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

Year 2021, , 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

References

• 1. Gursul, I., Gordnier, R., Visbal, M., 2005. Unsteady Aerodynamics of Nonslender Delta Wings. Progress in Aerospace Sciences, 41(7), 515-557.
• 2. Ol, M.V., Gharib, M., 2003. Leading-Edge Vortex Structure of Nonslender Delta Wings at Low Reynolds Number. AIAA Journal, 41(1), 16-26.
• 3. Gursul, I., Allan, M., Badcock, K., 2005.Opportunities for the Integrated Use of Measurements and Computations for the Understanding of Delta Wing Aerodynamics. Aerospace Science and Technology, 9(3), 181-189.
• 4. https://www.grc.nasa.gov/www/k12/airplane/liftco.html. Erişim Tarihi: 08.06.2021.
• 5. Kawazoe, H., Morita, S., 2004. Ground Effect on the Dynamic Characteristics of A Wing-Rock Delta Wing. 34th AIAA Fluid Dynamics Conference and Exhibit.
• 6. Lee, T., Tremblay-Dionne, V., Ko, L., 2018. Ground Effect on a Slender Reverse Delta Wing with Anhedral. Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering, 233(4), 1516-1525.
• 7. Tumse, S., Tasci, M. O., Karasu, I., Sahin, B., 2021. Effect of Ground on Flow Characteristics and Aerodynamic Performance of a Non-slender Delta Wing. Aerospace Science and Technology, 110, 106475.
• 8. Lee, T., Ko, L. S., 2018. Ground Effect on the Vortex Flow and Aerodynamics of a Slender Delta Wing. Journal of Fluids Engineering, 140(7).
• 9. Qu, Q., Lu, Z., Guo, H., Liu, P., Agarwal, R. K., 2015. Numerical Investigation of the Aerodynamics of a Delta Wing in Ground Effect. Journal of Aircraft, 52(1), 329–340.
• 10. Ahmed, M.R., Takasaki, T., Kohama, Y., 2007. Aerodynamics of a NACA4412 Airfoil in Ground Effect. AIAA Journal, 45(1), 37–47.
• 11. Narendra, K., Parthasarathy, K., 1990. Identification and Control of Dynamical Systems Using Neural Networks. IEEE Transactions on Neural Networks, 1(1), 4-27.
• 12. Hunt, K., Sbarbaro, D., Zbikowski, R., Gawthrop, P., 1992. Neural Networks for Control Systems-A Survey. Automatica, 28(6), 1083-1112.
• 13.Calise, A.J., Rysdyk., R.T., 1998. Nonlineare Adaptive Flight Control Using Neural Networks. IEEE Control Systems Magazine, 18, 14-25
• 14. Gim, Y., Jang, D.K., Sohn, D.K., Kim, H., Ko, H.S., 2020. Three-dimensional Particle Tracking Velocimetry Using Shallow Neural Network for Real-time Analysis. Experiments in Fluids, 61(2).
• 15.Rabault, J., Kolaas, J., Jensen, A., 2017. Performing Particle Image Velocimetry Using Artificial Neural Networks: A Proof-of-Concept. Measurement Science and Technology, 28(12), 1-14.
• 16.Cai, S., Liang, J., Gao, Q., Xu, C., Wei, R.,2020. Particle Image Velocimetry Based on a Deep Learning Motion Estimator. IEEE Transactions on Instrumentation and Measurement, 69(6), 3538-3554.
• 17.Rabault, J., Kuhnle, A., 2019. Accelerating Deep Reinforcement Learning Strategies of Flow Control through a Multi-environment Approach. Physics of Fluids, 31(9), 094105.
• 18. Tang, H., Rabault, J., Kuhnle, A., Wang, Y., Wang, T., 2020. Robust Active Flow Control over a range of Reynolds Numbers Using an Artificial Neural Network Trained Through Deep Reinforcement Learning. Physics of Fluids, 32(5), 053605.
