A Numerical Study for Prediction of Unmanned Aerial Vehicle Aerodynamic Performance Based on Chord Tip and Offset of the Wing

Firiana Firdaus*, Arif Wahjudi, Wawan Aries Widodo

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

The development of unmanned aerial vehicle (UAV) is multiplying with its use in various fields, which is marked by the emergence of various models that can be adapted based on the functions and needs of the UAV. A UAV with the Cessna 182 type, which can be easily found, is the research object in this paper. Aerodynamic performance is an essential part of the design of a UAV. Therefore, in this study, the geometry of the chord tip (Ct) and the distance of the sweep-back angel (offset) on the wing, which is set as factor parameters, are varied to predict aerodynamic performance as response parameters in the form of a ratio of lift coefficient to maximum drag coefficient (CL/CD max.) and drag coefficient at 0° angle of attack (CD-0). Simulation using XFLR5 to find the value of aerodynamic performance. Artificial neural network (ANN) is used to predict the performance value and find the relationship between the factor parameters at the input layer and the response parameters at the output layer. By using a network arrangement of a maximum of two hidden layers and a maximum of ten neurons in each hidden layer, an MSE of 1.8591 × 10 - 7 is obtained for the maximum CL/CD response and 3.958 × 10 - 7 for the CD-0 response. Dimensional changes in Ct affect the aerodynamic performance of the UAV than dimensional changes of offset.

Original languageEnglish
Title of host publicationRecent Advances in Renewable Energy Systems - Select Proceedings of ICOME 2021
EditorsMohan Kolhe, Aziz Muhammad, Abdel El Kharbachi, Tri Yogi Yuwono
PublisherSpringer Science and Business Media Deutschland GmbH
Pages45-51
Number of pages7
ISBN (Print)9789811915802
DOIs
Publication statusPublished - 2022
Event5th International Conference on Mechanical Engineering, ICOME 2021 - Virtual, Online
Duration: 25 Aug 202126 Aug 2021

Publication series

NameLecture Notes in Electrical Engineering
Volume876
ISSN (Print)1876-1100
ISSN (Electronic)1876-1119

Conference

Conference5th International Conference on Mechanical Engineering, ICOME 2021
CityVirtual, Online
Period25/08/2126/08/21

Keywords

  • Aerodynamic performance
  • Neural network
  • UAV
  • Wing geometry

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