Mission-centric design optimisation of unmanned aerial vehicles for enhanced operational effectiveness

Abstract This study introduces a mission-centric design optimisation framework for unmanned aerial vehicles (UAVs) to enhance mission performance across diverse operational scenarios. The proposed framework integrates multidisciplinary design optimisation with a wargaming-based simulation environment and leverages deep neural network-based surrogate models to balance key performance metrics, such as aerodynamic efficiency, radar cross section, structural weight and payload capacity. By incorporating automated task assignment, path planning and a probabilistic combat model, the framework evaluates UAV configurations in multi-domain, multi-asset scenarios. The algorithm identifies optimal solutions that maximise mission success while managing trade-offs among survivability, lethality and cost. Simulation results illustrate the framework’s functionality through representative mission scenarios, highlighting how design variables can influence operational effectiveness relative to baseline configurations. Furthermore, the modular design approach enables rapid UAV reconfiguration for evolving mission needs, offering scalable and adaptable solutions. These findings highlight the importance of integrating mission simulation tools with advanced optimisation techniques to address challenges in dynamic, high-threat environments, providing a robust methodology for UAV and fleet design.