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Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
13, Universitatii Street
Suceava - 720229
ROMANIA

Print ISSN: 1582-7445
Online ISSN: 1844-7600
WorldCat: 643243560
doi: 10.4316/AECE


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  4/2018 - 7

Redesign of Morphing UAV for Simultaneous Improvement of Directional Stability and Maximum Lift/Drag Ratio

ARIK, S. See more information about ARIK, S. on SCOPUS See more information about ARIK, S. on IEEExplore See more information about ARIK, S. on Web of Science, TURKMEN, I. See more information about  TURKMEN, I. on SCOPUS See more information about  TURKMEN, I. on SCOPUS See more information about TURKMEN, I. on Web of Science, OKTAY, T. See more information about OKTAY, T. on SCOPUS See more information about OKTAY, T. on SCOPUS See more information about OKTAY, T. on Web of Science
 
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Download PDF pdficon (1,475 KB) | Citation | Downloads: 269 | Views: 927

Author keywords
unmanned aerial vehicles, stability, artificial intelligence, neural networks, optimization

References keywords
neural(9), optimization(8), design(7), technology(5), networks(5), aerospace(5), performance(4), oktay(4), morphing(4), lift(4)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2018-11-30
Volume 18, Issue 4, Year 2018, On page(s): 57 - 62
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2018.04007
Web of Science Accession Number: 000451843400007
SCOPUS ID: 85058778064

Abstract
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This paper presents a novel method based on the artificial intelligence to redesign of morphing Unmanned Aerial Vehicle (UAV) for improvement of index consisting of directional stability and maximum lift/drag (L/D) ratio. In this study, Artificial Neural Network (ANN) based objective function is optimized with Artificial Bee Colony (ABC) algorithm. Firstly, the sweep angle is selected as input parameter and directional stability and maximum L/D ratio are selected as output parameters for ANN. ANN is trained with a small number of data obtained by the computational fluid dynamics method and the trained ANN is used for multiplying these data. Two ABC optimization algorithms with different objective functions are used to improve the index consisting of directional stability and maximum L/D ratio: While the first is used the adjustment of the ANN weights, the second is used the optimization of the ANN based objective function. Simulation results realized with limited data show that although directional stability and maximum L/D ratio have inverse relation, they are optimized equally and simultaneously. Thus, the artificial intelligence techniques provide fast and accurate determination of the optimal aerodynamic shape of UAV without time consuming and complexity caused by theoretical calculations.


References | Cited By  «-- Click to see who has cited this paper

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[2] H. Yeo, W. Johnson, "Performance and Design Investigation of Heavy Lift Tilt-Rotor with Aerodynamic Interference Effects," Journal of Aircraft, vol. 46, no. 4, pp. 1231-1239, 2009.
[CrossRef] [Web of Science Times Cited 25] [SCOPUS Times Cited 39]


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[CrossRef]


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[7] M. H. Djavareshkian, A. Esmaili, "Heuristic optimization of submerged hydrofoil using ANFIS-PSO," Ocean Engineering, vol. 92, pp. 55-63, 2014.
[CrossRef] [Web of Science Times Cited 10] [SCOPUS Times Cited 10]


[8] A. Hacioglu, "Augmenting Genetic Algorithm with Neural Network and Implementation to the Inverse Airfoil Design," 10th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference, Multidisciplinary Analysis Optimization Conferences, 2004.
[CrossRef]


[9] A. Hacioglu, "Fast evolutionary algorithm for airfoil design via neural network," AIAA Journal, vol. 45, no. 9, pp. 2196-2203, 2007.
[CrossRef] [Web of Science Times Cited 21] [SCOPUS Times Cited 30]


[10] N. R. Secco, B. S. de Mattos, "Artificial neural networks to predict aerodynamic coefficients of transport airplanes," Aircraft Engineering and Aerospace Technology, vol. 89, no. 2, pp. 211-230, 2017.
[CrossRef] [Web of Science Times Cited 6] [SCOPUS Times Cited 6]


[11] J. Brett, A. Ooi, "Effect of Sweep Angle on the Vertical Flow over Delta Wings at an Angle of Attack of 10°," Journal of Engineering Science and Technology, vol. 9, no. 6, pp. 768-781, 2014.

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[19] T. Oktay, S. Coban, "Simultaneous Longitudinal and Lateral Flight Control Systems Design for Both Passive and Active Morphing TUAVs," Elektronika ir Elektrotechnika, vol. 23, no. 5, pp. 15-20, 2017.
[CrossRef] [Web of Science Times Cited 5] [SCOPUS Times Cited 7]


[20] T. Oktay, M. Konar, M. A. Mohamed, M. Aydin, F. Sal, M. Onay, M. Soylak, "Autonomous flight performance improvement of load-carrying unmanned aerial vehicles by active morphing," International Journal of Mechanical, Aerospace, Industrial, Mechatronic and Manufacturing Engineering, vol. 10, no. 1, pp. 123-132, 2016.

[21] T. Oktay, S. Arik, I. Turkmen, M. Uzun, H. Çelik, "Neural network based redesign of morphing UAV for simultaneous improvement of roll stability and maximum lift/drag ratio," Aircraft Engineering and Aerospace Technology, 2018.
[CrossRef] [Web of Science Times Cited 5] [SCOPUS Times Cited 6]


[22] T. Oktay, M. Uzun, "Aerodynamic Tailcone Shape Optimization for Autonomous Navigation Performance Maximization of Morphing Aerial Robot," presented at the International Conference on Engineering and Natural Science, Sarajevo, 2016.



References Weight

Web of Science® Citations for all references: 3,560 TCR
SCOPUS® Citations for all references: 4,781 TCR

Web of Science® Average Citations per reference: 155 ACR
SCOPUS® Average Citations per reference: 208 ACR

TCR = Total Citations for References / ACR = Average Citations per Reference

We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more

Citations for references updated on 2020-06-03 06:56 in 74 seconds.




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Faculty of Electrical Engineering and Computer Science
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