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