AI-driven multidisciplinary conceptual design of unmanned aerial vehicles

This paper presents a multidisciplinary conceptual design framework for unmanned aerial vehicles based on artificial intelligence-driven analysis models. This approach leverages AI- driven analysis models that include aerodynamics, structural mass, and radar cross-section predictions to bring quantitative data to the initial design stage, enabling the selection of the most appropriate configuration from various optimized concept designs. Due to the design optimization cycle, the initial dimensions of key components such as the wing, tail, and fuselage are provided more accurately for later design activities. Simultaneously, the generated structure enables more suitable design point selection through the feedback loop within the design iteration. Therefore, in addition to reducing design costs, this approach also offers a substantial time advantage in the overall design process.