Spatiotemporally dynamic implicit large eddy simulation using machine learning classifiers

Maulik, Romit; San, Omer; Jacob, Jamey D.

doi:10.1016/j.physd.2020.132409

Spatiotemporally dynamic implicit large eddy simulation using machine learning classifiers

Journal Article · Sat Feb 15 00:00:00 EST 2020 · Physica. D, Nonlinear Phenomena

DOI:https://doi.org/10.1016/j.physd.2020.132409· OSTI ID:1737502

Maulik, Romit ^[1]; San, Omer ^[2]; Jacob, Jamey D. ^[2]

Oklahoma State Univ., Stillwater, OK (United States); Oklahoma State University Stillwater
Oklahoma State Univ., Stillwater, OK (United States)

In this article, we utilize machine learning to dynamically determine if a point on the computational grid requires implicit numerical dissipation for large eddy simulation (LES). The decision making process is learnt through a priori training on quantities derived from direct numerical simulation (DNS) data. In particular, we compute eddy-viscosities obtained through the coarse-graining of DNS quantities and utilize their projection onto a Gaussian distribution to categorize areas that may require dissipation. If our learning determines that closure is necessary, an upwinded scheme is utilized for computing the non-linear Jacobian. In contrast, if it is determined that closure is unnecessary, a symmetric and second-order accurate energy and enstrophy preserving Arakawa scheme is utilized instead. This results in a closure framework that precludes the specification of any model-form for the small scale contributions of turbulence but deploys an appropriate numerical dissipation from explicit closure driven hypotheses. Here, this methodology is deployed for the Kraichnan turbulence test-case and assessed through various statistical quantities such as angle-averaged kinetic energy spectra and vorticity structure functions. Our framework thus establishes a link between the use of explicit LES ideologies for closure and numerical dissipation-based modeling of turbulence leading to improved statistical fidelity of a posteriori simulations.

View Accepted Manuscript (DOE)

Research Organization:: Oklahoma State Univ., Stillwater, OK (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR)

Grant/Contract Number:: SC0019290

OSTI ID:: 1737502

Alternate ID(s):: OSTI ID: 1602314

Journal Information:: Physica. D, Nonlinear Phenomena, Journal Name: Physica. D, Nonlinear Phenomena Vol. 406; ISSN 0167-2789

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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