102 citations · 262 across the 10 of their papers we have counts for
6 papers · 1 filter
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…
Learning neural-network-based turbulence models for external transonic flows using ensemble Kalman method
Yi Liu, Xin-Lei Zhang, Guowei He
This paper presents a neural network-based turbulence modeling approach for transonic flows based on the ensemble Kalman method. The approach adopts a tensor basis neural network f…
Ensemble Kalman method for learning turbulence models from indirect observation data
Xin-Lei Zhang, Heng Xiao, Xiaodong Luo +1
In this work, we propose using an ensemble Kalman method to learn a nonlinear eddy viscosity model, represented as a tensor basis neural network, from velocity data. Data-driven tu…
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…
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…