Hostname: page-component-74656f9884-rdgjk Total loading time: 0 Render date: 2026-11-08T09:30:10.460Z Has data issue: false hasContentIssue false
  • English
  • Français

Single and multi–objective UAV aerofoil optimisation via hierarchical asynchronous parallel evolutionary algorithm

Published online by Cambridge University Press:  03 February 2016

L. F. Gonzalez ,
D. S. Lee ,
K. Srinivas  and
K. C. Wong
L. F. Gonzalez
Affiliation:
School of Amme, University of Sydney, Sydney, Australia
D. S. Lee
Affiliation:
School of Amme, University of Sydney, Sydney, Australia
K. Srinivas
Affiliation:
School of Amme, University of Sydney, Sydney, Australia
K. C. Wong
Affiliation:
School of Amme, University of Sydney, Sydney, Australia
Rights & Permissions [Opens in a new window]

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the 'Save PDF' action button.

Unmanned aerial vehicle (UAV) design tends to focus on sensors, payload and navigation systems, as these are the most expensive components. One area that is often overlooked in UAV design is airframe and aerodynamic shape optimisation. As for manned aircraft, optimisation is important in order to extend the operational envelope and efficiency of these vehicles. A traditional approach to optimisation is to use gradient-based techniques. These techniques are effective when applied to specific problems and within a specified range. These methods are efficient for finding optimal global solutions if the objective functions and constraints are differentiable. If a broader application of the optimiser is desired, or when the complexity of the problem arises because it is multi-modal, involves approximation, is non-differentiable, or involves multiple objectives and physics, as it is often the case in aerodynamic optimisation, more robust and alternative numerical tools are required. Emerging techniques such as evolutionary algorithms (EAs) have been shown to be robust as they require no derivatives or gradients of the objective function, have the capability of finding globally optimum solutions among many local optima, are easily executed in parallel, and can be adapted to arbitrary solver codes without major modifications. In this paper, the formulation and application of a evolutionary technique for aerofoil shape optimisation is described.

Initially, the paper presents an introduction to the features of the method and a short discussion on multi-objective optimisation. The method is first illustrated on its application to mathematical test cases. Then it is applied to representative test cases related to aerofoil design. Results indicate the ability of the method for finding optimal solutions and capturing Pareto optimal fronts.

Information

Type
Research Article
Information
The Aeronautical Journal , Volume 110 , Issue 1112 , October 2006 , pp. 659 - 672
Copyright
Copyright © Royal Aeronautical Society 2006 

