Abstract




 
   

IJE TRANSACTIONS A: Basics Vol. 22, No. 1 (February 2009) 69-88   

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  MODELING AND MULTI-OBJECTIVE OPTIMIZATION OF STALL CONTROL ON NACA0015 AIRFOIL WITH A SYNTHETIC JET USING GMDH TYPE NEURAL NETWORKS AND GENETIC ALGORITHMS
 
 
R. Razaghi, N. Amanifard* and N. Narimanzadeh

Department of Mechanical Engineering, Faculty of Engineering, University of Guilan, P.O. Box 3756, Rasht, Iran
ramin_razaghi@yahoo.com - namanif@guilan.ac.ir – nnzadeh@guilan.ac.ir

* Corresponding Author
 
 
( Received: March 12, 2008 – Accepted in Revised Form: May 09, 2008 )
 
 

Abstract    This study concerns numerical simulation, modeling and optimization of aerodynamic stall control using a synthetic jet actuator. Thenumerical simulation was carried out by a large-eddy simulation that employs a RNG-based model as the subgrid-scale model. The flow around a NACA0015 airfoil, including a synthetic jet located at 10 % of the chord, is studied under Reynolds number Re = 12.7 × 106 and the angle-of-attack at 18-deg conditions. Then, group method of data handling (GMDH) type neural networks are used for modeling the effects of the actuators parameters (momentum coefficient, reduced frequency, angle with respect to the wall) on both developed time-averaged lift (CL) and time-averaged drag (CD), using some numerically obtained training and test data. To use the obtained polynomial neural network models, multi-objective genetic algorithms (GAs) (non-dominated sorting genetic algorithm, NSGA-II) with a new diversity preserving the mechanism, which is then used for Pareto based optimization of control parameters considers two conflicting objectives such as lift (CL) and drag (CD). It is shown that some interesting and important relationships as useful optimal design principles are involved in the performance of stall control on NACA0015 airfoil. Using a synthetic jet actuator can be discovered by the Pareto based multi-objective optimization of polynomial models. Such important optimal principles would not have been obtained without the use of both GMDH-type neural network modeling and Pareto optimization approach.

 

Keywords    Aerodynamic Stall Control, Multi-Objective Optimization, Gas, Synthetic Jet

 

References   

1. Hassan, A., Straub, F. and Charles, B. D., “Effects of Surface Blowing/Suction on the Aerodynamics of Helicopter Rotor Blade-Vortex Interactions-A Numerical Simulation”, J. Am Helicopter Soc., Vol. 42, (1997), 182-194.

2. Seifert, A., Darabi, A. and Wygnanski, I., “Delay of Airfoil Stall by Periodic Excitation”, AIAA J., Vol. 33, No. 4, (1996), 691-707.

3. Seifert, A., Bashar, T., Koss, D., Shepshelovich, M. and Wygnanski, I., “Oscillatory Blowing: A Tool to Do Delay Boundary Layer Separation”, AIAA J., Vol. 31, No. 11, (1993), 2052-2060.

4. Smith, B. and Glezer, A., “Vectoring and Small-Scale Motions Effected in Free Shear Flows using Synthetic Jet Actuators”, AIAA Paper, (1997), 0213-0221.

5. Gilarranz, J., Traub, L. and Rediniotis, O., “Characterization of a Compact, High Power Synthetic Jet Actuator for Flow Separation Control”, AIAA Paper, (2002), 0127-0135.

6. Smith, D., Amitay, M., Kibens, V., Parekh, D. and Glezer, A., “Modification of Lifting Body Aerodynamics using Synthetic Jet Actuators”, AIAA Paper, (1998), 0209-0218.

7. Wu, J., Lu, X., Denney, A., Fan, M. and Wu, J., “Post-Stall Lift Enhancement on an Airfoil by Local Unsteady Control”, AIAA Paper, Lift, Drag and Pressure Characteristics, Part I, (1997), 2063-2071.

8. Wu, J., Lu, X., Wu, J., “Post-Stall Lift Enhancement on an Airfoil by Local Unsteady Control”, AIAA paper, Mode Competition and Vortex Dynamics, Part II, (1997),2064-2072.

9. Donovan, J. F., Kral, L. D. and Cary, A. W., “Active Control Applied to an Airfoil”, AIAA Paper, (1998), 0210-0218.

10. Ekaterinaris, J., “Active Flow Control of Wing Separated Flow”, ASME FEDSM'03 Joint Fluids Engineering Conference, Honolulu, Hawai, U.S.A., (July 6-10, 2003).

