Implementation of Convolutional Neural Network to Enhance Turbulence Models for Channel Flows

Omid Razizadeh, Sergey N. Yakovenko

Research output: Chapter in Book/Report/Conference proceedingConference contributionResearchpeer-review

Abstract

The convolutional neural network (CNN) is implemented to enhance a turbulence model which is needed to close the Reynolds-averaged Navier-Stokes (RANS) equations. The machine-learning technique uses the available data sets of high fidelity for canonical flow test cases. These data have been produced from large-eddy simulations or direct numerical simulations, which require huge computing resources. At the first stage, the widely used k-? model is taken as a baseline RANS model, and computations are performed by means of OpenFOAM for turbulent flows in the plane channel having the periodic hills on the lower wall and in the converging-diverging channel. Then, the CNN algorithm is applied to these cases. The prediction of the Reynolds-stress anisotropy tensor components is shown to be improved after the application of CNN with the mean square error loss function in comparison with that for the baseline RANS model in the investigated canonical turbulent flows in channels with walls of different geometry.

Original languageEnglish
Title of host publicationProceedings - 2020 Science and Artificial Intelligence Conference, S.A.I.ence 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1-4
Number of pages4
ISBN (Electronic)9780738131115
DOIs
Publication statusPublished - 14 Nov 2020
Event2020 Science and Artificial Intelligence Conference, S.A.I.ence 2020 - Virtual, Novosibirsk, Russian Federation
Duration: 14 Nov 202015 Nov 2020

Publication series

NameProceedings - 2020 Science and Artificial Intelligence Conference, S.A.I.ence 2020

Conference

Conference2020 Science and Artificial Intelligence Conference, S.A.I.ence 2020
CountryRussian Federation
CityVirtual, Novosibirsk
Period14.11.202015.11.2020

Keywords

  • datasets
  • machine learning
  • turbulence models

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