16 citations · 17 across the 3 of their papers we have counts for
3 papers
Machine-Learned Closure of URANS for Stably Stratified Turbulence: Connecting Physical Timescales & Data Hyperparameters of Deep Time-Series Models
Muralikrishnan Gopalakrishnan Meena, Demetri Liousas, Andrew D. Simin +4
We develop time-series machine learning (ML) methods for closure modeling of the Unsteady Reynolds Averaged Navier Stokes (URANS) equations applied to stably stratified turbulence…
Predictions of steady and unsteady flows using machine-learned surrogate models
Shanti Bhushan, Greg W Burgreen, Joshua L Bowman +2
The applicability of computational fluid dynamics (CFD) based design tools depend on the accuracy and complexity of the physical models, for example turbulence models, which remain…
Assessment of Neural Network Augmented Reynolds Averaged Navier Stokes Turbulence Model in Extrapolation Modes
Shanti Bhushan, Greg W. Burgreen, Wesley Brewer +1
A machine-learned (ML) model is developed to enhance the accuracy of turbulence transport equations of Reynolds Averaged Navier Stokes (RANS) solver and applied for periodic hill t…