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Tracking Turbulent Coherent Structures by Means of Neural Networks

Development and Validation of a Machine Learned Turbulence Model †

Department of Mechanical Engineering, Mississippi State University, Starkville, MS 39762, USA
Center for Advanced Vehicular Systems, Mississippi State University, Starkville, MS 39762, USA
DoD High Performance Computing Modernization Program PET/GDIT, Vicksburg, MS 39180, USA
Engineer Research and Development Center (ERDC), Vicksburg, MS 39180, USA
Author to whom correspondence should be addressed.
DOD DISTRIBUTION STATEMENT A. Approved for Public Release: Distribution Unlimited.
Academic Editor: Ricardo Vinuesa
Energies 2021, 14(5), 1465;
Received: 28 January 2021 / Revised: 23 February 2021 / Accepted: 25 February 2021 / Published: 8 March 2021
(This article belongs to the Special Issue Machine-Learning Methods for Complex Flows)
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. View Full-Text
Keywords: turbulence modeling; machine learning; DNS turbulence modeling; machine learning; DNS
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MDPI and ACS Style

Bhushan, S.; Burgreen, G.W.; Brewer, W.; Dettwiller, I.D. Development and Validation of a Machine Learned Turbulence Model. Energies 2021, 14, 1465.

AMA Style

Bhushan S, Burgreen GW, Brewer W, Dettwiller ID. Development and Validation of a Machine Learned Turbulence Model. Energies. 2021; 14(5):1465.

Chicago/Turabian Style

Bhushan, Shanti, Greg W. Burgreen, Wesley Brewer, and Ian D. Dettwiller. 2021. "Development and Validation of a Machine Learned Turbulence Model" Energies 14, no. 5: 1465.

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