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

Abstract

AbstractThe 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 \(1.8591 \times 10^{ - 7}\) is obtained for the maximum CL/CD response and \(3.958 \times 10^{ - 7}\) for the CD-0 response. Dimensional changes in Ct affect the aerodynamic performance of the UAV than dimensional changes of offset.KeywordsNeural networkUAVAerodynamic performanceWing geometry