A methodology to integrate reliability into the conceptual design of safety-critical multirotor unmanned aerial vehicles

This article introduces a new conceptual design methodology to evaluate and explore underactuated electric multirotor unmanned aerial vehicle (UAV) designs for safety-critical applications. A case study focusing on medical transport in an urban environment demonstrates the methodology's effectiveness. The current state of the art does not provide conceptual design methodologies that integrate reliability considerations for multirotor UAVs. The proposed methodology addresses this gap by developing systematic reliability calculation and introducing sizing based on failure cases. For this purpose, controllability and reliability analysis methods are developed and linked to an analytical sizing methodology. The controllability analysis is based on the available control authority index adapted for failure case assessment and reliability analysis. The link between the controllability analysis and the sizing methodology is achieved by introducing failure case sizing factors. The sizing relies on a modern analytical database-free methodology with multidisciplinary design optimization for design customization and computational efficiency. This methodology is developed with new design models to cover failure cases in forward flights. When applied to the case study, the methodology efficiently evaluates and compares five concepts and indicates that only two comply with both the safety and reliability requirements and mission specifications (payload and range). More specifically, the methodology shows the major impact of reliability considerations on the case study with sizing factors that almost double or triple the required rotor thrusts depending on the design. This methodology is applicable to challenging future multirotor UAV applications that require to demonstrate high safety levels and redundancies, such as urban air taxis, flying ambulances, and search and rescue and medical equipment transport. (C) 2022 Elsevier Masson SAS. All rights reserved.