Skip to main content
SpringerLink
Log in

Inverse Optimization Method for Aerodynamic Shape Design

  • Chapter
Recent Development of Aerodynamic Design Methodologies

Part of the book series: Notes on Numerical Fluid Mechanics (NNFM) ((NONUFM,volume 65))

  • 469 Accesses

Summary

Characteristics of aerodynamic optimization have been discussed through wing shape design problems. It has been demonstrated that distribution of the objective function can be extremely rough even in a simplified problem. In such a situation, Genetic Algorithm (GA) is more effective than a simple hill-climbing strategy. GA, however, requires a large amount of computational time. To alleviate this, the inverse optimization method is developed and discussed in this paper. This method optimizes target pressure distributions for the inverse problem. In the two dimensions, viscous drag is rninimized under specified lift and airfoil thickness. In the three dimensions, multi-objective GA (MOGA) based on the Pareto ranking method is employed. The present design procedure allows the minimization of the induced drag, while mamtaining the straight isobar pattern of pressures. The resulting procedure is shown to be successful when applied to transonic wing design.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. van den Dam, R. F., van Egmond, J. A. and Slooff, J. W.: Optimization of target pressure distributions, Special Course on Inverse Methods for Airfoil Design for Aeronautical and Turbomachinery Applications, AGARD Report No. 780, Reference 3, (1990).

    Google Scholar 

  2. Press, W. H., et al.: Numerical Recipes in FORTRAN: the art of scientific computing, 2nd ed., Cambridge University Press, Cambridge, (1992).

    Google Scholar 

  3. Vanderplaats, G. N.: Numerical Optimization Techniques for Engineering Design: with applications, McGraw-Hill, Inc., New York, (1984).

    MATH  Google Scholar 

  4. Goldberg, D. E.: Genetic Algorithms in Search Optimization & Machine Learning, Addison-Wesley Publishing Company, Inc., Reading, (1989).

    MATH  Google Scholar 

  5. Bramlette, M. F. and Cusic, R.: A comparative evaluation of search methods applied to the parametric design of aircraft, Proceedings of the Third International Conference on Genetic Algorithms, Morgan Kaufmann Publishers, Inc., San Mateo, pp. 213–218, (1989).

    Google Scholar 

  6. Crispin, Y.: Aircraft conceptual optimization using simulated evolution, AIAA Paper 94–0092, (1994).

    Google Scholar 

  7. Powell, D. J., Tong, S. S. and Skolnick, M. M.: EnGENEous domain independent, machine learning for design optimization, Proceedings of the Third International Conference on Genetic Algorithms, Morgan Kaufmann Publishers, Inc., San Mateo, pp. 151–159, (1989).

    Google Scholar 

  8. Gage, P. and Kroo, I.: A role for genetic algorithms in a preliminary design environment, AIAA Paper 93–3933, (1993).

    Google Scholar 

  9. Gregg, R. D. and Misegades, K. P.: Transonic wing optimization using evolution theory, AIAA Paper 87–0520, (1987).

    Google Scholar 

  10. Quagliarella, D. and Cioppa, A. D.: Genetic algorithms applied to the aerodynamic design of transonic airfoils, AIAA Paper 94–1896, (1994).

    Google Scholar 

  11. Yamamoto, K. And Inoue, O.: Applications of genetic algorithm to aerodynamic shape optimization, AIAA Paper 85–1650-CP, A collection of technical papers, 12th AIAA Computational Fluid Dynamics Conference, CP956, San Diego, CA, pp. 43–51, (1995).

    Google Scholar 

  12. Obayashi S. and Takanashi, S.: Genetic optimization of target pressure distributions for inverse design methods, AIAA Journal, vol. 34, no. 5, pp. 881–886, (1996).

    Article  Google Scholar 

  13. Doorly, D.: Parallel genetic algorithms for optimization in CFD, Genetic Algorithms in Engineering and Computer Science, Winter, G., et al. (ed.), John Wiley & Sons, Chichester, pp. 251–270, (1995).

    Google Scholar 

  14. Périaux, J., et al: Robust genetic algorithms for optimization problems in aerodynamic design, Genetic Algorithms in Engineering and Computer Science, Winter, G., et al. (ed.), John Wiley & Sons, Chichester, pp. 371–396, (1995).

    Google Scholar 

  15. Poloni, C.: Hybrid GA for multi objective aerodynamic shape optimization, Genetic Algorithms in Engineering and Computer Science, Winter, et al. (ed.), John Wiley & Sons, Chichester, pp. 397–416, (1995).

    Google Scholar 

  16. Labrujere, Th. E. and Slooff, J. W.: Computational methods for the aerodynamic design of aircraft components, Annual Review of Fluid Mechanics, Vol. 25, pp.183–214, (1993).

    Article  Google Scholar 

  17. van den Dam, R. F.: Constrained spanload optimization for minimum drag of multi-lifting surface configurations, Computational Methods for Aerodynamic Design (Inverse) and Optimization, AGARD Conference Proceedings No. 463, Reference 16, (1990).

    Google Scholar 

  18. van Egmond, J. A.: Numerical optimization of target pressure distributions for subsonic and transonic airfoil design, Computational Methods for Aerodynamic Design (Inverse) and Optimization, AGARD Conference Proceedings No. 463, Reference 17, (1990).

