4 papers
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…
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…
Uncertainty analysis of URANS simulations coupled with an anisotropic pressure fluctuation model
Ali Eidi, Richard P. Dwight
Accurate prediction of pressure and velocity fluctuations in turbulent flows is essential for understanding flow-induced vibration and structural fatigue. This study investigates t…
Physics-guided Bayesian neural networks for zonal corrections and uncertainty quantification in separated flows
Ali Eidi, Tyler Buchanan, Letian Jiang +1
Data-driven techniques have improved the accuracy of Reynolds-averaged Navier-Stokes (RANS) models in fluid dynamics. However, modeling separated flows remains challenging due to t…