79 citations · 136 across the 3 of their papers we have counts for
3 papers
physics.flu-dyn2022★ 17 cited
On the generalizability of machine-learning-assisted anisotropy mappings for predictive turbulence modelling
Ryley McConkey, Eugene Yee, Fue-Sang Lien
Several machine learning frameworks for augmenting turbulence closure models have been recently proposed. However, the generalizability of an augmented turbulence model remains an…
physics.flu-dyn2022★ 40 cited
Deep Structured Neural Networks for Turbulence Closure Modelling
Ryley McConkey, Eugene Yee, Fue-Sang Lien
Despite well-known limitations of Reynolds-averaged Navier-Stokes (RANS) simulations, this methodology remains the most widely used tool for predicting many turbulent flows, due to…
physics.flu-dyn2021★ 79 cited
A curated dataset for data-driven turbulence modelling
Ryley McConkey, Eugene Yee, Fue-Sang Lien
The recent surge in machine learning augmented turbulence modelling is a promising approach for addressing the limitations of Reynolds-averaged Navier-Stokes (RANS) models. This wo…