4.5 Article

Development and Validation of a Machine Learned Turbulence Model

Journal

ENERGIES
Volume 14, Issue 5, Pages -

Publisher

MDPI
DOI: 10.3390/en14051465

Keywords

turbulence modeling; machine learning; DNS

Categories

Funding

  1. Engineering Research & Development Center [W912HZ-17-2-0014]
  2. Department of Defense (DoD) High Performance Computing Modernization Program (HPCMP) under User Productivity Enhancement, Technology Transfer, and Training (PET) [47QFSA18K0111, ID04180146]

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The study shows that a machine learned turbulence model can provide grid convergent and smooth solutions, and converge to a correct solution from ill-posed flow conditions. The accuracy of the model relies on the choice of flow variables and training approach, with data clustering identified as a useful tool to avoid model skewness.
A stand-alone machine learned turbulence model is developed and applied for the solution of steady and unsteady boundary layer equations, and issues and constraints associated with the model are investigated. The results demonstrate that an accurately trained machine learned model can provide grid convergent, smooth solutions, work in extrapolation mode, and converge to a correct solution from ill-posed flow conditions. The accuracy of the machine learned response surface depends on the choice of flow variables, and training approach to minimize the overlap in the datasets. For the former, grouping flow variables into a problem relevant parameter for input features is desirable. For the latter, incorporation of physics-based constraints during training is helpful. Data clustering is also identified to be a useful tool as it avoids skewness of the model towards a dominant flow feature.

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