A sequential algorithm for decoupling the multidisciplinary constraints of hypersonic vehicle structural optimization design in a thermal environment

• Published in: Structural and multidisciplinary optimization 2023-08, Vol.66 (8), p.185, Article 185
• Main Authors: Wang, Xiaojun, Xu, Yusheng, Liu, Peiyan, Wang, Lei, Zeng, Linxi
• Format: Article
• Language: English
• Subjects: Algorithms; Computational Mathematics and Numerical Analysis; Cost analysis; Coupling; Decoupling; Design optimization; Engineering; Engineering Design; Flutter; Hypersonic vehicles; Optimization algorithms; Research Paper; Structural strength; Theoretical and Applied Mechanics; Thermal environments; Vibration
• ISSN: 1615-147X; 1615-1488
• DOI: 10.1007/s00158-023-03635-4

Aiming at the multidisciplinary coupling problem in the design of hypersonic vehicles, especially a series of effects caused by the coupling of aerodynamic heat and structural strength, the development of a multidisciplinary coupling analysis and optimization algorithm has become a key issue in the design process of hypersonic vehicles. To reduce the huge computational cost of multidisciplinary coupling analysis in the process of design optimization, the multifield coupling relationship is analyzed, and a simplified multifield coupling analysis process for hypersonic vehicles is proposed. To solve the problem of the multidisciplinary multiconstrained optimization solution being inefficient and difficult to converge, an optimization algorithm based on a sequential solution for the coupling of the thermal structure, thermal mode, and thermal flutter of hypersonic vehicles is proposed. This algorithm considers the interdependence of multiple disciplines but decouples their constraints through a three-step process. Firstly, the main optimization of the thermal structure is performed. Secondly, the suboptimization of thermal mode and thermal flutter is carried out. Finally, the algorithm returns to the main optimization. Through this three-step nested optimization process, the algorithm iterates until the optimal design point is reached. Numerical examples show that the algorithm can improve optimization efficiency under the premise of ensuring the accuracy of multidisciplinary optimization.