Inverse airfoil design algorithm based on multi-output least-squares support vector regression machines

Xinqi Zhu, Zhenghong Gao

科研成果: 书/报告/会议事项章节会议稿件同行评审

1 引用 (Scopus)

摘要

Inverse airfoil design algorithm can obtain the airfoil geometry according to the target pressure coefficient (Cp) distribution. Recently, the rapid development of machine learning method provides new idea to solve engineering problem. Multi-output least-squares support vector regression machines (MLS-SVR) is a multi-output regression machine learning method which can make prediction for several outputs simultaneously through learning a mapping from a multivariate input feature space to a multivariate output space. In this paper, MLS-SVR is used to learn the mapping from the Cp distribution to the geometry, which can be seen as a multi-output regression problem. Through iteratively adding the predicted airfoil geometry and its pressure coefficient distribution into the sample database, the precision of MLS-SVR to predict the right airfoil geometry corresponding to the target Cp distribution is improved. A low speed, transonic and supersonic airfoil inverse design problem are used to validate the efficiency of the proposed algorithm, and the experimental results show that the proposed algorithm can save 34.1% and 58.6% CFD evaluations for low speed and transonic cases respectively to obtain satisfactory airfoil.

源语言英语
主期刊名The Proceedings of the Asia-Pacific International Symposium on Aerospace Technology, APISAT 2018
编辑Xinguo Zhang
出版商Springer Verlag
1412-1426
页数15
ISBN(印刷版)9789811333040
DOI
出版状态已出版 - 2019
活动Asia-Pacific International Symposium on Aerospace Technology, APISAT 2018 - Chengdu, 中国
期限: 16 10月 201818 10月 2018

出版系列

姓名Lecture Notes in Electrical Engineering
459
ISSN(印刷版)1876-1100
ISSN(电子版)1876-1119

会议

会议Asia-Pacific International Symposium on Aerospace Technology, APISAT 2018
国家/地区中国
Chengdu
时期16/10/1818/10/18

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