Transition Modeling for Low Pressure Turbines Using Computational Fluid Dynamics Driven Machine Learning

Transition Modeling for Low Pressure Turbines Using Computational Fluid Dynamics Driven Machine Learning

Existing Reynolds Averaged Navier–Stokes-based transition models do not accurately predict separation induced transition for low pressure turbines. Therefore, in this paper, a novel framework based on computational fluids dynamics (CFD) driven machine learning coupled with multi-expression and multi...

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Bibliographic Details
Main Authors: Harshal D. Akolekar, Fabian Waschkowski, Yaomin Zhao, Roberto Pacciani, Richard D. Sandberg
Format: Article
Language:English
Published: MDPI AG 2021-08-01
Series:Energies
Subjects:
machine learning
multi-objective optimization
low pressure turbine
transition
turbulence modeling
Online Access:https://www.mdpi.com/1996-1073/14/15/4680

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https://www.mdpi.com/1996-1073/14/15/4680

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