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20212026
most citedDiscovery of a Physically Interpretable Data-Driven Wind-Turbine Wake Model

2 citations · 3 across the 6 of their papers we have counts for

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physics.flu-dyn2025

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.flu-dyn2025

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…

physics.flu-dyn2025★ 2 cited

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…