A small-scale UAV propeller optimization by using ant colony algorithm

Khashan, Mohammad K.; Khudhur, Dhamyaa S.; Balla, Hyder H.

doi:10.1063/5.0136596

Cited by 1 publication

(1 citation statement)

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“…For verification and validation of the results, all the results obtained from the algorithm are used in the same algorithm in reference [10]. As noted in Tables 1 and 2 above that when the factor of interest in the thrust coefficient (FITC) is 100%, the genetic algorithm will choose the diameter of the propeller to be 12 inches and at an angle of 7.83 degrees for the APC Free flight type to choose the maximum value for the thrust coefficient as well as for the ant colony algorithm the same diameter but greater angle was chosen for the type of GWS fan Direct drive.…”

Section: Resultsmentioning

confidence: 99%

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

Khashan¹,

Khudhur²,

Balla³

2023

QJES

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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.

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Section: Resultsmentioning

confidence: 99%