• 19.Belus, V., Rabault, J., Viquerat, J., Che, Z., Hachem, E., Reglade, U., 2019. Exploiting Locality and Translational Invariance to Design Effective Deep Reinforcement Learning Control of the 1-Dimensional Unstable Falling Liquid Film. AIP Advances, 9(12), 125014.
• 20. Akbiyik, H., Yavuz, H., 2021. Artificial Neural Network Application for Aerodynamics of an Airfoil Equipped with Plasma Actuators. Journal of Applied Fluid Mechanics, 14(4), 1165-1181.
• 21. Akansu, Y.E., Sarıoğlu, M., Seyhan, M., 2016. Aerodynamic Drag Force Estimation of a Truck Trailer Model Using Artificial Neural Network. International Journal of Automotive Engineering and Technologies, 5(4), 168-175.
• 22.Rokhsaz, K., Steck, J.E., 1993. Use of Neural Networks in Control of High-alpha Maneuvers. Journal of Guidance, Control, and Dynamics, 16(5), 934-939.
• 23.Rokhsaz, K., Steck, J.E., 1993. Application of Artificial Neural Networks in NonlinearAerodynamics and Aircraft Design. SAE Technical Paper Series.
• 24. Alkhedher, M., Al-Aribe, Khaled., 2019. Estimation and Prediction of Nonlinear Aerodynamics using Artificial Intelligence. 7th International Conference on Future Internet of Things and Cloud Workshops (FiCloudW).
• 25. Gomec, F.S., Canibek, M., 2017. Aerodynamic Database Improvement of Aircraft Based on Neural Networks and Genetic Algorithms. 7th European Conference for Aeronautics and Space Sciences (Eucass).
• 26.Ignatyev, D., Khrabrov, A., 2018. Experimental Study and Neural Network Modeling of Aerodynamic Characteristics of Canard Aircraft at High Angles of Attack. Aerospace, 5(1), 26.
• 27. Secco, N.R., Mattos, B.S., 2017. Artificial Neural Networks to Predict Aerodynamic Coefficients of Transport Airplanes. Aircraft Engineering and Aerospace Technology, 89(2), 211-230.
• 28.Rai, M.M., Madavan, N.K., 2001. Application of Artificial Neural Networks to the Design of Turbomachinery Airfoils. Journal of Propulsionand Power, 17(1), 176-183.
• 29. Akbiyik, H., Yavuz, H., 2020. Dbd Plazma Aktüatör Sürüm Frekansının Uçak Kanadı Etrafındaki Akışın Kontrolüne Etkisinin İncelenmesi. Konya Journal of Engineering Sciences, 8(3), 522–528.
• 30. Akbiyik, H., Yavuz, H., Akansu, Y.E., 2017. Comparison of the Linear and Spanwise-Segmented Dbd Plasma Actuators on Flow Control around a Naca0015 Airfoil. IEEE Transactions on Plasma Science, 45(11), 2913-2921.
• 31.Bishop, C.M., 1994. Neural Networks and Their Applications. Review of Scientific Instruments,n 65, 1803-1832.
• 32. Ozbek, A., 2016. Yapay Sinir Ağları Kullanarak Nemli Havanın Termodinamik Özelliklerinin Tahmini. Çukurova Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi, 31(1), 51–58.
• 33. Hosoz, M., Ertunc, H.M., Bulgurcu, H., 2007. Performance Prediction of a Cooling Tower Using Artificial Neural Network. Energy Conversion and Management, 48(4), 1349-1359.
• 34. Senkal, O., 2016. Yapay Sinir Ağları ile Atmosferik Parametreler Kullanılarak Türkiye için Güneş Radyasyonu Modellemesi. Çukurova Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi, 31(2), 179–186.
• 35. Lewis, C.D., 1982. Industrial and Business Forecasting Methods. Butterworth Scientific, London.