Nothing Special   »   [go: up one dir, main page]

Skip to main content

Aerodynamic Shape Optimization of Supersonic Wings by Adaptive Range Multiobjective Genetic Algorithms

  • Conference paper
  • First Online:
Evolutionary Multi-Criterion Optimization (EMO 2001)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1993))

Included in the following conference series:

Abstract

This paper describes an application of Adaptive Range Multiobjective Genetic Algorithms (ARMOGAs) to aerodynamic wing optimization. The objectives are to minimize transonic and supersonic drag coefficients, as well as the bending and twisting moments of the wings for the supersonic airplane. A total of 72 design variables are categorized to describe the wing’s planform, thickness distribution, and warp shape. ARMOGAs are an extension of MOGAs with the range adaptation. Four-objective optimization was successfully performed. Pareto solutions are compared with Pareto optimal wings obtained by the previous three-objective optimization and a wing designed by National Aerospace Laboratory (NAL).

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

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
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.

Similar content being viewed by others

References

  1. Yamamoto, K., Inoue, O.: Applications of Genetic Algorithm to Aerodynamic Shape Optimization. AIAA paper 95-1650 (1995)

    Google Scholar 

  2. Quagliarella, D., Periaux, J., Poloni, C., Winter, G. (Eds.): Genetic Algorithms and Evolution Strategies in Engineering and Computer Science. John Wiley and Sons, Chichester (1998)

    MATH  Google Scholar 

  3. Doorly, D.: Parallel Genetic Algorithms for Optimisation in CFD. In: Winter, G., et al. (Eds.): Genetic Algorithms in Engineering and Computer Science. John Wiley and Sons, Chichester (1995) 251–270

    Google Scholar 

  4. Oyama, A., Obayashi, S., Nakamura, S.: Real-Coded Adaptive Range Genetic Algorithm Applied to Transonic Wing Optimization. Lecture notes in Computer Science, Vol. 1917. Springer-Verlag, Berlin Heidelberg New York (2000) 712–721

    Google Scholar 

  5. Fonseca, C. M., Fleming, P. J.: Genetic algorithms for multiobjective optimization: formulation, discussion and generalization. Proc. of the 5th Int. Conference on Genetic Algorithms, Morgan Kaufmann Publishers (1993) 416–423

    Google Scholar 

  6. Obayashi, S., Sasaki, D., Takeguchi, Y., Hirose, N.: Multiobjective Evolutionary Computation for Supersonic Wing-Shape Optimization. IEEE Transactions on Evolutionary Computation, Vol. 4, No. 2 (2000) 182–187

    Article  Google Scholar 

  7. Sasaki, D., Obayashi, S., Sawada, K., Himeno, R.: Multiobjective Aerodynamic Optimization of Supersonic Wings Using Navier-Stokes Equations. Proc. of ECCOMAS 2000 [CD-ROM] (2000)

    Google Scholar 

  8. Iwamiya, T.: NAL SST Project and Aerodynamic Design of Experimental Aircraft. Proc. of 4th ECCOMAS Computing Fluid Dynamics Conference, Vol. 2 (1998) 580–585

    Google Scholar 

  9. Arakawa, M., Hagiwara, I.: Development of Adaptive Real Range (ARRange) Genetic Algorithms, JSME Int. J., Series C, Vol. 41, No. 4 (1998) 969–977

    Google Scholar 

  10. Arakawa, M., Hagiwara, I.: Nonlinear Integer, Discrete and Continuous Optimization Using Adaptive Range Genetic Algorithms, Proc. of 1997 ASME Design Engineering Technical Conferences (1997)

    Google Scholar 

  11. Grenon, R.: Numerical Optimization in Aerodynamic Design with Application to a Supersonic Transport Aircraft. Proc. of Int. CFD Workshop for Super-Sonic Transport Design (1998) 83–104

    Google Scholar 

  12. Obayashi, S., Grurswamy, G. P.: Convergence Acceleration of a Navier-Stokes Solver for Efficient Static Aeroelastic Computations. AIAA Journal, Vol. 33 (1995) 1134–1141

    Article  MATH  Google Scholar 

  13. Baldwin, B. S., Lomax, H.: Thin Layer Approximation and Algebraic Model for Separated Turbulent Flows. AIAA paper 78–257 (1978)

    Google Scholar 

  14. Jameson, A., Caughey, D. A.: Effect of Artificial Diffusion Scheme on Multigrid Convergence. AIAA Paper 77–635 (1977)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2001 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Sasaki, D., Morikawa, M., Obayashi, S., Nakahashi, K. (2001). Aerodynamic Shape Optimization of Supersonic Wings by Adaptive Range Multiobjective Genetic Algorithms. In: Zitzler, E., Thiele, L., Deb, K., Coello Coello, C.A., Corne, D. (eds) Evolutionary Multi-Criterion Optimization. EMO 2001. Lecture Notes in Computer Science, vol 1993. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44719-9_45

Download citation

  • DOI: https://doi.org/10.1007/3-540-44719-9_45

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-41745-3

  • Online ISBN: 978-3-540-44719-1

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics