3 papers
physics.flu-dyn2025
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…
physics.flu-dyn2025
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)…
physics.flu-dyn2025
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…