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Physical interpretation of neural network-based nonlinear eddy viscosity models
Xin-Lei Zhang, Heng Xiao, Solkeun Jee +1
Neural network-based turbulence modeling has gained significant success in improving turbulence predictions by incorporating high--fidelity data. However, the interpretability of t…
Combining direct and indirect sparse data for learning generalizable turbulence models
Xin-Lei Zhang, Heng Xiao, Xiaodong Luo +1
Learning turbulence models from observation data is of significant interest in discovering a unified model for a broad range of practical flow applications. Either the direct obser…
A PDE-free, neural network-based eddy viscosity model coupled with RANS equations
Ruiying Xu, Xu-Hui Zhou, Jiequn Han +2
Most turbulence models used in Reynolds-averaged Navier-Stokes (RANS) simulations are partial differential equations (PDE) that describe the transport of turbulent quantities. Such…
An equivariant neural operator for developing nonlocal tensorial constitutive models
Jiequn Han, Xu-Hui Zhou, Heng Xiao
Developing robust constitutive models is a fundamental and longstanding problem for accelerating the simulation of complicated physics. Machine learning provides promising tools to…
Neural Network Based Pore Flow Field Prediction in Porous Media Using Super Resolution
Xu-Hui Zhou, James E. McClure, Cheng Chen +1
Direct pore-scale simulations of fluid flow through porous media are computationally expensive to perform for realistic systems. Previous works have demonstrated using the geometry…
Ensemble gradient for learning turbulence models from indirect observations
Carlos A. Michelén Ströfer, Xin-Lei Zhang, Heng Xiao
Training data-driven turbulence models with high fidelity Reynolds stress can be impractical and recently such models have been trained with velocity and pressure measurements. For…
End-to-end differentiable learning of turbulence models from indirect observations
Carlos A. Michelén Ströfer, Heng Xiao
The emerging push of the differentiable programming paradigm in scientific computing is conducive to training deep learning turbulence models using indirect observations. This pape…
Assimilation of disparate data for enhanced reconstruction of turbulent mean flows
Xin-Lei Zhang, Heng Xiao, Guo-Wei He +1
Reconstruction of turbulent flow based on data assimilation methods is of significant importance for improving the estimation of flow characteristics by incorporating limited obser…
Recurrent Neural Network for End-to-End Modeling of Laminar-Turbulent Transition
Muhammad I. Zafar, Meelan M. Choudhari, Pedro Paredes +1
Accurate prediction of laminar-turbulent transition is a critical element of computational fluid dynamics simulations for aerodynamic design across multiple flow regimes. Tradition…
Frame-independent vector-cloud neural network for nonlocal constitutive modeling on arbitrary grids
Xu-Hui Zhou, Jiequn Han, Heng Xiao
Constitutive models are widely used for modeling complex systems in science and engineering, where first-principle-based, well-resolved simulations are often prohibitively expensiv…
Learning nonlocal constitutive models with neural networks
Xu-Hui Zhou, Jiequn Han, Heng Xiao
Constitutive and closure models play important roles in computational mechanics and computational physics in general. Classical constitutive models for solid and fluid materials ar…
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…
Convolutional Neural Network for Transition Modeling Based on Linear Stability Theory
Muhammad I. Zafar, Heng Xiao, Meelan M. Choudhari +4
Transition prediction is an important aspect of aerodynamic design because of its impact on skin friction and potential coupling with flow separation characteristics. Traditionally…
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…
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…
Quantification of Model Uncertainty in RANS Simulations: A Review
Heng Xiao, Paola Cinnella
In computational fluid dynamics simulations of industrial flows, models based on the Reynolds-averaged Navier--Stokes (RANS) equations are expected to play an important role in dec…
Turbulence Modeling in the Age of Data
Karthik Duraisamy, Gianluca Iaccarino, Heng Xiao
Data from experiments and direct simulations of turbulence have historically been used to calibrate simple engineering models such as those based on the Reynolds-averaged Navier--S…
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…
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…