2 citations · 3 across the 6 of their papers we have counts for
6 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…
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…
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 quantification of model-form uncertainty in Reynolds-averaged simulations of wind farms
Ali Eidi, Navid Zehtabiyan-Rezaie, Reza Ghiassi +2
Computational fluid dynamics using the Reynolds-averaged Navier-Stokes (RANS) remains the most cost-effective approach to study wake flows and power losses in wind farms. The under…
Model-form uncertainty quantification in RANS simulations of wakes and power losses in wind farms
Ali Eidi, Reza Ghiassi, Xiang Yang +1
Reynolds-averaged Navier-Stokes (RANS) is one of the most cost-efficient approaches to simulate wind-farm-atmosphere interactions. However, the applicability of RANS-based methods…