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

C Santoni; D Zhang; Z Zhang; D Samaras; F Sotiropoulos; A Khosronejad; Christian Santoni-Ortiz

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

C Santoni
, D Zhang
, Z Zhang
, D Samaras
, F Sotiropoulos
, A Khosronejad
, Christian Santoni-Ortiz

Mechanical Engineering

Research output: Contribution to journal › Article › peer-review

Original language	English
Journal	Physics of Fluids
Volume	36
Issue number	6
State	Published - 2024

Cite this

@article{16423babbcba44ccb709b6678e4869ef,

title = "Toward ultra-efficient high-fidelity predictions of wind turbine wakes: Augmenting the accuracy of engineering models with machine learning",

author = "C Santoni and D Zhang and Z Zhang and D Samaras and F Sotiropoulos and A Khosronejad and Christian Santoni-Ortiz",

year = "2024",

language = "English",

volume = "36",

journal = "Physics of Fluids",

number = "6",

}

TY - JOUR

T1 - Toward ultra-efficient high-fidelity predictions of wind turbine wakes: Augmenting the accuracy of engineering models with machine learning

AU - Santoni, C

AU - Zhang, D

AU - Zhang, Z

AU - Samaras, D

AU - Sotiropoulos, F

AU - Khosronejad, A

AU - Santoni-Ortiz, Christian

PY - 2024

Y1 - 2024

M3 - Article

VL - 36

JO - Physics of Fluids

JF - Physics of Fluids

IS - 6

ER -