References

1. Deb, K., Multi-Objective Optimisation Using Evolutionary Algorithms, 2003, Wiley.Google Scholar
2. Obayashi, S., Multidisciplinary design optimisation of aircraft wing planform based on evolutionary algorithms, 1998, Proceedings of IEEE International Conference on Systems, Man, and Cybernetics, La Jolla, CA, IEEE, October 1998.Google Scholar
3. Sobieski, J. and Haftka, R.T., Multidisciplinary aerospace design optimisation: survey of recent developments, 1996, AIAA Paper 96-0711.Google Scholar
4. Bäck, T. and Sschwefel, H.P., Evolution strategies I: variants and their computational implementation, In Winter, G., Périeaux, J., Gala, M. and Cuesta, P. (Eds), Genetic Algorithms in Engineering and Computer Science (pp 111–126), Chichester: Wiley, 1995.Google Scholar
5. Goldberg, D., Genetic Algorithms in Search, Optimisation and Machine Learning, 1989, Addison-Wesley.Google Scholar
6. Oyama, A., Liou, M.-S. and Obayashi, S., Transonic axial flow blade shape optimisation using evolutionary algorithm and three-dimension-alNavier-Stokes solver, 2002, Ninth AIAA/ISSMO Symposium on Multidisciplinary Analysis and Optimisation, Atlanta, GA, September 2002.Google Scholar
7. Parmee, I. and Watson, A.H., Preliminary airframe design using co-evolutionary multiobjective genetic algorithms, 1999, Proceedings of the Genetic and Evolutionary Computation Conference, July 1999, Banzhaf, W., Daida, J., Eiben, A.E., Garzon, M.H., Honavar, V., Jakiela, M. and Smith, R.E. (Eds), 2, pp 1657–1665, Orlando, FL, USA, Morgan Kaufmann.Google Scholar
8. Sefrioui, M., Périaux, J. and Ganascia, J.-G., Fast convergence thanks to diversity, 1996, Evolutionary Programming V Proc of the Fifth Annual Conference on Evolutionary Programming, Fogel, L.J., Angeline, P.J. and Back, T. (Eds), MIT Press.Google Scholar
9. Sefrioui, M. and Périaux, J., A Hierarchical genetic algorithm using multiple models for optimisation, Parallel Problem Solving from Nature, 2000, Schoenauer, M., Deb, K., Rudolph, G., Yao, X., Lutton, E., Merelo, J.J. and Schwefel, H.-P. (Eds), PPSN VI, pp 879–888, Springer.Google Scholar
10. Whitney, E., Sefrioui, M., Srinivas, K. and Périaux, J., Advances in hierarchical, parallel evolutionary algorithms for aerodynamic shape optimisation, JSME (Japan Society of Mechanical Engineers) Int J, 2002, 45, (1).Google Scholar
11. Holland, J.H., Adaption in Natural and Artificial Systems, 1975, University of Michigan Press.Google Scholar
12. Bäck, T., Rudolph, G. and Schwefel, H.P., Evolutionary programming and evolution strategies: similarities and differences, 1993, Proceedings of the Second Annual Conference on Evolutionary Programming, pp 11–22, Evolutionary Programming Society, San Diego, CA.Google Scholar
13. Fogel, D.B., An analysis of evolutionary programming, 1992, Proceedings of the First Annual Conference on Evolutionary Programming, Fogel, D.B. and Atmar, W. (Eds), pp 43–51, Evolutionary Programming Society, San Diego, CA.Google Scholar
14. Koza, J.R., On the Programming of Computers by Means of Natural Selection, 1992, MIT Press, Cambridge, MA.Google Scholar
15. Michalewicz, Z., Genetic algorithms + data structures = evolution programs. Artificial Intelligence, 1992, Springer-Verlag.Google Scholar
16. Theirens. Adaptive mutation rate control schemes in genetic algorithms, 2002, Proceedings of the 2002 IEEE World Congress on Computational Intelligence: Congress on Evolutionary Computation, pp 980–985, IEEE Press.Google Scholar
17. Hansen, N. and Ostermeier, A., Completely derandomized self-adaptation in evolution strategies, Evolutionary Computation, 2001, 9, (2), pp 159–195.Google Scholar
18. Collello, C.A, Van Veldhuizen, D.A. and Lamont, G.B., Evolutionary Algorithms for Solving Multi-Objective Problems, March 2002, Kluwer Academic Publishers, New York.Google Scholar
19. Cantu-Paz, E., Efficient and Accurate Parallel Genetic Algorithms, 2000, Kluwer Academic Publishers, New York.Google Scholar
20. Van Veldhuizen, D.A., Zydallis, J.B. and Lamont, G.B., Considerations in engineering parallel multiobjective evolutionary algorithms, IEEE Transactions on Evolutionary Computation, April 2003, 7, (2), pp 144–173.Google Scholar
21. Geist, A., Beguelin, A., Dongarra, J., Jiang, W., Manchek, R. and Sunderam, V. PVM: Parallel Virtual Machine. A User’s Guide and Tutorial for Networked Parallel Computing, 1994, Massachusetts Institute of Technology.Google Scholar
22. Wakunda, J. and Zell, A., Median-selection for parallel steady-state evolution strategies, Parallel Problem Solving from Nature, 2000, Schoenauer, M., Deb, K., Rudolph, G., Yao, X., Lutton, E., Merelo, J.J. and Schwefel, H.-P. (Eds), PPSN VI, pp 405–414, Springer, Berlin.Google Scholar
23. Beyer, H.-G. and Deb, K., On the desired behaviors of self-adaptive evolutionary algorithms, Parallel Problem Solving from Nature, 2000 Schoenauer, M., et al, (Eds), 6, pp 59–68, Springer-Verlag, Heidelberg.Google Scholar
24. Collard, P. and Escazut, C., Genetic operators in a dual genetic algorithm, 1995, Proceedings of the Seventh IEEE International Conference on Tools with Artificial Intelligence, November 1995, pp 12–19, Virginia, USA, IEEE.Google Scholar
25. Drela, M. XFOIL 6.94 User Guide, 2001, MIT Aero Astro.Google Scholar
26. Drela, M., A User’s Guide to MSES V2.3, February 1993.Google Scholar
27. Abott, I.H. and Von Doenhoff, A.E., Theory of Wing Sections, 1980, Dover.Google Scholar
28. Munson, K., Jane’s Unmanned Aerial Vehicles and Targets, 1998, Jane’s Information Group.Google Scholar
29. Ashill, P.R., Fulker, J.L and Shires, A., A novel technique for controlling shock strength of laminar-flow aerofoil sections, 1992, Proceedings First European Forum on Laminar Flow Technology, 16-18 March 1992, pp 175–183, Hamburg, Germany.Google Scholar
30. Oh, J.T., Park, H.C. and Hwang, W., Active shape control of a double-plate structure using piezoceramics and SMA wires, Smart Materials and Structures, 2001, 10, (5), pp 1100–1106.Google Scholar
31. Bushnell, D. Shock wave drag reduction, Annual Review of Fluid Mechanics, January 2004, 36, NASA Langley Research Center, NASA.Google Scholar