Abstract
A stand-alone machine learned turbulence model is applied for the solution of integral boundary layer equations, and issues and constraints associated with the model are discussed. The results demonstrate that grouping flow variables into a problem relevant parameter for input during machine learning is desirable to improve accuracy of the model. Further, the accuracy of the model can be improved significantly by incorporation of physics-based constraints during training. Data driven machine learning training requires trial-and-error approach, shows oscillations in a posteriori predictions, and shows unphysical results when used with arbitrary initial condition, as the query is essentially extrapolations. Physics informed machine learning addresses the above limitations, and is identified to be a viable approach for development of machine learned turbulence model.
| Original language | English |
|---|---|
| Title of host publication | Computational Fluid Dynamics; Micro and Nano Fluid Dynamics |
| Publisher | American Society of Mechanical Engineers (ASME) |
| ISBN (Electronic) | 9780791883730 |
| DOIs | |
| State | Published - 2020 |
| Externally published | Yes |
| Event | ASME 2020 Fluids Engineering Division Summer Meeting, FEDSM 2020, collocated with the ASME 2020 Heat Transfer Summer Conference and the ASME 2020 18th International Conference on Nanochannels, Microchannels, and Minichannels - Virtual, Online Duration: Jul 13 2020 → Jul 15 2020 |
Publication series
| Name | American Society of Mechanical Engineers, Fluids Engineering Division (Publication) FEDSM |
|---|---|
| Volume | 3 |
| ISSN (Print) | 0888-8116 |
Conference
| Conference | ASME 2020 Fluids Engineering Division Summer Meeting, FEDSM 2020, collocated with the ASME 2020 Heat Transfer Summer Conference and the ASME 2020 18th International Conference on Nanochannels, Microchannels, and Minichannels |
|---|---|
| City | Virtual, Online |
| Period | 07/13/20 → 07/15/20 |
Funding
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 U.S. Government.
Keywords
- Machine Learning
- Turbulence Modeling