79 citations · 148 across the 11 of their papers we have counts for
11 papers
Rotational equivariance and locality in data-driven subgrid-scale closures
Ryley McConkey, Julia Balla, Elyssa Hofgard +2
Data-driven subgrid-scale closures for large eddy simulation are of significant interest in many engineering and geoscience applications. In this context, several important questio…
The Closure Challenge: a benchmark task for machine learning in turbulence modelling
Ryley McConkey, Tyler Buchanan, Tess Smidt +3
We introduce a field-wide benchmark challenge for machine learning in Reynolds-averaged Navier-Stokes (RANS) turbulence modelling. Though open-source datasets exist for training da…
Turbulence teaches equivariance to neural networks
Ryley McConkey, Julia Balla, Jeremiah Bailey +5
We show that the rotational nature of turbulence affects how neural networks learn mappings between quantities governed by the Navier-Stokes equations. We train super-resolution mo…
Implicit Augmentation from Distributional Symmetry in Turbulence Super-Resolution
Julia Balla, Jeremiah Bailey, Ali Backour +4
The immense computational cost of simulating turbulence has motivated the use of machine learning approaches for super-resolving turbulent flows. A central challenge is ensuring th…
Kolmogorov-Arnold Networks for Turbulence Anisotropy Mapping
Nikhila Kalia, Ryley McConkey, Eugene Yee +1
This study evaluates the generalization performance and representation efficiency (parsimony) of a previously introduced Tensor Basis Kolmogorov-Arnold Network (TBKAN) architecture…
Bayesian Optimization of the GEKO Turbulence Model for Predicting Flow Separation Over a Smooth Surface
Nikhila Kalia, Ryley McConkey, Eugene Yee +1
This paper applies Bayesian-optimization-RANS (turbo-RANS) to improve Reynolds-averaged Navier-Stokes (RANS) turbulence models for a converging-diverging channel, a case with adver…
Realizability-Informed Machine Learning for Turbulence Anisotropy Mappings
Ryley McConkey, Nikhila Kalia, Eugene Yee +1
Within the context of machine learning-based closure mappings for RANS turbulence modelling, physical realizability is often enforced using ad-hoc postprocessing of the predicted a…
turbo-RANS: Straightforward and Efficient Bayesian Optimization of Turbulence Model Coefficients
Ryley McConkey, Nikhila Kalia, Eugene Yee +1
Industrial simulations of turbulent flows often rely on Reynolds-averaged Navier-Stokes (RANS) turbulence models, which contain numerous closure coefficients that need to be calibr…
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…
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…
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…