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Uncovering near-wall blood flow from sparse data with physics-informed neural networks
Amirhossein Arzani, Jian-Xun Wang, Roshan M. D'Souza
Near-wall blood flow and wall shear stress (WSS) regulate major forms of cardiovascular disease, yet they are challenging to quantify with high fidelity. Patient-specific computati…
Super-resolution and denoising of fluid flow using physics-informed convolutional neural networks without high-resolution labels
Han Gao, Luning Sun, Jian-Xun Wang
High-resolution (HR) information of fluid flows, although preferable, is usually less accessible due to limited computational or experimental resources. In many cases, fluid data a…
A Bi-fidelity Surrogate Modeling Approach for Uncertainty Propagation in Three-Dimensional Hemodynamic Simulations
Han Gao, Xueyu Zhu, Jian-Xun Wang
Image-based computational fluid dynamics (CFD) modeling enables derivation of hemodynamic information, which has become a paradigm in cardiovascular research and healthcare. Noneth…
Prediction of Reynolds Stresses in High-Mach-Number Turbulent Boundary Layers using Physics-Informed Machine Learning
Jian-Xun Wang, Junji Huang, Lian Duan +1
Modeled Reynolds stress is a major source of model-form uncertainties in Reynolds-averaged Navier-Stokes (RANS) simulations. Recently, a physics-informed machine-learning (PIML) ap…
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…
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…
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…
A Random Matrix Approach for Quantifying Model-Form Uncertainties in Turbulence Modeling
Heng Xiao, Jian-Xun Wang, Roger G. Ghanem
With the ever-increasing use of Reynolds-Averaged Navier--Stokes (RANS) simulations in mission-critical applications, the quantification of model-form uncertainty in RANS models ha…
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…