AI-driven unmanned aerial system conceptual design with configuration selection

This paper presents an intelligent conceptual design framework for the configuration selection of aerial vehicles. In this approach, the quantitative data is brought to the earliest stage of design utilizing AI-driven analysis models and it allows to choose the most suitable one among the possible configurations. Thanks to the design optimization cycle, the initial dimensions of the main components such as the wing, tail and fuselage are more accurately provided for later design activities. At the same time, the generated structure provides a more appropriate design point selection thanks to the feedback loop in design iteration. Thus, while reducing the design cost, a significant time advantage is also provided in the design process. The paper presents a generic use case based on a high-performance combat UAV design study to demonstrate the abilities of the proposed model.