Multidisciplinary Design Optimization of Flight Vehicles : Theory and Applications

This book systematically introduced the theory and application of Multidisciplinary Design Optimization (MDO) of flight vehicles. The MDO theory part includes the background, theoretical fundamentals, MDO-oriented modeling, traditional machine learning methods and deep learning-based approximation, sequential approximation modeling, sensitivity analysis, optimization search strategies, MDO optimization procedure, and uncertainty-based MDO. The MDO application covers both subsystem and system examples, including MDO of satellite inner instrument layout design, structural topology optimization, satellite system design, on-orbit servicing task optimization, and MDO of missile and aircraft. This book is characterized by the novelty and practicality with abundant contents, and it is written in an easy way for new learners. It is used by researchers and engineering designers who are engaged in design of flight vehicles or other complex industrial systems. And it is also used as textbook for graduate or undergraduate students majoring in flight vehicle design or related disciplines.

Wen Yao is a professor of Defense Innovation Institute, Chinese Academy of Military Science, Ph.D. Her main research interests include spacecraft system engineering, uncertainty quantification (UQ), multidisciplinary design optimization (MDO), etc. She has published more than 60 papers in top journals and conferences in the fields of spaceflight, structural optimization, and reliability, including 26 SCI papers, 2 of which are listed in the top 1% and 10% of highly cited papers in engineering ESI, respectively. The doctoral dissertation was awarded as the first national excellent doctoral dissertation in 2016. Currently, she is a member of the Chinese Society of Astronautics (CSA) and the Chinese Society of Aeronautics and Astronautics (CSAA). She has won the first prize of the Natural Science Award of Hunan Province.

Yong Zhao is a professor of College of Aerospace Science and Engineering, National University of Defense Technology, Ph.D. His main research interests include spacecraft system engineering, multidisciplinary design optimization (MDO), etc. Currently, he is a member of the Chinese Society of Astronautics (CSA) and the Chinese Society of Aeronautics and Astronautics (CSAA). He has won the second prize of the National Science and Technology Progress Award, the second prize of the National Technology Invention Award, the first Prize of the National Defense Technology Invention, and the excellent award of the Chinese Patent, etc.

Xiaohu Zheng is an assistant professor of Defense Innovation Institute, Chinese Academy of Military Science, Ph.D. His main research interests include reliability engineering, uncertainty quantification (UQ), multidisciplinary design optimization (MDO), etc. He has published more than 30 papers in top journals and conferences in the fields of spaceflight, uncertainty quantification, reliability analysis, and digital calculation, including 16 SCI papers and a best paper award. As the first author and corresponding author, he has published 7 papers in the journal Reliability Engineering & System Safety and participated in 8 related projects about uncertainty quantification, spacecraft reliability design, and physical field digital calculation.

Ning Wang is an assistant professor of Defense Innovation Institute, Chinese Academy of Military Science, Ph.D. His main research interests include model validation, uncertainty quantification, and optical-mechanical-thermal integration analysis, etc. He is committed to applying advanced machine learning methods to the traditional engineering development process to improve efficiency and quality. As the first author, he has published 4 SCI papers in internationally renowned journals such as IEEE Transactions on Fuzzy Systems and Computer Methods in Applied Mechanics and Engineering, obtained 1 invention patent authorization, and participated in 4 related projects about model validation and physical field digital twin.