IJE TRANSACTIONS A: Basics Vol. 31, No. 4 (April 2018) 666-672    Article in Press

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H. Safikhani and M. Jamalinasab
( Received: August 20, 2017 – Accepted in Revised Form: January 04, 2018 )

Abstract    A multi-objective optimization (MOO) of two-element wing models with morphing flap by using computational fluid dynamics (CFD) techniques, artificial neural networks (ANN), and non-dominated sorting genetic algorithms (NSGA II), is performed in this paper. At first, the domain is solved numerically in various two-element wing models with morphing flap using CFD techniques and lift (L) and drag (D) coefficients in wings are calculated. Afterward, for modeling L and D using grouped method of data handling (GMDH) type artificial neural networks, numerical data of the preceding step will be applied. Eventually, for Pareto based multi-objective optimization of two-element wing models with morphing flap using NSGA II algorithm, the modeling, which is accomplished by GMDH will be applied. It is shown that the achieved Pareto solution includes important design information on such wings.


Keywords    Two-element Wings; Morphing Flap; Multi-objective Optimization; Grouped Method of data Handling; NSGA II; Quadrature Phase Shift Keying



در این مقاله با استفاده از الگوریتم ژنتیک چندهدفی، فرآیند بهینه سازی چندهدفی بال های دو المانی انجام شده است. در ابتدا ناحیۀ محاسباتی با استفاده از دینامیک سیالات محاسباتی حل شده است و در تمامی محاسبات ضرایب برا و پسا محاسبه شده اند. در مرحله بعد از داده های مرحله قبل جهت مدلسازی توابع هدف با استفاده از شبکه عصبی انجام شده است. در پایان نمدار پارتو که شامل اطلاعات بسیار مفیدی در مورد طراحی بال های دو المانی می باشد، ارائه شده است.

References     1.     Gamboa, P., Aleixo, P., Vale, J., Lau, F. and Suleman, A., Design and testing of a morphing wing for an experimental uav. 2007, University Of Beira Interior Covilha (Portugal).

2.     Trapani, G., Kipouros, T. and Savill, A., "Computational aerodynamic design for 2d high-lift airfoil configurations", Pegasus AIAA,  (2010).

3.     Steinbuch, M., Marcus, B. and Shepshelovich, M., "Development of uav wings-subsonic designs", in 41st Aerospace Sciences Meeting and Exhibit., (2003), 603-610.

4.     Kanazaki, M., Tanaka, K., Jeong, S. and Yamamoto, K., "Multi-objective aerodynamic optimization of elements' setting for high-lift airfoil using kriging model", in 44th AIAA Aerospace Sciences Meeting and Exhibit., (2006), 1471-1480.

5.     Jeong, S., Murayama, M. and Yamamoto, K., "Efficient optimization design method using kriging model", Journal of Aircraft,  Vol. 42, No. 2, (2005), 413-420.

6.     Simpson, T., Mistree, F., Korte, J. and Mauery, T., "Comparison of response surface and kriging models for multidisciplinary design optimization", in 7th AIAA/USAF/NASA/ISSMO Symposium on Multidisciplinary Analysis and Optimization., (1998), 4755-4762.

7.     Landman, D. and Britcher, C.P., "Experimental geometry optimization techniques for multi-element airfoils", Journal of Aircraft,  Vol. 37, No. 4, (2000), 707-713.

8.     Vavalle, A. and Qin, N., "Iterative response surface based optimization scheme for transonic airfoil design", Journal of Aircraft,  Vol. 44, No. 2, (2007), 365-376.

9.     Xiong-feng, Z., Zhong-xi, H., Zheng, G. and Zhao-Wei, L., "Dynamic mesh based airfoil design optimization", World Academy of Science, Engineering and Technology, International Journal of Mechanical, Aerospace, Industrial, Mechatronic and Manufacturing Engineering,  Vol. 6, No. 9, (2012), 1902-1907.

10.   Ross, T.E. and Crossley, W.A., "Method to assess commercial aircraft technologies", Journal of Aircraft,  Vol. 37, No. 4, (2000), 570-579.

