返回
Parameter-correlation study on shock-shock interaction using a machine learning method
DOI:10.1016/j.ast.2020.106247.png)
摘要
En 中文
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. (c) 2020 Elsevier Masson SAS. All rights reserved.
Keyword:
Shock-shock interaction
Machine learning
Hypersonic flow
Impinging jet
Genetic programming
Triple point
AI总结
对已上传原文的论文进行重点信息的提取,主要内容包括:简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
5.8
论文数:
1.0W
被引数:
3.0W
机构
引用论文
Control of transitional shock wave boundary layer interaction using structurally constrained surface morphing使用结构约束的表面变形控制过渡休克波边界层相互作用
Type III and type IV shock/shock interferences:: theoretical and experimental aspectsIII型和IV型休克/休克干扰:: 理论和实验方面