11. Astrom, K. J. and Eykhoff, P., “System Identification, A Survey”, Automatica J., Vol. 7, (1971), 123-162.

12. Sanchez, E., Shibata, T. and Zadeh, L. A., “Genetic Algorithms and Fuzzy Logic Systems”, World Scientific, Riveredge, New Jersey, U.S.A., (1997).

13. Kristinson, K. and Dumont, G., “System Identification and Control using Genetic Algorithms”, J. IEEE Trans Syst Man Cybern, Vol. 22, No. 5, (1992), 1033-1046.

14. Koza, J., “Genetic Programming, on the Programming of Computers by Means of Natural Selection”, MIT Press, Cambridge, MA, U.S.A., (1992).

15. Iba, H., Kuita, T., Degaris, H. and Sator, T., “System Identification using Structured Genetic Algorithms”, Proc. of 5th Int. Conf. on Genetic Algorithms, ICGA’93, U.S.A., (1993).

16. Rodríguez-Vázquez, K., “Multi-Objective Evolutionary Algorithms in Non-Linear System Identification”, PhD Thesis, Department of Automatic Control and Systems Engineering, The University of Sheffield, Sheffield, U.K., (1999).

17. Fonseca, C. M. and Fleming, P. J., “Nonlinear System Identification with Multi-Objective Genetic Algorithms”, Proceedings of the 13th World Congress of the International Federation of Automatic Control, Pergamon Press, San Francisco, California, U.S.A., (1996), 187-192.

18. Liu, G. P. and Kadirkamanathan, V., “Multi-Objective Criteria for Neural Network Structure Selection and Identification of Nonlinear Systems using Genetic Algorithms”, IEEE Proceedings on Control Theory and Applications, Vol. 146, No. 5, (1999), 373-382.

19. Ivakhnenko, A. G., “Polynomial Theory of Complex Systems”, IEEE Trans Syst Man Cybern, SMC-1, (1971), 364-378.

20. Farlow, S. J., “Self-Organizing Method in Modelling: GMDH Type Algorithm”, Marcel Dekker Inc., Paris, French, (1984).

21. Mueller, J. A. and Lemke, F., “Self-Organising Data Mining: An Intelligent Approach to Extract Knowledge From Data”, Pub. Libri, Hamburg, Germany, (2000).

22. Iba, H., Degaris, H. and Sato, T., “A Numerical Approach to Genetic Programming for System Identification”, J. Evolutionary Computation, Vol. 3, No. 4, (1996), 417-452.

23. Nariman-Zadeh, N., Darvizeh, A. and Ahmad-Zadeh, G. R., “Hybrid Genetic Design of GMDH-Type Neural Networks Using Singular Value Decomposition for Modelling and Prediction of the Explosive Cutting Process”, Proceedings of the I MECH E Part B, Journal of Engineering Manufacture, Vol. 217, (2003), 779-790.

24. Nariman-Zadeh, N., Darvizeh, A., Felezi, M. E. and Gharababaei, H., “Polynomial Modelling of Explosive Compaction Process of Metallic Powders using GMDH-Type Neural Networks and Singular Value Decomposition”, Journal of Modelling and Simulation in Materials Science and Engineering, Vol. 10, No. 6, (2002), 727-744.

25. Nariman-Zadeh, N., Darvizeh, A., Darvizeh, M. and Gharababaei, H., “Modelling of Explosive Cutting Process of Plates using GMDH-Type Neural Network and Singular Value Decomposition”, Journal of Materials Processing Technology, Vol. 128, No. 1, (2002), 80-87.

26. Porto, V. W., “Evolutionary Computation Approaches to Solving Problems in Neural Computation”, Handbook of Evolutionary Computation Back, T., Fogel, D. B. and Michalewicz, Z., Editors. Institute of Physics Publishing and New York, Oxford University Press, (1997), D1.2:1-D1.2:6.

27. Yao, X., “Evolving Artificial Neural Networks”, Proceedings of IEEE, Vol. 87, No. 9, (1999), 1423-1447.

28. Vasechkina, E. F. and Yarin, V. D., “Evolv. Ing Polynomial Neural Network by Means of Genetic Algorithm: Some Application Examples”, J. Complexity International, Vol. 9, (2001), 729-744.

29. Nariman-Zadeh, N., Atashkari, K., Jamali, A., Pilechi, A. and Yao, X., “Invers Thermodynamic Pareto Optimization of Turbojet Engines using Multi-Objective Genetic Algorithms”, J. of Engineering Optimization, Vol. 37, 2005, 437-462.

