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pro vyhledávání: '"Patrick Amstad"'
Publikováno v:
Volume 10D: Turbomachinery — Multidisciplinary Design Approaches, Optimization, and Uncertainty Quantification; Turbomachinery General Interest; Unsteady Flows in Turbomachinery.
This paper presents a complete and general machine-learning assisted optimization framework for the Generalized k-ω (GEKO) turbulence model based on experimental measurements. The optimization framework is applied to the use case of a turbocharger r
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::dab6320d824ef6a2d19430fc61498ec9
https://doi.org/10.1115/gt2022-78306
https://doi.org/10.1115/gt2022-78306
