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Characterization of Model-Based Uncertainties in Incompressible Turbulent Flows by Machine Learning

[+] Author Affiliations
Mustafa Usta

Georgia Institute of Technology, Atlanta, GA

Ali Tosyali

Rutgers University, Piscataway, NJ

Paper No. IMECE2018-87178, pp. V007T09A029; 10 pages
doi:10.1115/IMECE2018-87178
From:
  • ASME 2018 International Mechanical Engineering Congress and Exposition
  • Volume 7: Fluids Engineering
  • Pittsburgh, Pennsylvania, USA, November 9–15, 2018
  • Conference Sponsors: ASME
  • ISBN: 978-0-7918-5210-1
  • Copyright © 2018 by ASME

abstract

This work determines the inaccuracy of using Reynolds averaged Navier Stokes (RANS) turbulence models in transition to turbulent flow regimes by predicting the model-based discrepancies between RANS and large eddy simulation (LES) models. Then, it incorporates the capabilities of machine learning algorithms to characterize the discrepancies which are defined as a function of mean flow properties of RANS simulations. First, three-dimensional CFD simulations using k-omega Shear Stress Transport (SST) and dynamic one-equation subgrid-scale models are conducted in a wall-bounded channel containing a cylinder for RANS and LES, respectively, to identify the turbulent kinetic energy discrepancy. Second, several flow features such as viscosity ratio, wall-distance based Reynolds number, and vortex stretching are calculated from the mean flow properties of RANS. Then the discrepancy is regressed on these flow features using the Random Forests regression algorithm. Finally, the discrepancy of the test flow is predicted using the trained algorithm. The results reveal that a significant discrepancy exists between RANS and LES simulations, and ML algorithm successfully predicts the increased model uncertainties caused by the employment of k-omega SST turbulence model for transitional fluid flows.

Copyright © 2018 by ASME

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