Advertisement

Supervised Parallel Genetic Algorithms in Aerodynamic Optimisation

  • D. J. Doorly
  • J. Peiró

Abstract

This paper describes the application of parallel genetic algorithms (coupled with CFD analysis) to problems of optimal aerodynamic or aerodynamic-structural design of wings and airfoils. The method has been implemented on a variety of parallel architectures, and results to illustrate its application are presented. A common problem with genetic algorithms (GAs) is how to maintain diversity of the gene pool and avoid premature convergence of the population. Subdivision of the population into semi-isolated subpopulations (commonly referred to as ‘demes’) not only helps significantly in this regard, but is ideally suited to implementation on parallel environments. Considerable further advantages may be obtained when some form of automated supervision is added to direct the operation of the parallel GA. A supervision strategy and its parallel implementation are also considered.

Keywords

Genetic Algorithm Parallel Genetic Algorithm Airfoil Shape Airfoil Optimisation Airfoil Geometry 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. [1]
    P. Devine, G. Kendal, and R. Paton. When ‘Herby’ met ElViS — Experiments with Genetic Based Learning Systems, chapter 16. John Wiley & Sons, Chichester, 1996.Google Scholar
  2. [2]
    D. J. Doorly. Parallel genetic algorithms for optimization in CFD, chapter 13. John Wiley & Sons, 1995.Google Scholar
  3. [3]
    D.J. Doorly and J. Peiró. Aerodynamic optimisation using supervised parallel genetic algorithms. In Proc. 13th AIAA CFD Conference. Snowmass Co., 1997. to appear.Google Scholar
  4. [4]
    D.J. Doorly, J. Peiró, T. Kuan, and J-P. Oesterle. Optimisation of airfoils using parallel genetic algorithms. In Proc. 15th Int. Conf. Num. Meth. Fluid Dyn. Monterey, 1996.Google Scholar
  5. [5]
    D. J. Doorly, J. Peiró, and J-P. Oesterle. Optimisation of aerodynamic and coupled aerodynamic-structural design using parallel genetic algorithms. In Proc. Sixth AIAA/NASA/ISSMO Symposium on Multidisciplinary Analysis and Optimization, pages 401–409. AIAA, 1996.Google Scholar
  6. [6]
    M. Drela. XFOIL, An Analysis and Design System for Low Reynolds Number Aerodynamics. Number 54 in Lecture Notes in Engineering. Springer Verlag, Berlin, 1989.Google Scholar
  7. [7]
    D.E. Goldberg. Genetic Algorithms in Search, Optimisation and Machine Learning. Addison-Wesley, 1988.Google Scholar
  8. [8]
    J.H. Holland. Adaptation in Natural and Artficial Systems. MIT Press, 1992.Google Scholar
  9. [9]
    J. Nang and K. Matsuo. A survey of parallel genetic algorithms. J. SICE, 33(6):500–509, 1994.Google Scholar
  10. [10]
    J. Peraire, K. Morgan, and J. Peiró. Unstructured Grid Methods for Advection Dominated Flows, volume 787, pages 5-1–5-39. 1992.Google Scholar
  11. [11]
    C. Poloni, chapter 20. John Wiley & Sons, 1995.Google Scholar
  12. [12]
    D. Quagliarella and A. DellaCioppa. Genetic algorithms applied to the aerodynamic design of transonic airfoils. J. Aircraft, 32:889–891, 1995.CrossRefGoogle Scholar
  13. [13]
    R. Tanese. Distributed Genetic Algorithms. PhD thesis, U. Michigan, 1989.Google Scholar
  14. [14]
    K. Yamamoto and O. Inoue. Applications of genetic algorithms to aerodynamic shape optimisation. In Proc. 12th AIAA Computational Fluid Dynamics Conference. San Diego, CA, 1995. AIAA-95-1650-CP.Google Scholar

Copyright information

© Springer-Verlag Wien 1998

Authors and Affiliations

  • D. J. Doorly
    • 1
  • J. Peiró
    • 1
  1. 1.Department of AeronauticsImperial CollegeLondonUK

Personalised recommendations