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Toward ultra-efficient high fidelity predictions of wind turbine wakes: Augmenting the accuracy of engineering models via LES-trained machine learning

Fuente: arXiv

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Détails bibliographiques
Auteurs principaux:	Santoni, Christian, Zhang, Dichang, Zhang, Zexia, Samaras, Dimitris, Sotiropoulos, Fotis, Khosronejad, Ali
Format:	Preprint
Publié:	2024
Sujets:	Fluid Dynamics
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

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