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Sub-grid scale model classification and blending through deep learning

Published online by Cambridge University Press:  14 May 2019

Romit Maulik ,
Omer San Open the ORCID record for Omer San [Opens in a new window] ,
Jamey D. Jacob  and
Christopher Crick
Romit Maulik
Affiliation:
Mechanical and Aerospace Engineering, Oklahoma State University, Stillwater, OK 74078, USA
Omer San*
Affiliation:
Mechanical and Aerospace Engineering, Oklahoma State University, Stillwater, OK 74078, USA
Jamey D. Jacob
Affiliation:
Mechanical and Aerospace Engineering, Oklahoma State University, Stillwater, OK 74078, USA
Christopher Crick
Affiliation:
Computer Science, Oklahoma State University, Stillwater, OK 74078, USA
*
†Email address for correspondence: osan@okstate.edu
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Abstract

In this article we detail the use of machine learning for spatio-temporally dynamic turbulence model classification and hybridization for large eddy simulations (LES) of turbulence. Our predictive framework is devised around the determination of local conditional probabilities for turbulence models that have varying underlying hypotheses. As a first deployment of this learning, we classify a point on our computational grid as that which requires the functional hypothesis, the structural hypothesis or no modelling at all. This ensures that the appropriate model is specified from a priori knowledge and an efficient balance of model characteristics is obtained in a particular flow computation. In addition, we also utilize the conditional-probability predictions of the same machine learning to blend turbulence models for another hybrid closure. Our test case for the demonstration of this concept is given by Kraichnan turbulence, which exhibits a strong interplay of enstrophy and energy cascades in the wavenumber domain. Our results indicate that the proposed methods lead to robust and stable closure and may potentially be used to combine the strengths of various models for complex flow phenomena prediction.

JFM classification

Mathematical Foundations: Computational methods Turbulent Flows: Turbulence modelling

Information

Type
JFM Papers
Information
Journal of Fluid Mechanics , Volume 870 , 10 July 2019 , pp. 784 - 812
Copyright
© 2019 Cambridge University Press 

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