Parameter-correlation study on shock-shock interaction using a machine learning method

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@Article{PENG:2020:AST,

• author = "J. Peng and C. T. Luo and Z. J. Han and Z. M. Hu and G. L. Han and Z. L. Jiang",
• title = "Parameter-correlation study on shock-shock interaction using a machine learning method",
• journal = "Aerospace Science and Technology",
• volume = "107",
• pages = "106247",
• year = "2020",
• ISSN = "1270-9638",
• DOI = "doi:10.1016/j.ast.2020.106247",
• URL = "https://www.sciencedirect.com/science/article/pii/S1270963820309299",
• keywords = "genetic algorithms, genetic programming, Shock-shock interaction, Machine learning, Hypersonic flow, Impinging jet, Triple point",
• abstract = "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 a type IV and type IV a type III shock-shock interaction can be obtained by the machine learning based method",

}

Genetic Programming entries for J Peng C T Luo Z J Han Z M Hu G L Han Z L Jiang

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