Open AccessMachine Learning for the Development of Data-Driven Turbulence Closures in Coolant Systems
Author(s) -
James Hammond,
Francesco Montomoli,
Marco Pietropaoli,
Richard D. Sandberg,
Vittorio Michelassi
Publication year - 2022
Publication title -
journal of turbomachinery
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.972
H-Index - 112
eISSN - 1528-8900
pISSN - 0889-504X
DOI - 10.1115/1.4053533
Subject(s) - reynolds averaged navier–stokes equations , turbulence , fidelity , computer science , work (physics) , turbine blade , heat transfer , duct (anatomy) , high fidelity , coolant , experimental data , data driven , turbine , mechanical engineering , engineering , mechanics , artificial intelligence , physics , mathematics , medicine , telecommunications , statistics , electrical engineering , pathology
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