Current turbulent heat flux models fail to predict accurate temperature distributions in film cooling flows. The present paper focuses on a machine learning (ML) approach to this problem, in which the gradient diffusion hypothesis (GDH) is used in conjunction with a data-driven prediction for the turbulent diffusivity field αt. An overview of the model is presented, followed by validation against two film cooling datasets. Despite insufficiencies, the model shows some improvement in the near-injection region. The present work also attempts to interpret the complex ML decision process, by analyzing the model features and determining their importance. These results show that the model is heavily reliant of distance to the wall d and eddy viscosity νt, while other features display localized prominence.
Physical Interpretation of Machine Learning Models Applied to Film Cooling Flows
Redwood City, CA 94063
Stanford, CA 94305
Contributed by the International Gas Turbine Institute (IGTI) of ASME for publication in the JOURNAL OF TURBOMACHINERY. Manuscript received August 16, 2018; final manuscript received August 22, 2018; published online October 17, 2018. Editor: Kenneth Hall.
Milani, P. M., Ling, J., and Eaton, J. K. (October 17, 2018). "Physical Interpretation of Machine Learning Models Applied to Film Cooling Flows." ASME. J. Turbomach. January 2019; 141(1): 011004. https://doi.org/10.1115/1.4041291
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