    Google Scholar 

  19. Takanashi, S.: Iterative three-dimensional transonic wing design using integral equations, Journal of Aircraft, Vol. 22, No. 8, pp. 655–660, (1985).

    Article  Google Scholar 

  20. Fujii, K. and Obayashi, S.: Navier-Stokes simulations of transonic flows over a practical wing configuration, AIAA Journal, Vol. 25, No. 3, pp. 369–370, (1987).

    Article  Google Scholar 

  21. Torenbeek, E.: Synthesis of Subsonic Airplane Design, Kluwer Academic Publishers, Dordrecht, pp. 215 – 262, (1982).

    Google Scholar 

  22. Anderson, Jr., J.D.: Introduction to Flight, McGraw-Hill Inc., New York, pp. 216–222, (1989).

    Google Scholar 

  23. Tamaki, H., Kita H. and Kobayashi, S.: Multi-objective optimization by genetic algorithms: a review, Proceedings of 1996 IEEE International Conference on Evolutionary Computation, pp. 517–522, (1996).

    Google Scholar 

  24. Schaffer, J. D.: Multiple objective optimization with vector evaluated genetic algorithm, Proceedings of the 1st International Conference on Genetic Algorithms, Morgan Kaufinann Publishers, Inc., San Mateo, pp. 93–100, (1985).

    Google Scholar 

  25. Fonseca C. M., and Fleming, P. J.: Genetic algorithms for multiobjective optimization: formulation, discussion and generalization, Proceedings of the 5th International Conference on Genetic Algorithms, Morgan Kaufinann Publishers, Inc., San Mateo, pp. 416–423, (1993).

    Google Scholar 

  26. Horn, J., Nafplitois, N. and Goldberg, D., E.: A niched Pareto genetic algorithm for multiobjective optimization, Proceedings of the 1st IEEE Conference on Evolutionary Computation, pp. 82–87, (1994).

    Google Scholar 

  27. Michielessen, E. and Weile D. S.: Electromagnetic System Design Using Genetic Algorithms, Genetic Algorithms in Engineering and Computer Science, Winter, et al. (ed.), John Wiley & Sons, Chichester, pp. 345–370, (1995).

    Google Scholar 

  28. Katz, J. and Plotkin, A.: Low-Speed Aerodynamics: from wing theory to panel methods, international edition, McGraw-Hill Inc., New York, (1991).

    Google Scholar 

  29. Vanderplaats, G. N.: ADS — A FORTRAN program for automated design synthesis, Version 3.00, Engineering Design Optimization, Inc., (1988).

    Google Scholar 

  30. Davis, L.: Handbook of Genetic Algorithms, Van Nostrand Reinhold, (1990).

    Google Scholar 

  31. Tsutsui, S. and Fujimoto, Y.: Forking genetic algorithms with blocking and shrinking modes (fGA), Proceedings of the 5th International Conference on Genetic Algorithms, Morgan Kaufinann Publishers, Inc., San Mateo, pp. 206–213, (1993).

    Google Scholar 

  32. Rogers, D. F. and Adams, J. A.: Mathematical Elements for Computer Graphics, Second Edition, McGraw-Hill, Inc., New York, (1990).

    Google Scholar 

  33. Harris, C. D.: NASA supercritical airfoils — a matrix of family-related airfoils, NASA TP-2969, (1990).

    Google Scholar 

  34. Young, A. D.: Boundary Layers, AIAA Education Series, Washington, D. C., (1989).

    Google Scholar 

  35. Matsushima, K., Obayashi, S. and Fujii, K.: Navier-Stokes computations of transonic flow using the LU-ADI method, AIAA Paper 87–0421, (1987).

    Google Scholar 

  36. Baldwin, B. S. and Lomax, H.: Thin-layer approximation and algebraic model for separated turbulent flows, AIAA Paper 78–257, (1978).

    Google Scholar 

  37. Michalewicz, Z.: Genetic Algorithms + Data Structures = Evolution Programs, Second extended edition, Springer-Verlag, Berlin, pp. 57–58, (1994).

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Aeronautics and Space Engineering, Tohoku University, Sendai, 980-77, Japan

    Shigeru Obayashi

Authors
  1. Shigeru Obayashi
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Kozo Fujii George S. Dulikravich

Rights and permissions

Reprints and permissions

Copyright information

© 1999 Friedr. Vieweg & Sohn Verlagsgesellschaft mbH, Braunschweig/Wiesbaden

About this chapter

Cite this chapter

Obayashi, S. (1999). Inverse Optimization Method for Aerodynamic Shape Design. In: Fujii, K., Dulikravich, G.S. (eds) Recent Development of Aerodynamic Design Methodologies. Notes on Numerical Fluid Mechanics (NNFM), vol 65. Vieweg+Teubner Verlag. https://doi.org/10.1007/978-3-322-89952-1_2

Download citation

  • DOI: https://doi.org/10.1007/978-3-322-89952-1_2

  • Publisher Name: Vieweg+Teubner Verlag

  • Print ISBN: 978-3-322-89954-5

  • Online ISBN: 978-3-322-89952-1

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation