Kareem Khashan, M., S. Khudhur, D., H. Balla, H. (2023). Comparison between the two methods of optimization: Genetic algorithm (GA) and ant colony algorithm (ACO) for the propulsion system of UAV. Alustath, 16(2), 92-95. doi: 10.30772/qjes.v16i2.861
Mohammed Kareem Khashan; Dhamyaa S. Khudhur; Hyder H. Balla. "Comparison between the two methods of optimization: Genetic algorithm (GA) and ant colony algorithm (ACO) for the propulsion system of UAV". Alustath, 16, 2, 2023, 92-95. doi: 10.30772/qjes.v16i2.861
Kareem Khashan, M., S. Khudhur, D., H. Balla, H. (2023). 'Comparison between the two methods of optimization: Genetic algorithm (GA) and ant colony algorithm (ACO) for the propulsion system of UAV', Alustath, 16(2), pp. 92-95. doi: 10.30772/qjes.v16i2.861
Kareem Khashan, M., S. Khudhur, D., H. Balla, H. Comparison between the two methods of optimization: Genetic algorithm (GA) and ant colony algorithm (ACO) for the propulsion system of UAV. Alustath, 2023; 16(2): 92-95. doi: 10.30772/qjes.v16i2.861

Comparison between the two methods of optimization: Genetic algorithm (GA) and ant colony algorithm (ACO) for the propulsion system of UAV

Al-Qadisiyah Journal for Engineering Sciences
Volume 16, Issue 2, June 2023, Pages 92-95    PDF (425.16 K)
Document Type: Research Paper
DOI: 10.30772/qjes.v16i2.861
Authors
Mohammed Kareem Khashan1; Dhamyaa S. Khudhur2; Hyder H. Balla* 1
1Department of Aeronautical Technologies, Najaf Technical Institute, Al-Furat Al-Awsat Technical University, Al-Najaf, Iraq
2Mechanical Engineering Department, College of Engineering, Mustansiriyah University, Baghdad, Iraq.
Abstract
A propeller generates lift in the direction of revolution, similar to a revolving wing. Many previous propeller optimization techniques exist; nevertheless, they often find the optimal thrust coefficient at a constant power coefficient and vice versa. Using two types of algorithms, the genetic algorithm (GA), and the ant colony algorithm (ACO), and comparing with each other, this study will discover the optimal value of the thrust coefficient and the power coefficient combined to obtain the optimum value of the thrust and the lowest value of the power at the same time. A Simple Blade Element Theory Blade served as the foundation for all assumptions. This article examined over 80 various designs, brands, and types of propellers in a 2-blade configuration with diameters ranging from 2.5 to 19 inches and varying pitch values. The data for the baseline propeller was obtained from the UIUC Propeller Database. The inputs for the optimization are the propeller type, diameter, pitch angle, rotational speed, thrust coefficient, and power coefficient. The results show that by determining the factor of interest in the thrust coefficient (FITC), the algorithm can find the optimal propeller specifications. When the (FITC) is 100%, the algorithm will ignore the effect of the power coefficient and vice versa. In the instance (FITC) is 100 percent, the genetic algorithm performed much better than the ant colony algorithm (ACO). But the Ant colony algorithm is more accurate than the genetic algorithm.
Keywords
UAVs; Genetic; Optimization; Propeller; Ant-Colony; Algorithm
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