30. Atashkari, K., Nariman-Zadeh, N., Gölcü, M., Khalkhali A. and Jamali, A., “Modelling and Multi-Objective Optimization of a Variable Valve-Timing Spark-Ignition Engine using Polynomial Neural Networks and Evolutionary Algorithms”, J. of Energy Conversion and Management, Vol. 48, No. 3, (2006), 1029-1041.

31. Arora, J. S., “Introduction to Optimum Design”, Mcgraw-Hill, New York, U.S.A., (1989).

32. Rao, S. S., “Engineering Optimization: Theory and Practice”, John Wiley and Sons, New York, U.S.A., (1996).

33. Goldberg, D. E., “Genetic Algorithms in Search, Optimization, and Machine Learning”, Addison-Wesley, New York, U.S.A., (1989).

34. Back, T., Fogel, D. B. and Michalewicz, Z., “Handbook of Evolutionary Computation”, Institute of Physics Publishing and Oxford University Press, New York, U.S.A., (1997).

35. Renner, G. and Ekart, A., “Genetic Algorithms in Computer Aided Design”, J. Computer-Aided Design, Vol. 35, (2003), 709-726.

36. Srinivas, N. and Deb, K., “Multi-Objective Optimization using Non-Dominated Sorting in Genetic Algorithms”, J. Evolutionary Computation, Vol. 2, No. 3, (1994), 221-248.

37. Fonseca, C. M. and Fleming, P. J., “Genetic Algorithms for Multi-Objective Optimization: Formulation, Discussion and Generalization”, Proc. of the Fifth Int. Conf. on Genetic Algorithms, Forrest, S., Editor, San Mateo, CA, Morgan Kaufmann, (1993), 416-423.

38. Coello Coello, C. A. and Christiansen, A. D., “Multi-Objective Optimization of Trusses using Genetic Algorithms”, J. Computers and Structures, Vol. 75, (2000), 647-660.

39. Coello Coello, C. A., Van Veldhuizen, D. A., and Lamont, G. B., “Evolutionary Algorithms for Solving Multi-Objective Problems”, Kluwer Academic Publishers, New York, U.S.A., (2002).

40. Pareto, V., “Cours D’economic Ploitique”, Rouge, Lausanne, Switzerland, (1896).

41. Rosenberg, R. S., “Simulation of Genetic Populations with Biochemical Properties”, PhD Thesis, University of Michigan, Ann Harbor, Michigan, U.S.A., (1967).

42. Schaffer, J. D., “Multiple Objective Optimization with Vector Evaluated Genetic Algorithms”, Proc. of First Int. Conf. on Genetic Algorithms and Their Applications, Grefenstette, J.J., Editor, Lawrence Erlbaum, London, U.K., (1985), 93-100.

43. Zitzler, E., Thiele, L., “An Evolutionary Algorithm for Multi-Objective Optimization: The Strength Pareto Approach”, Tech. Report 43, Computer Engineering and Communication Network Lab, Swiss Federal Ins. of Tech., Zurich, Switzerland, (1998).

44. Knowles, J. and Corne, D., “The Pareto Archived Evolution Strategy: A New Baseline Algorithm for Multi-Objective Optimization”, Proc. of the 1999 Congress on Evolutionary Computation, Piscataway, IEEE Service Center, New Jersey, U.S.A., (1999), 98-105.

45. Horn, J., Nafpliotis, N. and Goldberg, D. E., “A Niched Pareto Genetic Algorithm For Multi-Objective Optimization”, Proceedings of the First IEEEConference on Evolutionary Computation, IEEE World Congress on Computational Intelligence, Piscataway, IEEE Service Centre, New Jersey, U.S.A., Vol. 1, (1994), 82-87.

46. Coello Coello, C. A., “A Comprehensive Survey of Evolutionary Based Multi-Objective Optimization Techniques”, Knowledge and Information Systems, An Int. Journal, Vol. 3, (1999), 269-308.

47. Deb., K., “Multi-Objective Optimization Using Evolutionary Algorithms”, John Wiley, U.K., (2001).

48. Khare, V., Yao, X. and Deb, K. “Performance Scaling of Multi-Objective Evolutionary Algorithms”, Proc. of Second International Conference on Evolutionary Multi-Criterion Optimization, (EMO’03), Portugal, (2003).

49. Toffolo, A. and Benini, E., “Genetic Diversity as an Objective in Multi-Objective Evolutionary Algorithms”, Evolutionary Computation, MIT Press, Vol. 11, No. 2, (2003), 151-167.

