Skip to main content

Advertisement

Log in

Aerodynamic Optimization of Airfoils Using Adaptive Parameterization and Genetic Algorithm

  • Published:
Journal of Optimization Theory and Applications Aims and scope Submit manuscript

Abstract

A new method for airfoil shape parameterization is presented, and its influences on the optimum design and convergence of the evolutionary optimization process are investigated. An online adaptive method is used that alters the airfoil parametric function during the process of optimization. A geometric inverse design is carried out, and the capability of the method for producing general airfoil shapes is assessed. The performance of the method is then evaluated by aerodynamic shape optimization. The result indicates that the proposed method improves the optimum design airfoil significantly. In addition, it reduces the total number of flow solver calls, which consequently reduces the required computational time.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16

Similar content being viewed by others

References

  1. Le, C., Bruns, T., Tortorelli, D.: A gradient-based, parameter-free approach to shape optimization. Comput. Methods Appl. Mech. Eng. 200, 985–996 (2011)

    Article  MATH  MathSciNet  Google Scholar 

  2. Goldberg, D.E.: Genetic Algorithm in Search, Optimization and Machine Learning. Addison-Wesley, Reading (1989)

    Google Scholar 

  3. Sasaki, D., Obayashi, Sh., Nakahashi, K.: Navier–Stokes optimization of supersonic wings with four objectives using evolutionary algorithm. J. Aircr. 39, 621–629 (2002)

    Article  Google Scholar 

  4. Shahrokhi, A., Jahangirian, A.: A surrogate assisted evolutionary optimization method with application to the transonic airfoil design. Optim. Eng. 42, 497–515 (2010)

    Article  Google Scholar 

  5. Pehlivanoglu, Y., Yagiz, B.: Aerodynamic design prediction using surrogate-based modeling in genetic algorithm architecture. Aerosp. Sci. Technol. 23, 479–491 (2011)

    Article  Google Scholar 

  6. Anderson, K.W., Bonhaus, D.L.: Airfoil design on unstructured grids for turbulent flows. AIAA J. 37, 185–191 (1999)

    Article  Google Scholar 

  7. Zhang, F., Chen, S., Khali, M.: Multi-point optimization of transonic wing by real coded genetic algorithm. In: The Eleventh Annual Conference of the CFD Society of Canada, Vancouver (2003)

    Google Scholar 

  8. Song, W., Keane, A.J.: A study of shape parameterization method for airfoil optimization. In: 10th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference, Albany, New York (2004)

    Google Scholar 

  9. Kulfan, B.M.: Recent extensions and applications of the ‘CST’ universal parametric geometry representation method. Aeronaut. J. 114, 157–176 (2010)

    Google Scholar 

  10. Kulfan, B.M.: Universal parametric geometry representation method. J. Aircr. 45, 142–158 (2008)

    Article  Google Scholar 

  11. Samareh, J.A.: Survey of shape parameterization techniques for high-fidelity multidisciplinary shape optimization. AIAA J. 39, 877–884 (2001)

    Article  Google Scholar 

  12. Li, P., Seebass, A.R., Sobieczky, H.: Manual aerodynamic optimization of an oblique flying wing. AIAA J. 98, 0598 (1998)

    Google Scholar 

  13. Oyama, A., Obayashi, S., Nakahashi, K., Nakamura, T.: Aerodynamic optimization of transonic wing design based on evolutionary algorithm. In: Third International Conference on Non Linear Problems in Aviation and Aerospace Methods and Software, Daytona Beach, FL, USA (2000)

    Google Scholar 

  14. Castonguay, P., Nadarajah, S.: Effect of shape parameterization on aerodynamic shape optimization. In: 45th AIAA Aerospace Science Meeting an Exhibit, Reno, Nevada (2007)

    Google Scholar 

  15. Sobieczky, H.: Parametric airfoils and wings. In: Fujii, K., Delikravich, G.S. (eds.) Notes on Numerical Fluid Mechanics, Wiesbaden, pp. 71–88 (1998)

    Google Scholar 

  16. Shahrokhi, A., Jahangirian, A., Fouladi, N.: Navier–Stokes optimization using genetic algorithm and a flexible parametric airfoil method. In: ERCOFTAC Conference on Design Optimization: Methods and Application, University of Las Palmas de Gran Canaria, Spain (2006)

    Google Scholar 

  17. Shahrokhi, A., Jahangirian, A.: Airfoil shape parameterization for optimum Navier–Stokes design with genetic algorithm. Aerosp. Sci. Technol. 11, 443–450 (2007)

    Article  MATH  Google Scholar 

  18. Quagliarella, D., Cioppa, A.D.: Genetic algorithm applied to the aerodynamic design of transonic airfoils. J. Aircr. 32, 889–891 (1995)

    Article  Google Scholar 

  19. Tes, D., Chan, Y.Y.: Multi-point design of airfoil by genetic algorithm. In: 8th Annual Conference of the CFD Society of Canada, Montreal, Canada (2000)

    Google Scholar 

  20. Deb, K.: Multi-objective Optimization Using Evolutionary Algorithms. Academic Press, New York (2001)

    MATH  Google Scholar 

  21. Jahangirian, A., Hadidoolabi, M.: Unstructured moving grids for implicit calculation of unsteady compressible viscous flows. Int. J. Numer. Methods Fluids 47, 1107–1113 (2005)

    Article  MATH  Google Scholar 

  22. Jahangirian, A., Johnston, L.J.: Automatic generation of adaptive unstructured grids for viscous flow applications. In: 5th International Conference on Numerical Grid Generation in CFD, Mississippi State University (1996)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to A. Jahangirian.

Additional information

Communicated by David G. Hull.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Ebrahimi, M., Jahangirian, A. Aerodynamic Optimization of Airfoils Using Adaptive Parameterization and Genetic Algorithm. J Optim Theory Appl 162, 257–271 (2014). https://doi.org/10.1007/s10957-013-0442-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10957-013-0442-1

Keywords

Navigation