陈海昕, 邓凯文, 李润泽
收稿日期:
2018-06-26
修回日期:
2018-07-17
出版日期:
2019-01-15
发布日期:
2018-08-16
通讯作者:
陈海昕
E-mail:chenhaixin@tsinghua.edu.cn
基金资助:
CHEN Haixin, DENG Kaiwen, LI Runze
Received:
2018-06-26
Revised:
2018-07-17
Online:
2019-01-15
Published:
2018-08-16
Supported by:
摘要: 近年来优化设计在气动设计中发挥了越来越多的作用,但实用性和效率是制约其发挥作用的两大障碍。在大型客机超临界机翼设计中,通过"人在回路"(依靠人的经验在优化进行过程中实施必要干预)等努力,取得了较好的效果,机器学习技术逐步得到发展。提出了利用机器学习技术模拟人在优化过程中的合理行为和作用机制,以深层次利用信息和知识,改善优化的实用性和效率。梳理了机器学习技术在气动优化中应用的发展脉络,并结合工作实践介绍了机器学习在优化设计中的典型应用。进一步探讨了深度学习在气动优化中的可能应用方式。
中图分类号:
陈海昕, 邓凯文, 李润泽. 机器学习技术在气动优化中的应用[J]. 航空学报, 2019, 40(1): 522480-522480.
CHEN Haixin, DENG Kaiwen, LI Runze. Utilization of machine learning technology in aerodynamic optimization[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2019, 40(1): 522480-522480.
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