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Subgrid modelling fortwo-dimensionalturbulence usingneural networks Romit MaulikOmer SanAdil Rasheed2018 год

Subgrid modelling for two-dimensional turbulence using neural networks
статья из журнала

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Аннотация: In this investigation, a data-driven turbulence closure framework is introduced and deployed for the sub-grid modelling of Kraichnan turbulence. The novelty of the proposed method lies in the fact that snapshots from high-fidelity numerical data are used to inform artificial neural networks for predicting the turbulence source term through localized grid-resolved information. In particular, our proposed methodology successfully establishes a map between inputs given by stencils of the vorticity and the streamfunction along with information from two well-known eddy-viscosity kernels. Through this we predict the sub-grid vorticity forcing in a temporally and spatially dynamic fashion. Our study is both a-priori and a-posteriori in nature. In the former, we present an extensive hyper-parameter optimization analysis in addition to learning quantification through probability density function based validation of sub-grid predictions. In the latter, we analyse the performance of our framework for flow evolution in a classical decaying two-dimensional turbulence test case in the presence of errors related to temporal and spatial discretization. Statistical assessments in the form of angle-averaged kinetic energy spectra demonstrate the promise of the proposed methodology for sub-grid quantity inference. In addition, it is also observed that some measure of a-posteriori error must be considered during optimal model selection for greater accuracy. The results in this article thus represent a promising development in the formalization of a framework for generation of heuristic-free turbulence closures from data.
Год издания: 2018
Авторы: Romit Maulik, Omer San, Adil Rasheed, Prakash Vedula
Издательство: Cambridge University Press
Источник: Journal of Fluid Mechanics
Ключевые слова: Model Reduction and Neural Networks, Fluid Dynamics and Turbulent Flows, Meteorological Phenomena and Simulations
Другие ссылки: Journal of Fluid Mechanics (HTML)
OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (HTML)
arXiv (Cornell University) (PDF)
arXiv (Cornell University) (HTML)
Duo Research Archive (University of Oslo) (PDF)
Duo Research Archive (University of Oslo) (HTML)
DataCite API (HTML)