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.
| Original language | English |
|---|---|
| Article number | 1465 |
| Journal | Energies |
| Volume | 14 |
| Issue number | 5 |
| DOIs | |
| State | Published - Mar 1 2021 |
Funding
Funding: Effort at Mississippi State University was sponsored by the Engineering Research & Development Center under Cooperative Agreement number W912HZ-17-2-0014. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of the Engineering Research & Development Center or the US Government. This material is also based upon work supported by, or in part by, the Department of Defense (DoD) High Performance Computing Modernization Program (HPCMP) under User Productivity Enhancement, Technology Transfer, and Training (PET) contract #47QFSA18K0111, TO# ID04180146. Effort at Mississippi State University was sponsored by the Engineering Research & Development Center under Cooperative Agreement number W912HZ-17-2-0014. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of the Engineering Research & Development Center or the US Government. This material is also based upon work supported by, or in part by, the Department of Defense (DoD) High Performance Computing Modernization Program (HPCMP) under User Productivity Enhancement, Technology Transfer, and Training (PET) contract #47QFSA18K0111, TO# ID04180146.
Keywords
- DNS
- Machine learning
- Turbulence modeling