پیوندهای مفید
اخبار و اعلانات
آمار
| تعداد نشریات | 11 |
| تعداد شماره‌ها | 217 |
| تعداد مقالات | 2,170 |
| تعداد مشاهده مقاله | 3,066,314 |
| تعداد دریافت فایل اصل مقاله | 2,231,158 |
Applying evolutionary optimization on the airfoil design | ||
| Journal of Computational & Applied Research in Mechanical Engineering (JCARME) | ||
| مقاله 6، دوره 2، شماره 1، اسفند 2012، صفحه 51-62 اصل مقاله (341.07 K) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22061/jcarme.2012.46 | ||
| نویسندگان | ||
| Abolfazl Khalkhali*1؛ Hamed Safikhani2 | ||
| 1School of Automotive Engineering, Iran University of Science and Technology, Tehran, Iran | ||
| 2Department of Mechanical Engineering, Amirkabir University of Technology, Tehran, Iran | ||
| تاریخ دریافت: 22 تیر 1391، تاریخ بازنگری: 23 تیر 1391، تاریخ پذیرش: 25 تیر 1391 | ||
| چکیده | ||
| In this paper, lift and drag coefficients were numerically investigated using NUMECA software in a set of 4-digit NACA airfoils. Two metamodels based on the evolved group method of data handling (GMDH) type neural networks were then obtained for modeling both lift coefficient (CL) and drag coefficient (CD) with respect to the geometrical design parameters. After using such obtained polynomial neural networks, modified non-dominated sorting genetic algorithm (NSGAII) was used for Pareto based optimization of 4-digit NACA airfoils considering two conflicting objectives such as (CL) and (CD). Further evaluations of the design points in the obtained Pareto fronts using the NUMECA software showed the effectiveness of such an approach. Moreover, it was shown that some interesting and important relationships as the useful optimal design principles involved in the performance of the airfoils can be discovered by the Pareto-based multi-objective optimization of the obtained polynomial meta-models. Such important optimal principles would not have been obtained without using the approach presented in this paper. | ||
| کلیدواژه‌ها | ||
| Multi-objective Optimization؛ GMDH؛ Genetic Algorithm؛ 4-Digit NACA Airfoils؛ NUMECA | ||
| مراجع | ||
|
[1] Oyama, T. Nonomura and K. Fujii, “Data Mining of Pareto-Optimal Transonic Airfoil Shapes Using Proper Orthogonal Decomposition”, Journal of Aircraft, Vol. 47, No. 5, pp. 1756-1762, (2010).
[2] Razaghi, N. Amanifard and N. Nariman-Zadeh, “Modeling and Multi-Objective Optimization of Stall Control on NACA 0015 Airfoil with a Synthetic Jet Using GMDH Type Neural Networks and Genetic Algorithms”, IJE Transactions A: Basics, Vol. 22, No.1, pp. 69‒88, (2009).
[3] J. Astrom, and P. Eykhoff, “System identification, a survey”, Automatica, Vol. 7, pp. 123‒162, (1971).
[4] Sanchez, T. Shibata and L. A. Zadeh, Genetic Algorithms and Fuzzy Logic Systems, Vol. 7, World Scientific, Riveredge, NJ, (1997).
[5] Kristinson and G. Dumont, “System identification and control using genetic algorithms”, IEEE Trans. On Sys., Man, and Cybern, Vol. 22, No. 5, pp. 1033‒1046, (1992).
[6] G. Ivakhnenko, “Polynomial Theory of Complex Systems”, IEEE Trans. Syst. Man & Cybern, SMC-1, pp. 364‒378, (1971).
[7] J. Farlow, Self-organizing Method in Modeling: GMDH type algorithm, Marcel Dekker Inc., New York, (1984).
[8] Nariman-Zadeh, A. Darvizeh, M. E. Felezi and H. Gharababaei, “Polynomial modelling of explosive compaction process of metallic powders using GMDH-type neural networks and singular value decomposition”, Model. Simul. Mater. Sci. Eng., Vol. 10, No.6, pp.727‒744, (2002).
[9] Khalkhali and Hamed Safikhani, “Pareto Based Multi-Objective Optimization of Cyclone Vortex Finder using CFD, GMDH Type Neural Networks and Genetic Algorithms”, Engineering Optimization, Vol. 44, No. 1, pp. 105‒118, (2012).
[10] Khalkhali, Mehdi Farajpoor, Hamed Safikhani, “Modeling and Multi-Objective Optimization of Forward-Curved Blades Centrifugal Fans using CFD and Neural Networks”, Transaction of the Canadian Society for Mechanical Engineering, Vol. 35, No. 1, pp. 63-79, (2011).
[11] Atashkari, N. Nariman-Zadeh, M. Go¨lcu, , A. Khalkhali and A. Jamali “Modeling and multi-objective optimization of a variable valve timing spark-ignition engine using polynomial neural networks and evolutionary algorithms”, Energy Conversion and Management, Vol. 48, No. 3, pp.1029–41, (2007).
[12] Abbott and A. E. Von Doenhoff, Theory of Wing Sections, Dover Publications, New York, pp. 61‒62, (1958).
[13] K. Anderson, J. L. Thomas and C. L. Rumsey, “Application of Thin-Layer Navier Stokes Equations near Maximum Lift”, AIAA Journal, Vol. 13, pp. 49-57, (1984).
[14] A. Coello and A. D. Christiansen “Multi objective optimization of trusses using genetic algorithms”, Computers & Structures, Vol. 75, pp. 647‒660, (2000).
[15] Pareto, Cours d’economic ploitique, Lausanne, Rouge, (1896).
[16] E. Goldberg, Genetic Algorithms in Search, Optimization, and Machine Learning, 1st ed., Addison-Wesley, New York, (1989).
[17] M. Fonseca and P. J. Fleming, “Genetic algorithms for multi-objective optimization: Formulation, discussion and generalization”, Proc. of the Fifth Int. Conf. on Genetic Algorithms, Forrest S. (Ed.), San Mateo, CA, Morgan Kaufmann, pp. 416‒423, (1993).
[18] Toffolo and E. Benini, “Genetic Diversity as an Objective in Multi-objective evolutionary Algorithms”, Evolutionary Computation, Vol. 11, pp. 151-167, (2003). | ||
|
آمار تعداد مشاهده مقاله: 2,512 تعداد دریافت فایل اصل مقاله: 1,474 |
||