
Heng Xiao, Jin-Long Wu, Jian-xun Wang +1
In the turbulence modeling community, significant efforts have been made to quantify the uncertainties in the Reynolds-Averaged Navier--Stokes (RANS) models and to improve their pr…
A Physics Informed Machine Learning Approach for Reconstructing Reynolds Stress Modeling Discrepancies Based on DNS Data
Jian-Xun Wang, Jin-Long Wu, Heng Xiao
Turbulence modeling is a critical component in numerical simulations of industrial flows based on Reynolds-averaged Navier-Stokes (RANS) equations. However, after decades of effort…
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…
Incorporating Prior Knowledge for Quantifying and Reducing Model-Form Uncertainty in RANS Simulations
Jian-Xun Wang, Jin-Long Wu, Heng Xiao
Simulations based on Reynolds-Averaged Navier--Stokes (RANS) models have been used to support high-consequence decisions related to turbulent flows. Apart from the deterministic mo…
A Bayesian Calibration-Prediction Method for Reducing Model-Form Uncertainties with Application in RANS Simulations
J. -L. Wu, J. -X. Wang, H. Xiao
Model-form uncertainties in complex mechanics systems are a major obstacle for predictive simulations. Reducing these uncertainties is critical for stake-holders to make risk-infor…
Quantifying and Reducing Model-Form Uncertainties in Reynolds-Averaged Navier-Stokes Simulations: A Data-Driven, Physics-Based Bayesian Approach
H. Xiao, J. -L. Wu, J. -X. Wang +2
Despite their well-known limitations, Reynolds-Averaged Navier-Stokes (RANS) models are still the workhorse tools for turbulent flow simulations in today's engineering application.…