Parameter-correlation study on shock–shock interaction using a machine learning method
| Autor: | Guilai Han, Z.J. Han, Zonglin Jiang, Changtong Luo, Jun Peng, Zongmin Hu |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Physics
0209 industrial biotechnology Jet (fluid) Work (thermodynamics) Scale (ratio) business.industry Astrophysics::High Energy Astrophysical Phenomena Aerospace Engineering 02 engineering and technology Type (model theory) Machine learning computer.software_genre 01 natural sciences 010305 fluids & plasmas Shock (mechanics) Set (abstract data type) 020901 industrial engineering & automation Flow (mathematics) 0103 physical sciences Supersonic speed Artificial intelligence business computer Astrophysics::Galaxy Astrophysics |
| Zdroj: | Aerospace Science and Technology. 107:106247 |
| ISSN: | 1270-9638 |
| DOI: | 10.1016/j.ast.2020.106247 |
| Popis: | To predict the maximum heating load induced by shock–shock interaction more reliably and accurately, the geometrical scale of the overall wave configuration of shock–shock interaction is very useful. However, it is hard to be solved with traditional shock theory due to its complexity. The results of numerical and experimental studies are case-by-case. Concise formulas correlating the geometrical scales of shock–shock interaction with the given flow parameters are desired but still unavailable. In the present work, a set of correlative formulas for the triple-points' coordinates of type IVa, IV, and III shock–shock interaction are derived by multilevel block building algorithm, a functional machine learning method. The key flow structure of shock–shock interaction, i.e., the supersonic impinging jet, can be determined with the help of shock theories and the formulas. In addition, the transition criteria respectively for the overall wave configuration transitions of type IVa ↔ type IV and type IV ↔ type III shock–shock interaction can be obtained by the machine learning based method. |
| Databáze: | OpenAIRE |
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