Wesley Brewer
Research Subjects
Research Interests
Wesley Brewer has a total of 3 co-authors. Here are the top co-authors by number of shared publications:
- Greg W. Burgreen
Center for Advanced Vehicular Systems, Mississippi State University, Starkville, 39759, MS, USA
1 shared publication - Ian D. Dettwiller
Information Technology Laboratory
1 shared publication - Shanti Bhushan
Michael W. Hall School of Mechanical Engineering, Mississippi State University 2 , Starkville, Mississippi 39759
1 shared publication
Latest Publications
- Open Access
Development and Validation of a Machine Learned Turbulence Model
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.
Open AccessDevelopment and Validation of a Machine Learned Turbulence Model
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.