36 citations · 62 across the 9 of their papers we have counts for
21 papers
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…
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…
DAFI: An Open-Source Framework for Ensemble-Based Data Assimilation and Field Inversion
Carlos A. Michelén Ströfer, Xin-Lei Zhang, Heng Xiao
In many areas of science and engineering, it is a common task to infer physical fields from sparse observations. This paper presents the DAFI code intended as a flexible framework…