5 papers · 1 filter
Bayesian neural network correction of RANS turbulence models with uncertainty quantification in separated flows
Tyler Buchanan, Ali Eidi, Richard P. Dwight
Data-driven correction of turbulence models offers a promising route for improving Reynolds-averaged Navier-Stokes (RANS) predictions, but quantifying uncertainty in such correctio…
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…
Discovery of a Physically Interpretable Data-Driven Wind-Turbine Wake Model
Kherlen Jigjid, Ali Eidi, Nguyen Anh Khoa Doan +1
This study presents a compact data-driven Reynolds-averaged Navier-Stokes (RANS) model for wind turbine wake prediction, built as an enhancement of the standard \(k\)-\(\varepsilon…
Data-Driven RANS Closures Using a Relative Importance Term Analysis Based Classifier for 2D and 3D Separated Flows
Tyler Buchanan, Monica Lăcătuş, Alastair West +1
This study presents a novel approach for enhancing Reynolds-averaged Navier-Stokes (RANS) turbulence modeling through the application of a Relative Importance Term Analysis (RITA)…
Data-driven turbulence modelling for magnetohydrodynamic flows in annular pipes
Alejandro Montoya Santamaria, Tyler Buchanan, Francesco Fico +3
We present a data-driven approach to Reynolds-averaged Navier-Stokes turbulence closure modelling in magnetohydrodynamic (MHD) flows. In these flows the magnetic field interacting…