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Adaptive space transformation: An invariant based method for predicting aerodynamic coefficients of hypersonic vehicles
Luo CT(罗长童); Hu ZM(胡宗民); Zhang SL; Jiang ZL(姜宗林); Luo, CT (reprint author), Chinese Acad Sci, Inst Mech, Beijing 100190, Peoples R China.
Source PublicationEngineering Applications of Artificial Intelligence
2015-11
Volume46Pages:93-103
ISSN0952-1976
AbstractWhen 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.
KeywordAerodynamic Coefficient Data Correlation Scaling Parameter Genetic Programming Invariant
DOI10.1016/j.engappai.2015.09.001
URL查看原文
Indexed BySCI ; EI
Language英语
WOS IDWOS:000365369100009
WOS KeywordEVOLUTION
WOS Research AreaAutomation & Control Systems ; Computer Science ; Engineering
WOS SubjectAutomation & Control Systems ; Computer Science, Artificial Intelligence ; Engineering, Multidisciplinary ; Engineering, Electrical & Electronic
Funding OrganizationThis research has been supported by Innovation Grant of Chinese Academy of Sciences and the National Natural Science Foundation of China (Grant nos. 90916028 and 11532014).
DepartmentLHD激波与爆轰物理
Classification一类
Citation statistics
Cited Times:4[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://dspace.imech.ac.cn/handle/311007/58342
Collection高温气体动力学国家重点实验室
Corresponding AuthorLuo, CT (reprint author), Chinese Acad Sci, Inst Mech, Beijing 100190, Peoples R China.
Recommended Citation
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,&Luo, CT .(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.
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