Airfoil Optimization with Metaheuristic Artificial Bee Colony Algorithm Supported by Neural Network Trained Using Nasa-Foilsim Data

Year 2022, Volume: 6 Issue: 2, 93 - 102, 24.07.2022

Şeyma Doğan , Cemil Altın

https://doi.org/10.30518/jav.1066478

Abstract

In this study, the wing profile, which is difficult to calculate and determine, has been optimized with the help of Foilsim data and optimization algorithms. Foilsim data provided by NASA (National Aeronautics and Space Administration) and used by many researchers, especially in developing model airplanes, has been provided to use in aircraft wing shape optimization. Although Foilsim is a very useful simulation program for designers, it cannot be used effectively in optimization processes due to its web environment. Lift coefficient is needed for Lift equation in airfoil shape optimization. Lift coefficient depends on angle, camber, and thickness of airfoil Calculation of Lift coefficient is difficult and needs heavy mathematical equations or real experiments. By using Foilsim data and optimization algorithm (Artificial Neural Networks: ANN, Artificial Bee Colony: ABC), wing angle, camber and thickness values, which are difficult to determine and calculate, were estimated and comparative experiments of the values were made. (Fixed Lift, Fixed Speed, Fixed Wing Area). Experimental results have shown that it is a useful study for airfoil shape optimization. In short, in this study, by using the Foilsim data and the optimization algorithm to provide the lifting force determined by the designer, the most suitable angle, camber, thickness values of the wing, which are difficult to determine and calculate, are determined to enable the production of efficient aircraft. The user enters the desired lift value into the ABC optimization algorithm and finds the required wing properties for the desired lift value.

