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Development and Validation of a Machine Learned Turbulence Model

Author

Listed:
  • Shanti Bhushan

    (Department of Mechanical Engineering, Mississippi State University, Starkville, MS 39762, USA
    Center for Advanced Vehicular Systems, Mississippi State University, Starkville, MS 39762, USA)

  • Greg W. Burgreen

    (Center for Advanced Vehicular Systems, Mississippi State University, Starkville, MS 39762, USA)

  • Wesley Brewer

    (DoD High Performance Computing Modernization Program PET/GDIT, Vicksburg, MS 39180, USA)

  • Ian D. Dettwiller

    (Engineer Research and Development Center (ERDC), Vicksburg, MS 39180, USA)

Abstract

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.

Suggested Citation

  • Shanti Bhushan & Greg W. Burgreen & Wesley Brewer & Ian D. Dettwiller, 2021. "Development and Validation of a Machine Learned Turbulence Model," Energies, MDPI, vol. 14(5), pages 1-34, March.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:5:p:1465-:d:512674
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    References listed on IDEAS

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    1. Chao Jiang & Junyi Mi & Shujin Laima & Hui Li, 2020. "A Novel Algebraic Stress Model with Machine-Learning-Assisted Parameterization," Energies, MDPI, vol. 13(1), pages 1-21, January.
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    Cited by:

    1. Fábio Antônio do Nascimento Setúbal & Sérgio de Souza Custódio Filho & Newton Sure Soeiro & Alexandre Luiz Amarante Mesquita & Marcus Vinicius Alves Nunes, 2022. "Force Identification from Vibration Data by Response Surface and Random Forest Regression Algorithms," Energies, MDPI, vol. 15(10), pages 1-15, May.
    2. Ricardo Vinuesa & Soledad Le Clainche, 2022. "Machine-Learning Methods for Complex Flows," Energies, MDPI, vol. 15(4), pages 1-5, February.

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