%A CHEN Haixin, DENG Kaiwen, LI Runze %T Utilization of machine learning technology in aerodynamic optimization %0 Journal Article %D 2019 %J Acta Aeronautica et Astronautica Sinica %R 10.7527/S1000-6893.2018.22480 %P 522480-522480 %V 40 %N 1 %U {https://hkxb.buaa.edu.cn/CN/abstract/article_17270.shtml} %8 2019-01-15 %X While design optimization has been widely used in the aerodynamic design in the last decades, its incapability to efficiently obtain applicable solutions has seriously limited its utilization potential. The proposal and utilization of so-called "man-in-loop" methodology (to use expert's experience to interfere the optimization process) in supercritical wing design of commercial airliners has shown impressive performance improvement. Regarding the rapid development of machine learning technology in recent decades and to improve both the efficiency and applicability of the current aerodynamic optimization methods, this article proposes to leverage machine learning technology to imitate and substitute experts' rational interference inside the optimization loop to automatically further utilize essential information generated during optimization. For such purposes, this article reviews the history of the development and utilization of machine learning technology related to aerodynamic optimization and presents several typical cases based on authors' engineering practices. Furthermore, this article discusses possible utilization forms of deep learning technology in aerodynamic optimization.