Keywords

Wing Profile, Optimization, ABC Algorithm, ANN Algorithm, MATLAB

References

• Akay, B. (2009). Nümerik optimizasyon problemlerinde yapay arı kolonisi (artıfıcıal bee colony) algoritmasının performans analizi. [Doctoral dissertation, Erciyes University]. Yök Açık Bilim. https://acikbilim.yok.gov.tr/handle/20.500.12812/499805.
• Du, X., He, P. & Martins, J. R. R. A. (2021). Rapid airfoil design optimization via neural networks-based parameterization and surrogate modeling. Aerospace Science and Technology, 113, 106701.
• Elmas, Ç. (2018), Yapay Zeka Uygulamaları, (Birinci Basım), 1-58, Ankara: Seçkin Yayınları.
• Goodfellow, I., Bengio, Y., Courville, A. (2016). Deeplearning (First Edition). MIT press,96-152.
• Gülcü, A., & Kuzucuoğlu, D. (2006). Yapay zeka tekniklerinden genetik algoritma ve tabu arama yöntemlerinin eğitim kurumlarının haftalık ders programlarının hazırlanmasında kullanımı [Master dissertation, University of Marmara]. Yök Açık Bilim. https://acikbilim.yok.gov.tr/handle/20.500.12812/226432.
• Han, Z. H., Abu-Zurayk, M., Görtz, S., & Ilic, C. (2015). Surrogate-Based Aerodynamic Shape Optimization of a Wing-Body Transport Aircraft Configuration. In Notes on Numerical Fluid Mechanics and Multidisciplinary Design (Vol. 138, pp. 257–282). Springer, Cham.
• Han, Z. H., Zhang, K. S., Liu, J., & Song, W. P. (2013). Surrogate-based aerodynamic shape optimization with application to wind turbine airfoils. 51st AIAA Aerospace Sciences Meeting Including the New Horizons Forum and Aerospace Exposition 2013.
• Hicks, R. M., & Henne, P. A. (1978). Wing Design by Numerical Optimization, Journal of Arcraft 15(7), 407–412.
• Jameson, A. (1988).Aerodynamic design via control theory. Journal of Scientific Computing, 3(3), 233–260.
• Jameson, A. (1995). Optimum aerodynamic design using CFD and control theory. 12th Computational Fluid Dynamics Conference, 926–949. Springer
• Jameson, A., & Martinelli, L. (2000). Aerodynamic shape optimization techniques based on control theory. In Computational Mathematics Driven by Industrial Problems (pp. 151–221). Springer, Berlin, Heidelberg.
• Karaboga, D., & Akay, B. (2009). A comparative study of Artificial Bee Colony algorithm. Applied Mathematics and Computation, 214(1), 108–132.
• Kaya, B., & Eke, İ. (2020). Yapay Arı Kolonisi Algoritması ile yapılan geliştirmeler ve sonuçları. Verimlilik Dergisi T.C. Sanayi ve Teknoloji Bakanlığı Yayını 17(1) 99-115.
• Koreanschi, A., Sugar Gabor, O., Acotto, J., Brianchon, G., Portier, G., Botez, R. M., Mamou, M., & Mebarki, Y. (2017). Optimization and design of an aircraft’s morphing wing-tip demonstrator for drag reduction at low speed, Part I – Aerodynamic optimization using genetic, bee colony and gradient descent algorithms. Chinese Journal of Aeronautics, 30(1), 149–163.
• Kose, O. and Oktay, T. (2021). Hexarotor Longitudinal Flight Control with Deep Neural Network, PID Algorithm and Morphing. European Journal of Science and Technology, (27), 115–124.
• Koziel, S., & Leifsson, L. (2013). Surrogate-based aerodynamic shape optimization by variable-resolution models. AIAA Journal, 51(1), 94–106.
• Khurana, M. S., Winarto, H. ve Sinha, A. K. (2009). Airfoil optimisation by swarm algorithm with mutation and Artificial Neural Networks. 47th AIAA Aerospace Sciences Meeting including the New Horizons Forum and Aerospace Exposition.
• Küçüksille, E. U., & Tokmak, M. (2011). Yapay Arı Kolonisi Algoritması Kullanarak Otomatik Ders Çizelgeleme. Süleyman Demirel Üniversitesi Fen Bilimleri Dergisi 15(3) 203-210.
• Li, J., Cai, J. ve Qu, K. (2019). Surrogate-based aerodynamic shape optimization with the active subspace method. Structural and Multidisciplinary Optimization, 59(2), 403–419. d
• Ma, P., Yu, J., Chen, F., & Xue, Z. (2017). Airfoil optimization design based on a combined optimization strategy. Advances in Engineering Research (AER) , volume 130, Proceedings of the 2017 5th International Conference on Frontiers of Msnufacturing Science and Measuring Technology (pp.529–537).
• MacEachern, C., & Yildiz, I. (2018). Wind Energy. In Comprehensive Energy Systems. Vols. 1–5, 665–701. Elsevier Inc.
• Mar Aye, C., Pholdee, N., & Bureerat, S. (2020). Surrogate-assisted Meta-Heuristic method for Aerodynamic Design of an Aircraft Wing. IOP Conference Series: Materials Science and Engineering, 886(1), 012026.
• Mukesh, R., Lingadurai, K., & Selvakumar, U. (2018). Airfoil Shape Optimization based on Surrogate Model. Journal of The Institution of Engineers (India): Series C, 99(1), 1–8.
• Negnevitsky, M. N. (2005). Artificial Intelligence: A Guide to Intelligent Systems, Addison Wesley.
• Öztürk, K., & Şahin, M. E. (2018). Yapay Sinir Ağları ve Yapay Zekaya Genel Bir Bakış. Takvim-i Vekayi, 6(2), 25–36.
• Sun, G., & Wang, S. (2019). A review of the artificial neural network surrogate modeling in aerodynamic design. 233(16),SAGE Journals.5863–5872.
• Surrogate model. (Jan. 22, 2022). In Wikipedia. https://en.wikipedia.org/wiki/Surrogate_model.
• Türkay, M. (2021, November 15), Optimizasyon modelleri ve çözüm metodları. PDFShare: http://home.ku.edu.tr/~ mturkay/indr501/Optimizasyon.pdf.