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A Machine Learning Approach for Determining the Turbulent Diffusivity in Film Cooling Flows

Milani, Pedro M. and Ling, Julia and Sáez Mischlich, Gonzalo and Bodart, Julien and Eaton, John K. A Machine Learning Approach for Determining the Turbulent Diffusivity in Film Cooling Flows. (2018) Journal of Turbomachinery, 140 (2). 1-8. ISSN 0889-504X

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Official URL: http://dx.doi.org/10.1115/1.4038275

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.

Item Type:Article
Audience (journal):International peer-reviewed journal
Uncontrolled Keywords:
Institution:Université de Toulouse > Institut Supérieur de l'Aéronautique et de l'Espace - ISAE-SUPAERO (FRANCE)
Other partners > Sandia (USA)
Other partners > Stanford University (USA)
Laboratory name:
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Deposited By: Julien Bodart
Deposited On:13 Dec 2017 12:08

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