Multi-objective drone propeller optimization using genetic algorithms (GA) for enhanced thrust and silent operation

Prestige Institute of Engineering Management and Research, Indore, M.P., 452010, India

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Abstract

Optimizing drone propeller performance is essential for the enhancing thrust generation and reducing the acoustic noise under real world operation. In this study, we use multiple optimization techniques like Genetic Algorithm (GA), Particle Swam Optimization (PSO) and Simulated Annealing (SA) to determine the optimal geometric parameters for the drone propellers. The optimization process is designed based on the drone's weight and the RPM generated by the motor, to improve its aerodynamic performance. The approach focuses on the optimization of key geometric features such as propeller radius, chord length, pitch, twist, and sweep and other geometric characteristics like maximum thickness of the propeller, cross-section, centre of gravity in Y and Z axis, while keeping the propeller material as constant. By comparing the optimal parameters from different optimization methods, we show that while each technique effectively identifies high-performance configurations, the complementary use of these methods enhances the validation of our optimized parameters. Although the current study does not include material optimization, this aspect is a promising direction for future research.