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A neural network approach for the blind deconvolution of turbulent flows

Published online by Cambridge University Press:  13 October 2017

R. Maulik  and
O. San Open the ORCID record for O. San [Opens in a new window]
R. Maulik
Affiliation:
School of Mechanical and Aerospace Engineering, Oklahoma State University, Stillwater, OK 74078, USA
O. San*
Affiliation:
School of Mechanical and Aerospace Engineering, Oklahoma State University, Stillwater, OK 74078, USA
*
†Email address for correspondence: osan@okstate.edu
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Abstract

We present a single-layer feed-forward artificial neural network architecture trained through a supervised learning approach for the deconvolution of flow variables from their coarse-grained computations such as those encountered in large eddy simulations. We stress that the deconvolution procedure proposed in this investigation is blind, i.e. the deconvolved field is computed without any pre-existing information about the filtering procedure or kernel. This may be conceptually contrasted to the celebrated approximate deconvolution approaches where a filter shape is predefined for an iterative deconvolution process. We demonstrate that the proposed blind deconvolution network performs exceptionally well in the a priori testing of two-dimensional Kraichnan, three-dimensional Kolmogorov and compressible stratified turbulence test cases, and shows promise in forming the backbone of a physics-augmented data-driven closure for the Navier–Stokes equations.

JFM classification

Mathematical Foundations: Computational methods Turbulent Flows: Turbulence modelling

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Papers
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Journal of Fluid Mechanics , Volume 831 , 25 November 2017 , pp. 151 - 181
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© 2017 Cambridge University Press 

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