This paper presents a practical, end-to-end approach to improving the flight endurance of a one-meter wingspan blended-wing-body (BWB) unmanned aerial vehicle (UAV) powered by a low-power propulsion system. It aims to increase aerodynamic efficiency and confirm structural viability for small-motor, energy-limited missions. The study combined high-fidelity computational fluid dynamics (CFD) to evaluate aerodynamic behavior with machine learning surrogate modelling and Bayesian optimization to search the design space efficiently. Key geometric variables i.e. wing planform, airfoil choice, winglet geometry and surface twist were evaluated using a Gaussian Process Regression surrogate trained on CFD results, enabling rapid prediction of aerodynamic performance across many candidate configurations. Optimized surface geometries increased the lift-to-drag ratio significantly, reducing required propulsion power and extending predicted endurance. To ensure the design could withstand operational loads, the airframe was sized for manufacture using a twill weave 3K 200gsm carbon fiber/epoxy composite and evaluated with finite element analysis (FEA); results showed the reinforced structure met safety and performance criteria for expected flight loads. A prototype was fabricated and subjected to wind tunnel testing, which demonstrated longer-duration flights under lower wind speed, as to propulsion, compared with baseline configurations. Together, the aerodynamic and structural optimizations produced a lightweight, manufacturable BWB UAV suitable for energy-efficient tasks such as long-range logistics, surveillance, and emergency response where low propulsion power is a constraint. The methods and results offer a repeatable workflow for integrating CFD, surrogate modelling, Bayesian design search, composite design, and physical testing to develop low-power UAVs, and provide practical guidance for designers and engineers working on similar low-energy aircraft systems.
Naseem et al. (Tue,) studied this question.