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research-article

A Machine Learning Approach for Determining the Turbulent Diffusivity in 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

Gonzalo Saez-Mischlich

ISAE-SUPAERO, University of Toulouse, Toulouse 31400, France
gonzalosaezm55@gmail.com

Julien Bodart

ISAE-SUPAERO, University of Toulouse, Toulouse 31400, France
julien.bodart@gmail.com

John Eaton

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

1Corresponding author.

ASME doi:10.1115/1.4038275 History: Received August 22, 2017; Revised September 23, 2017

Abstract

In film cooling flows, it is important to know the temperature distribution resulting from the interaction between a hot main flow and a cooler jet. However, current Reynolds-averaged Navier-Stokes (RANS) models yield poor temperature predictions. A novel approach for RANS modeling of the turbulent heat flux is proposed, in which the simple gradient diffusion hypothesis (GDH) is assumed and a machine learning algorithm is used to infer an improved turbulent diffusivity field. This approach is implemented using three distinct data sets: two are used to train the model and the third is used for validation. The results show that the proposed method produces significant improvement compared to the common RANS closure, especially in the prediction of film cooling effectiveness.

Copyright (c) 2017 by ASME
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