5th INTERNATIONAL GAP MATHEMATICS-ENGINEERING-SCIENCE AND HEALTH SCIENCES, Şanlıurfa, Türkiye, 4 - 06 Aralık 2020, ss.65
In
this study, artificial neural network (ANN) method was applied to predict lift
force of a non-slender delta wing under ground effect. The delta wing has a
sweep angle of Ʌ=40° and chord length, c=14 cm. The variation of
lift forces under the effect of the ground was revealed at angles of attack
within the range of 2°≤α≤35° with 3° interval. In this investigation the
variation of distance between trailing edge of the delta wing, h
normalized with the chord length, c of the wing. The h/c which is
the one of the most important parameters of the study was selected as h/c=
0.7, 0.55, 0.4, 0.25 and 0.1. The aim of the investigation is to predict lift
force generated by the delta wing in case of h/c=0.4 by using actual
lift forces in cases of h/c=0.7, 0.55, 0.25 and 0.1. Resilient
propagation (RP) learning algorithm was carried out in the current simulation.
The hidden layers and output layer of the network includes logistic sigmoid
transfer function (logsig) and linear transfer function (purelin) as an
activation function. At the end, lift force values acquired by artificial
neural network (ANN) were compared with the actual values of lift forces. The
mean absolute percentage error (MAPE) which is a measure of closeness of
estimated value to the actual value was found as 2.1%. On the other hand, the
two important parameters to assess the accuracy of estimated value, mean
absolute deviation (MAD) which states the average of absolute error
between predicted and real values and the root mean square
error (RMSE) was calculated as 2.8% and 3.1%, respectively. It can be said that
MAPE, MAD and RMSE are within the acceptable ranges. Thus, this study
demonstrates that lift force of the delta wing under ground effect can be
accurately predicted by using artificial neural network (ANN).