A rapidly structured aircraft concept design method based on generative artificial intelligence
Yao Tong, M. X. Luo, Simiao Ren, Zheng Zhang, Chunqing Xing, Ziliang Du
Abstract
Aircraft conceptual design is a critical step in the development and research of aircraft, involving complex processes and multiple disciplines. Improving the efficiency of aircraft conceptual design while ensuring quality is an important challenge. Intelligent technologies such as neural networks have played significant roles in areas like aerodynamics and structural analysis. However, due to issues such as high data demands and difficulties in transfer learning, their application in the conceptual design phase has been limited. The rise of generative artificial intelligence, exemplified by Large Language Model (LLM), offers a new approach to this problem. Therefore, this study proposes a methodology for generating aircraft conceptual design solutions based on LLMs and develops a prototype system. First, four of the current best-performing general-purpose LLMs are selected for deployment as foundational models. Then, based on the general prompt framework of LLMs, schema for aircraft conceptual design solutions, and real-world design cases, task prompts for generating aircraft conceptual design solutions are crafted, resulting in three types of prompts: Full-Instruction, 1-Shot, and 5-Shot. Finally, the prototype system is utilized to design conceptual solutions, and the model-generated solutions are compared with those designed by engineers from both objective and subjective perspectives. The experimental results indicate that LLMs demonstrate conceptual design capabilities comparable to those of engineers, exhibiting strong generalization ability and potential for innovative design.