11.   Secanell, M., Suleman, A. and Gamboa, P., "Design of a morphing airfoil using aerodynamic shape optimization", AIAA Journal,  Vol. 44, No. 7, (2006), 1550-1562.

12.   Kim, S., Alonso, J. and Jameson, A., "Design optimization of high-lift configurations using a viscous continuous adjoint method", in 40th AIAA Aerospace Sciences Meeting & Exhibit., (2002), 844-852.

13.   Di Matteo, N., Guo, S., Ahmed, S. and Li, D., "Design and analysis of a morphing flap structure for high lift wing", in 51st AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference 18th AIAA/ASME/AHS Adaptive Structures Conference 12th., (2010), 3096-3103.

14.   Renukumar, B., Bramkamp, F.D., Hesse, M. and Ballmann, J., "Effect of flap and slat riggings on 2-d high-lift aerodynamics", Journal of Aircraft,  Vol. 43, No. 5, (2006), 1259-1271.

15.   Rogers, S., Menter, F., Durbin, P. and Mansour, N., "A comparison of turbulence models in computing multi-element airfoil flows", in 32nd Aerospace Sciences Meeting and Exhibit., (1994), 291-300.

16.   Manshadi, M.D. and Jamalinasab, M., "Optimizing a two-element wing model with morphing flap by means of the response surface method", Iranian Journal of Science and Technology, Transactions of Mechanical Engineering,  Vol. 41, No. 4, (2017), 343-352.

17.   Farlow, S., Self-organizing method in modeling: Gmdh type algorithm, 1984, Marcel Dekker Inc., New York.

18.   Amanifard, N., Nariman-Zadeh, N., Farahani, M. and Khalkhali, A., "Modelling of multiple short-length-scale stall cells in an axial compressor using evolved gmdh neural networks", Energy Conversion and Management,  Vol. 49, No. 10, (2008), 2588-2594.

19.   Nariman-Zadeh, N., Darvizeh, A. and Ahmad-Zadeh, G., "Hybrid genetic design of gmdh-type neural networks using singular value decomposition for modelling and prediction of the explosive cutting process", Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture,  Vol. 217, No. 6, (2003), 779-790.

20.   Amanifard, N., Nariman-Zadeh, N., Borji, M., Khalkhali, A. and Habibdoust, A., "Modelling and pareto optimization of heat transfer and flow coefficients in microchannels using gmdh type neural networks and genetic algorithms", Energy Conversion and Management,  Vol. 49, No. 2, (2008), 311-325.

21.   Deb, K., Pratap, A., Agarwal, S. and Meyarivan, T., "A fast and elitist multiobjective genetic algorithm: NSGA-II", IEEE Transactions on Evolutionary Computation,  Vol. 6, No. 2, (2002), 182-197.

22.   Safikhani, H., Akhavan-Behabadi, M., Nariman-Zadeh, N. and Abadi, M.M., "Modeling and multi-objective optimization of square cyclones using cfd and neural networks", Chemical Engineering Research and Design,  Vol. 89, No. 3, (2011), 301-309.

23.   Sanaye, S. and Hajabdollahi, H., "Thermal-economic multi-objective optimization of plate fin heat exchanger using genetic algorithm", Applied Energy,  Vol. 87, No. 6, (2010), 1893-1902.

24.   Sanaye, S. and Dehghandokht, M., "Modeling and multi-objective optimization of parallel flow condenser using evolutionary algorithm", Applied Energy,  Vol. 88, No. 5, (2011), 1568-1577.

25.   Shepshelovich, M. and Nagel, A., Slotted high lift aerofoils. 2012, Google Patents.

26.   Spalart, P. and Allmaras, S., "A one-equation turbulence model for aerodynamic flows", in 30th aerospace sciences meeting and exhibit., (1992), 439-446.

27.   M., N.K. and Muddkavi Y., "Cfd analysis of multi-element aerofoils using openfoam", in Proceedings of the 37th National & 4th International Conference on Fluid Mechanics and Fluid Power, IIT Madras, Chennai, India., (2010).

28.   Ivakhnenko, A.G., "Polynomial theory of complex systems", IEEE Transactions on Systems, Man, and Cybernetics,  No. 4, (1971), 364-378.

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