59 citations · 271 across the 20 of their papers we have counts for
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Physics-guided machine learning for wind-farm power prediction: Toward interpretability and generalizability
Navid Zehtabiyan-Rezaie, Alexandros Iosifidis, Mahdi Abkar
With the increasing amount of available data from simulations and experiments, research for the development of data-driven models for wind-farm power prediction has increased signi…
Survey of machine learning wall models for large eddy simulation
Aurélien Vadrot, Xiang I. A. Yang, Mahdi Abkar
This survey investigates wall modeling in large eddy simulations (LES) using data-driven machine learning (ML) techniques. To this end, we implement three ML wall models in an open…
Frozen propagation of Reynolds force vector from high-fidelity data into Reynolds-averaged simulations of secondary flows
Ali Amarloo, Pourya Forooghi, Mahdi Abkar
Successful propagation of information from high-fidelity sources (i.e., direct numerical simulations and large-eddy simulations) into Reynolds-averaged Navier-Stokes (RANS) equatio…
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…
Data-driven fluid mechanics of wind farms: A review
Navid Zehtabiyan-Rezaie, Alexandros Iosifidis, Mahdi Abkar
With the growing number of wind farms over the last decades and the availability of large datasets, research in wind-farm flow modeling - one of the key components in optimizing th…