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Variable Population Models in a Neural Network- Augmented Genetic Algorithm for Shape Optimization | ||
| AUT Journal of Mechanical Engineering | ||
| مقاله 2، دوره 4، شماره 4، اسفند 2020، صفحه 435-448 اصل مقاله (1.46 M) | ||
| نوع مقاله: Research Article | ||
| شناسه دیجیتال (DOI): 10.22060/ajme.2020.16445.5819 | ||
| نویسندگان | ||
| Ali R. Davari* 1؛ S. M. Pourkiaei2 | ||
| 1Department of MEchanical and Aerospace Eng., Schience and Research Branch, Azad University | ||
| 2Department of Engineering, Science and Research Branch, Islamic Azad University | ||
| چکیده | ||
| The optimization process for an airfoil using genetic algorithm has been an increasingly popular problem in recent years. In recent years, the role of the population model in genetic algorithm has been underlined. In many of the recently proposed models, the convergence time was adversely increased or the elitism or mutation operators failed to work properly due to the inherent oscillations in the oncoming generations. In this paper, the idea of continuous variable population size has been introduced to optimize the airfoil shape. This scheme has been shown to converge to higher performance airfoils and can decrease the convergence time, without any oscillatory behavior. Furthermore, to reduce the run time to evaluate the fitness value, a generalized regression neural network has been developed and trained by the numerical data to evaluate the lift to drag ratio for a vast range of NACA four digits airfoils. The values predicted by this neural network have been proved to be in good agreement with the other experimental and numerical data and were then used to calculate the lift-to-drag ratios as the fitness value for various airfoils generated during the optimization process. The idea can ever be more effective in similar problems with a huge amount of computational time to calculate the fitness values and converge to the most efficient airfoil in a reasonable time. | ||
| کلیدواژه‌ها | ||
| Genetic Algorithm؛ Population؛ Wind Turbine؛ NACA Airfoils؛ Optimization؛ GRNN | ||
| مراجع | ||
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[1] D.E. Goldberg, J.H. Holland, Genetic algorithms and machine learning, (1988). [2] D.W. Bechert, Some Considerations on Measurements in Fluid Dynamics, Near-Wall Turbulent Flows, Elsevier Science B.V., New York, 1993. [3] X. Chen, R. Agarwal, Optimization of FX, DU and NACA airfoils for wind turbine blades using a multi-objective genetic algorithm, in: 51st AIAA Aerospace Sciences Meeting including the New Horizons Forum and Aerospace Exposition, 2013, pp. 782. [4] P. Gage, I. Kroo, A role for genetic algorithms in a preliminary design environment, in: Aircraft Design, Systems, and Operations Meeting, 1993, pp. 3933. [5] K. Yamamoto, O. Inoue, New evolutionary direction operator for genetic algorithms, AIAA journal, 33(10) (1995) 1990-1993. [6] M. Anderson, The potential of genetic algorithms for subsonic wing design, in: Aircraft Engineering, Technology, and Operations Congress, 1995, pp. 3925. [7] D. Doorly, J. Peiro, J.-P. Oesterle, Optimisation of aerodynamic and coupled aerodynamic-structural design using parallel genetic algorithms, in: 6th Symposium on Multidisciplinary Analysis and Optimization, 1996, pp. 4027. [8] B. Bai, X. Li, H. Chen, Aerodynamic and Aeroacoustics Optimization Design of Multi-Element Airfoil by a Genetic Algorithm, in: 25th AIAA/CEAS Aeroacoustics Conference, 2019, pp. 2762. [9] S. Darwish, M. Abdelrahman, A.M. Elmekawy, K. Elsayed, Aerodynamic Shape Optimization of Helicopter Rotor Blades in Hover using Genetic Algorithm and Adjoint Method, in: 2018 AIAA Aerospace Sciences Meeting, 2018, pp. 0044. [10] C. Seager, R.K. Agarwal, Hypersonic blunt-body shape optimization for reducing drag and heat transfer, Journal of Thermophysics and Heat Transfer, 31(1) (2015) 48-55. [11] K.R. Anderson, C. McNamara, T.J. Gross, A. Gatti, Multi-objective Genetic Algorithm Optimization of a Four-Stage Cascade Vapor Compression Refrigeration System, in: 2018 Joint Thermophysics and Heat Transfer Conference, 2018, pp. 3909. [12] A. HACIOGLU, AUGMENTED GENETIC ALGORITHM WITH NEURAL NETWORK AND IMPLEMENTATION TO INVERSE AIRFOIL DESIGN, JOURNAL OF AERONAUTICS AND SPACE TECHNOLOGIES, 1(3) (2004) 1-7. [13] X. Chen, R. Agarwal, Optimization of flatback airfoils for wind-turbine blades using a genetic algorithm, Journal of Aircraft, 49(2) (2012) 622-629. [14] Y. Chen, K. Hord, R. Prater, Y. Lian, L. Bai, Design optimization of a vertical axis wind turbine using a genetic algorithm and surrogate models, in: 12th AIAA Aviation Technology, Integration, and Operations (ATIO) Conference and 14th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference, 2012, pp. 5434. [15] R. Duvigneau, M. Visonneau, Hybrid genetic algorithms and neural networks for fast CFD-based design, in: 9th AIAA/ISSMO Symposium on Multidisciplinary Analysis and Optimization, 2002, pp. 5465. [16] J.-L. Liu, Intelligent genetic algorithm and its application to aerodynamic optimization of airplanes, AIAA journal, 43(3) (2005) 530-538. [17] W. Su, Y. Zuo, Z. Gao, Preliminary aerodynamic shape optimization using genetic algorithm and neural network, in: 11th AIAA/ISSMO multidisciplinary analysis and optimization conference, 2006, pp. 7106. [18] Y.V. Pehlivanoglu, A. Hacıoglu, Inverse design of 2-D airfoil via vibrational genetic algorithm, Journal of Aeronautics and Space Technologies, 2(4) (2006) 7-14. [19] A. Hacioglu, Fast evolutionary algorithm for airfoil design via neural network, AIAA journal, 45(9) (2007) 2196-2203. [20] D.E. Goldberg, K. Deb, J.H. Clark, Genetic algorithms, noise, and the sizing of populations, COMPLEX SYSTEMS-CHAMPAIGN-, 6 (1992) 333-333. [21] J. Arabas, Z. Michalewicz, J. Mulawka, GAVaPS-a genetic algorithm with varying population size, in: Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence, IEEE, 1994, pp. 73-78. [22] V.K. Koumousis, C.P. Katsaras, A saw-tooth genetic algorithm combining the effects of variable population size and reinitialization to enhance performance, IEEE Transactions on Evolutionary Computation, 10(1) (2006) 19-28. [23] S.A. Darani, O. Abdelkhalik, Convergence analysis of hidden genes genetic algorithms in space trajectory optimization, Journal of Aerospace Information Systems, 15(4) (2018) 228-238. [24] J. McCosker, Design and optimization of a small wind turbine, Rensselaer Polytechnic Institute, Troy, NY, (2012). [25] I.H. Abbott, E. Albert, von Doenhoff. Theory of Wing Sections, in, Dover Publications, Inc., New York, 1959. [26] E.L. Houghton, P.W. Carpenter, Aerodynamics for engineering students, Elsevier, 2003. [27] D.F. Specht, A general regression neural network, IEEE transactions on neural networks, 2(6) (1991) 568-576. | ||
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