Lift Coefficient Estimation of a Delta Wing Under the Ground Effect Using Artificial Neural Network

Year 2021, Volume: 36 Issue: 3, 625 - 636, 30.09.2021

Sergen Tümse Mehmet Bilgili Beşir Şahin

https://doi.org/10.21605/cukurovaumfd.1005315

Abstract

The estimation of the lift coefficient, CL of a non-slender delta wing under the ground effect, is performed by employing an artificial neural network (ANN). The purpose of the study is to estimate the lift coefficient, CL acting on the delta wing for the ground distance h/c=0.4 by utilizing the actual lift coefficient, CL for the ground distances h/c=1, 0.7, 0.55, 0.25 and 0.1. In this ANN model, the angle of attack, α and ground distance, h/c were used as input parameters and lift coefficients, CL as the output parameter. While mean absolute percentage error (MAPE) and root mean squared error (RMSE) were found as 1.60% and 0.0114 in the testing stage, they were calculated as 1.77% and 0.01 in the training stage. Hence, this investigation shows that the lift coefficient, CL of the delta wing in ground effect can be correctly estimated by developing an ANN model.

Keywords

Artificial neural network, Delta wing, Ground effect, Lift coefficient

Yer Etkisi Altındaki Delta Kanat Üzerinde Oluşan Taşıma Katsayısının Yapay Sinir Ağı Kullanılarak Tahmin Edilmesi

Abstract

Bu çalışmada, yapay sinir ağı kullanılarak, yer etkisi altında olan düşük süpürme açısına sahip delta kanat üzerindeki taşıma katsayının, CL tahmini yapılmıştır. Çalışmanın amacı boyutsuzlaştırılmış yer mesafesinin, h/c=1, 0,7, 0,55, 0,25 ve 0.1 olduğu durumlardaki gerçek taşıma katsayıları kullanılarak, boyutsuz yer mesafesinin, h/c=0.4 olduğu durumdaki taşıma katsayısını tahmin etmektir. Oluşturulan yapay sinir ağı modelinde, hücum açısı, α ve boyutsuzlaştırılmış yer mesafesi, h/c girdi parametreleri olarak kullanılmış, taşıma katsayısı, CL ise çıktı parametresi olarak kullanılmıştır. Oluşturulan yapay sinir ağı modelinin eğitimin aşamasında, ortalama mutlak yüzde hata (MAPE) ve kök ortalama kare hatası (RMSE) sırasıyla %1,60 ve 0,0114 olarak hesaplanırken, test aşamasında bu değerler sırasıyla %1,77 ve 0,01 olarak hesaplanmıştır. Sonuç olarak bu çalışma, yapay sinir ağı kullanılarak yer etkisi altında olan delta kanat üzerindeki taşıma katsayısının, CL doğru bir şekilde tahmin edilebileceğini göstermiştir.

Keywords

Yapay sinir ağı, Delta kanat, Yer etkisi, Taşıma katsayısı

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