Adaptive space transformation: An invariant based method for predicting aerodynamic coefficients of hypersonic vehicles
文献类型：期刊论文
| 作者 | Luo CT(罗长童) ; Hu ZM(胡宗民); Zhang SL; Jiang ZL(姜宗林)
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| 刊名 | Engineering Applications of Artificial Intelligence
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| 出版日期 | 2015-11 |
| 卷号 | 46页码:93-103 |
| 通讯作者邮箱 | luo@imech.ac.cn ; huzm@imech.ac.cn ; zhang@na.cse.nagoya-u.ac.jp ; zljiang@imech.ac.cn |
| 关键词 | Aerodynamic coefficient Data correlation Scaling parameter Genetic programming Invariant |
| ISSN号 | 0952-1976 |
| 产权排序 | [Luo, Changtong; Hu, Zongmin; Jiang, Zonglin] Chinese Acad Sci, Inst Mech, Beijing 100190, Peoples R China; [Zhang, Shao-Liang] Nagoya Univ, Dept Computat Sci & Engn, Nagoya, Aichi 4648603, Japan |
| 通讯作者 | Luo, CT (reprint author), Chinese Acad Sci, Inst Mech, Beijing 100190, Peoples R China. |
| 中文摘要 | When developing a new hypersonic vehicle, thousands of wind tunnel tests to study its aerodynamic performance are needed. Due to limitations of experimental facilities and/or cost budget, only a part of flight parameters could be replicated. The point to predict might locate outside the convex hull of sample points. This makes it necessary but difficult to predict its aerodynamic coefficients under flight conditions so as to make the vehicle under control and be optimized. Approximation based methods including regression, nonlinear fit, artificial neural network, and support vector machine could predict well within the convex hull (interpolation). But the prediction performance will degenerate very fast as the new point gets away from the convex hull (extrapolation). In this paper, we suggest regarding the prediction not just a mathematical extrapolation, but a mathematics-assisted physical problem, and propose a supervised self-learning scheme, adaptive space transformation (AST), for the prediction. AST tries to automatically detect an underlying invariant relation with the known data under the supervision of physicists. Once the invariant is detected, it will be used for prediction. The result should be valid provided that the physical condition has not essentially changed. The study indicates that AST can predict the aerodynamic coefficient reliably, and is also a promising method for other extrapolation related predictions. (C) 2015 Elsevier Ltd. All rights reserved. |
| 分类号 | 一类 |
| 类目[WOS] | Automation & Control Systems ; Computer Science, Artificial Intelligence ; Engineering, Multidisciplinary ; Engineering, Electrical & Electronic |
| 研究领域[WOS] | Automation & Control Systems ; Computer Science ; Engineering |
| 关键词[WOS] | EVOLUTION |
| 收录类别 | SCI ; EI |
| 原文出处 | http://dx.doi.org/10.1016/j.engappai.2015.09.001 |
| 语种 | 英语 |
| WOS记录号 | WOS:000365369100009 |
| 源URL | [http://dspace.imech.ac.cn/handle/311007/58342]  |
| 专题 | 力学研究所_高温气体动力学国家重点实验室 |
| 推荐引用方式 GB/T 7714 | Luo CT,Hu ZM,Zhang SL,et al. Adaptive space transformation: An invariant based method for predicting aerodynamic coefficients of hypersonic vehicles[J]. Engineering Applications of Artificial Intelligence,2015,46:93-103. |
| APA | Luo CT,Hu ZM,Zhang SL,&Jiang ZL.(2015).Adaptive space transformation: An invariant based method for predicting aerodynamic coefficients of hypersonic vehicles.Engineering Applications of Artificial Intelligence,46,93-103. |
| MLA | Luo CT,et al."Adaptive space transformation: An invariant based method for predicting aerodynamic coefficients of hypersonic vehicles".Engineering Applications of Artificial Intelligence 46(2015):93-103. |
入库方式： OAI收割
来源：力学研究所
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