Data-driven quantification of model-form uncertainty in Reynolds-averaged simulations of wind farms
Ali Eidi,
Navid Zehtabiyan-Rezaie,
Reza Ghiassi
et al.
Abstract:Computational fluid dynamics using the Reynolds-averaged Navier-Stokes (RANS) remains the most costeffective approach to study wake flows and power losses in wind farms. The underlying assumptions associated with turbulence closures is one of the biggest sources of errors and uncertainties in the model predictions. This work aims to quantify model-form uncertainties in RANS simulations of wind farms by perturbing the Reynolds stress tensor through a data-driven machine-learning technique. To this end, the extr… Show more
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