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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

 HIGHLY CITED PAPER 

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: 670 | Views: 1,829

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

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Cited-By SCOPUS

SCOPUS® Times Cited: 4
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Cited-By CrossRef

[1] Simultaneous tailplane of small UAV and autopilot system design, Çoban, Sezer, Aircraft Engineering and Aerospace Technology, ISSN 1748-8842, Issue 10, Volume 91, 2019.
Digital Object Identifier: 10.1108/AEAT-03-2019-0043
[CrossRef]

[2] Simultaneous determination of maximum acceleration and endurance of morphing UAV with ABC algorithm-based model, Konar, Mehmet, Aircraft Engineering and Aerospace Technology, ISSN 1748-8842, Issue 4, Volume 92, 2020.
Digital Object Identifier: 10.1108/AEAT-11-2019-0229
[CrossRef]

[3] Artificial Intelligence to Enhance Aerodynamic Shape Optimisation of the Aegis UAV, Azabi, Yousef, Savvaris, Al, Kipouros, Timoleon, Machine Learning and Knowledge Extraction, ISSN 2504-4990, Issue 2, Volume 1, 2019.
Digital Object Identifier: 10.3390/make1020033
[CrossRef]

[4] Sabit Hücum Açısı ile Yapılan Wingsuit Atlayışlarının Aerodinamik Hesaplamalarının İncelenmesi, KEKEÇ, Emin Tugay, KONAR, Mehmet, Journal of Aviation, ISSN 2587-1676, 2021.
Digital Object Identifier: 10.30518/jav.866712
[CrossRef]

[5] Improvement of the thrust-torque ratio of an unmanned helicopter by using the ABC algorithm, Konar, Mehmet, Turkmen, Aydin, Oktay, Tugrul, Aircraft Engineering and Aerospace Technology, ISSN 1748-8842, Issue 8, Volume 92, 2020.
Digital Object Identifier: 10.1108/AEAT-03-2020-0057
[CrossRef]

[6] GAO Algoritma tabanlı YSA modeliyle İHA motorunun performansının ve uçuş süresinin maksimizasyonu, Konar, Mehmet, European Journal of Science and Technology, ISSN 2148-2683, Volume , 2019.
Digital Object Identifier: 10.31590/ejosat.529093
[CrossRef]

[7] Redesign of morphing UAV's winglet using DS algorithm based ANFIS model, Konar, Mehmet, Aircraft Engineering and Aerospace Technology, ISSN 1748-8842, Issue 9, Volume 91, 2019.
Digital Object Identifier: 10.1108/AEAT-09-2018-0255
[CrossRef]

[8] NACA 4412 Kanadı Üzerinde Bir Emme Kanalı Tasarlanmasının Aerodinamik Etkileri, Oktay, Tugrul, Kanat, Öztürk Özdemir, European Journal of Science and Technology, ISSN 2148-2683, 2019.
Digital Object Identifier: 10.31590/ejosat.651523
[CrossRef]

Updated 2 days, 15 hours ago

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