514 citations · 1.2k across the 5 of their papers we have counts for
5 papers
Turbulent scalar flux in inclined jets in crossflow: counter gradient transport and deep learning modelling
Pedro M. Milani, Julia Ling, John K. Eaton
A cylindrical and inclined jet in crossflow is studied under two distinct velocity ratios, and , using highly resolved large eddy simulations (LES). First, an investigat…
Generalization of machine-learned turbulent heat flux models applied to film cooling flows
Pedro M. Milani, Julia Ling, John K. Eaton
The design of film cooling systems relies heavily on Reynolds-Averaged Navier-Stokes (RANS) simulations, which solve for mean quantities and model all turbulent scales. Most turbul…
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…
A Priori Assessment of Prediction Confidence for Data-Driven Turbulence Modeling
Jin-Long Wu, Jian-Xun Wang, Heng Xiao +1
Although Reynolds-Averaged Navier-Stokes (RANS) equations are still the dominant tool for engineering design and analysis applications involving turbulent flows, standard RANS mode…