50. Deb, K., Agrawal, S., Pratap, A. and Meyarivan, T., “A Fast and Elitist Multi-Objective Genetic Algorithm: NSGA-II”, J. IEEE Trans. on Evolutionary Computation, Vol. 6, No. 2, (2002), 182-197.

51. Coello Coello, C. A. and Becerra, R. L., “Evolutionary Multi-Objective Optimization using A Cultural Algorithm”, IEEE Swarm Intelligence Symp., U.S.A., (2003), 6-13.

52. Sarker, R., Liang, K. H. and Newton, C., “A New Continuous Optimization Multi-Objective Evolutionary Algorithm”, European Journal of Operational Research, Vol. 140, (2002), 12-23.

53. Osyezka, A., “Multicriteria Optimization for Engineering Design”, Gero, J. S., Editor, Design Optimization, Academic Press, New Jersey, U.S.A., (1985), 193-227.

54. Smagorinsky, J., “General Circulation Experiments with the Primitive Equations, I. The Basic Experiment”, J. Mon. Weather Rev., Vol. 91, (1963), 99-164.

55. Lilly, D. K., “The Representation of Small Scale Turbulence in Numerical Simulation Experiments”, Proc. IBM Scientific Computing Symposium on Environmental Sciences, Goldstine, H. H., Editor., IBM Form, No. 320-1951, (1967), 195-210.

56. Yakhot, A., Orszag, S. A., Yakhot, V. and Israeli, M., “Renormalization Group Formulation of Large-Eddy Simulation”, Journal of Scientific Computing, Vol. 4, (1989), 139-158.

57. Park, N., Yoo, J. Y. and Choi, H., “Discretization Errors in Large Eddy Simulation: on the Suitability of Centered and Upwind-Biased Compact Difference Schemes”, Journal of Computational Physics, Vol. 198, (2004), 580-616.

58. Mittal, R. and Moin, P., “Suitability of Upwind-Biased Finite-Difference Schemes for Large-Eddy Simulation of Turbulent Flows”, AIAA Journal, Vol. 35, (1997), 1415-1418.

59. Issa, R. I., “Solution of The Implicitly Discretized Fluid Flow Equations by Operator Splitting”, J. Comput Phys., Vol. 62, (1986), 40-65.

60. Rhie, C. and Chow, W., “Numerical Study of Turbulent Flow Past an Airfoil with Trailing Edge Separation”, AIAA J., Vol. 21, (1983), 1525-32.

61. Gilarranz, J. L. and Rediniotis, O. K., “Compact, High-Power Synthetic Jet Actuators for Flow Separation Control,” AIAA Paper, (2001), 0737-0745.

62. Seifert, A. and Latunia, G. P., “Oscillatory Excitation of Unsteady Compressible Flows Over Airfoils at Flight Reynolds Numbers”, 37th AIAA Aerospace Sciences Meeting and Exhibit, Reno, NV, U.S.A., (January 11-14, 1999).

63. Greenblatt, D., Darabi, A., Nishri, B. and Wygnanski, I., “Some Factors Affecting Stall Control with Particular Emphasis on Dynamic Stall”, 30th AIAA Fluid Dynamics Conference, AIAA Paper, Norfolk, VA, U.S.A., (June 28-July 1, 1999), 3504-3512.

64. Zhou, M. D., Fernholz, H. H., Ma, H. Y., Wu, J. Z. and Wu, J. M., “Vortex Capture by a Two-Dimensional Airfoil with a Small Oscillating Leading-Edge Flap”, AIAA Paper, (1993), 3266-3574.

65. Anderson, W. K., Thomas, J. L. and Rumsey, C. L., “Application of Thin-Layer Navier-Stokes Equations Near Maximum Lift”, AIAA Paper, (1984), 0049-0057.

66. Ekaterinaris, J. A., “Numerical Investigations of Dynamic Stall Active Control for Incompressible and Compressible Flows”, AIAA Paper, (2000), 4333-4341.

67. Seifert, A., Darabi, A. and Wygnanski, I., “Delay of Airfoil Stall by Periodic Excitation”, AIAA J. Aircraft, Vol. 33, No. 4, (1996), 691-698.

68. Gilarranz, J. L. and Rediniotis, O. K., “Compact, High-Power Synthetic Jet Actuators for Flow Separation Control”, AIAA Paper, (2001), 0737-0745.

69. Hassan, A. and Janakiram, R. D., “Effects of Zero-Mass Synthetic Jets on the Aerodynamics of the NACA-0012 Airfoil”, Journal of the American Helicopter Society, Vol. 43, No. 4, (October 1998), 2326-2337.





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