Development and Validation of a Machine Learned Turbulence Model

Shanti Bhushan, Greg W. Burgreen, Wesley Brewer and Ian D. Dettwiller
Additional contact information
Shanti Bhushan: Department of Mechanical Engineering, 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

Energies, 2021, vol. 14, issue 5, 1-34

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.

Keywords: turbulence modeling; machine learning; DNS (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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