An integrated framework for multidisciplinary design optimization of unmanned aerial vehicles

Abstract

The growing demand for high-performance Unmanned Aerial Vehicles (UAVs) in civil, commercial, and defense applications has increased the need for advanced engineering methodologies capable of addressing complex and often conflicting design requirements. Conventional sequential design approaches generally optimize individual subsystems independently, frequently overlooking the interactions between aerodynamics, structures, propulsion, flight dynamics, control systems, and mission performance. These limitations may result in increased development costs, longer design cycles, and reduced overall system efficiency. This paper proposes an integrated framework for Multidisciplinary Design Optimization (MDO) of UAVs that combines mission analysis, conceptual aircraft design, aerodynamic optimization, structural analysis, propulsion system integration, flight performance evaluation, and system-level optimization within a unified engineering process. The proposed framework facilitates continuous information exchange among engineering disciplines, enabling designers to identify globally optimal solutions while satisfying operational, structural, and aerodynamic constraints. The framework incorporates modern computational tools, including Computational Fluid Dynamics (CFD), Finite Element Analysis (FEA), numerical optimization algorithms, and simulation-based performance evaluation techniques. Furthermore, the study discusses the potential integration of artificial intelligence and machine learning algorithms to accelerate design optimization and improve engineering decision-making during the early stages of UAV development. The proposed integrated framework provides a systematic methodology for improving aircraft efficiency, reducing structural weight, minimizing energy consumption, shortening product development time, and enhancing mission effectiveness. The presented approach establishes a foundation for future research on intelligent multidisciplinary optimization of next-generation unmanned aerial systems.