DOI : https://doi.org/10.18517/ijaseit.11.6.11415

Improving the Flight Endurance of a Separate-Lift-and-Thrust Hybrid through Gaussian Process Optimization

Francis Gregory Ng (1), Alvin Chua (2)
(1) Mechanical Engineering Department, De La Salle University-Manila, 2401 Taft Avenue, Manila, 1004, Philippines
(2) Mechanical Engineering Department, De La Salle University-Manila, 2401 Taft Avenue, Manila, 1004, Philippines
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How to cite (IJASEIT) :
Ng, Francis Gregory, and Alvin Chua. “Improving the Flight Endurance of a Separate-Lift-and-Thrust Hybrid through Gaussian Process Optimization”. International Journal on Advanced Science, Engineering and Information Technology, vol. 11, no. 6, Dec. 2021, pp. 2287-92, doi:10.18517/ijaseit.11.6.11415.
Citation Format :
A separate-lift-and-thrust hybrid is a modified fixed-wing drone which includes quadcopter rotors. This results in the combined capability of forwarding flight as well as vertical take-off and landing (VTOL), making it a low-cost method that can deliver substantial gains in utility. Though this is a strong point compared to other types of VTOL drones, the hybrid design may incur a significant trade-off because added weight and drag can severely reduce the drone's flight endurance. This study attempts to mitigate the impact by improving the configuration of the selection and positioning parameters. Since drag estimations are costly, a Gaussian process optimization method was performed, as it is economical with respect to the required number of iterations. A set of arbitrarily selected components was prepared for use with the optimization method, recording the relevant performance data and constructing the CAD models of the components for use in simulations. The optimization method was able to increase the estimated flight endurance to 27.99 minutes, a significant improvement compared to a set of random configurations, which only yielded 9.54 minutes at best. The respectable result was obtained even though difficulties were experienced regarding the infeasible regions that arise from the many constraints. Future implementation of this optimization approach can be further improved. It may be worthwhile to utilize a low-fidelity model from the base fixed-wing drone simulations, in contrast with using an initial zero mean for the prior of the Gaussian process.

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