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research-article

Physical Interpretation of Machine Learning Models Applied to Film Cooling Flows

[+] Author and Article Information
Pedro M. Milani

Mechanical Engineering Department, Stanford University, Stanford, CA 94305
pmmilani@stanford.edu

Julia Ling

Principal Scientist, Citrine Informatics, Redwood City, CA 94063
julialing11@gmail.com

Dr. John Eaton

Mechanical Engineering Department, Stanford University, Stanford, CA 94305
eatonj@stanford.edu

1Corresponding author.

ASME doi:10.1115/1.4041291 History: Received August 16, 2018; Revised August 22, 2018

Abstract

Current turbulent heat flux models fail to predict accurate temperature distributions in film cooling flows. The present paper focuses on a machine learning 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. 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 machine learning 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 and eddy viscosity, while other features display localized prominence.

Copyright (c) 2018 by ASME
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