Searching for turbulence models by artificial neural network

Masataka Gamahara, Yuji Hattori

Research output: Contribution to journalArticlepeer-review

78 Citations (Scopus)

Abstract

An artificial neural network (ANN) is tested as a tool for finding a new subgrid model of the subgrid-scale (SGS) stress in large-eddy simulation. An ANN is used to establish a functional relation between the grid-scale flow field and the SGS stress without any assumption of the form of function. Data required for training and test of the ANN are provided by direct numerical simulation of a turbulent channel flow. It is shown that an ANN can establish a model similar to the gradient model. The correlation coefficients between the real SGS stress and the output of the ANN are comparable to or larger than similarity models, but smaller than a two-parameter dynamic mixed model. Large-eddy simulations using the trained ANN are also performed. Although ANN models show no advantage over the Smagorinsky model, the results confirm that the ANN is a promising tool for establishing a new subgrid model with further improvement.

Original languageEnglish
Article number054604
JournalPhysical Review Fluids
Volume2
Issue number5
DOIs
Publication statusPublished - 2017 May

ASJC Scopus subject areas

  • Computational Mechanics
  • Modelling and Simulation
  • Fluid Flow and Transfer Processes

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