LOGIN TO YOUR ACCOUNT

Username
Password
Remember Me
Or use your Academic/Social account:

CREATE AN ACCOUNT

Or use your Academic/Social account:

Congratulations!

You have just completed your registration at OpenAire.

Before you can login to the site, you will need to activate your account. An e-mail will be sent to you with the proper instructions.

Important!

Please note that this site is currently undergoing Beta testing.
Any new content you create is not guaranteed to be present to the final version of the site upon release.

Thank you for your patience,
OpenAire Dev Team.

Close This Message

CREATE AN ACCOUNT

Name:
Username:
Password:
Verify Password:
E-mail:
Verify E-mail:
*All Fields Are Required.
Please Verify You Are Human:
fbtwitterlinkedinvimeoflicker grey 14rssslideshare1
Lee, D.S.; Periaux, J.; Gonzalez, L.F.; Onate, E.; Qin, N. (2011)
Publisher: American Institute of Aeronautics and Astronautics
Languages: English
Types: Other
Subjects:
The use of adaptive wing/aerofoil designs is being considered as promising techniques in aeronautic/aerospace since they can reduce aircraft emissions, improve aerodynamic performance of manned or unmanned aircraft. The paper investigates the robust design and optimisation for one type of adaptive techniques; Active Flow Control (AFC) bump at transonic flow conditions on a Natural Laminar Flow (NLF) aerofoil designed to increase aerodynamic efficiency (especially high lift to drag ratio). The concept of using Shock Control Bump (SCB) is to control supersonic flow on the suction/pressure side of NLF aerofoil: RAE 5243 that leads to delaying shock occurrence or weakening its strength. Such AFC technique reduces total drag at transonic speeds due to reduction of wave drag. The location of Boundary Layer Transition (BLT) can influence the position the supersonic shock occurrence. The BLT position is an uncertainty in aerodynamic design due to the many factors, such as surface contamination or surface erosion. The paper studies the SCB shape design optimisation using robust Evolutionary Algorithms (EAs) with uncertainty in BLT positions. The optimisation method is based on a canonical evolution strategy and incorporates the concepts of hierarchical topology, parallel computing and asynchronous evaluation. Two test cases are conducted; the first test assumes the BLT is at 45% of chord from the leading edge and the second test considers robust design optimisation for SCB at the variability of BLT positions and lift coefficient. Numerical result shows that the optimisation method coupled to uncertainty design techniques produces Pareto optimal SCB shapes which have low sensitivity and high aerodynamic performance while having significant total drag reduction.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • 1Lee, D. S., Gonzalez, L. F., Srinivas, K., Periaux, J., Robust Evolutionary Algorithms for UAV/UCAV Aerodynamic and RCS Design Optimisation, International Journal Computers and Fluids. Vol 37. Issue 5, pages 547-564, ISSN 0045-7930. 2008.
    • 2Lee, D. S., Gonzalez, L. F., Srinivas, K., Periaux, J., Robust Design Optimisation using Multi-Objective Evolutionary Algorithms, International Journal Computers and Fluids. Vol 37. Issue 5, pages 565-583, ISSN 0045-7930. 2008.
    • 3Taguchi, G., Chowdhury, S., Robust Engineering, McGraw-Hill, New York, 2000.
    • 4Lee, D. S., Gonzalez, L. F., Periaux, J., Srinivas, K., Evolutionary Optimisation Methods with Uncertainty for Modern Multidisciplinary Design in Aeronautical Engineering, Notes on Numerical Fluid Mechanics and Multidisciplinary Design (NNFM 100), 100 Volumes NNFM and 40 Years Numerical Fluid Mechanics. Pages 271-284, Ch. 3., Heidelberg: SpringerBerlin, ISBN 978-3-540-70804-9, 2009.
    • 5Ashill, P. R., Fulker, L. J. and Shires, A., A novel technique for controlling shock strength of laminar-flow aerofoil sections. Proceedings 1st Europian Forum on Laminar Flow Technology, pp. 175-183, Hamburg, Germany, DGLR, AAAF, RAeS, March 16-18 1992.
    • 6Qin, N., Zhu, Y. and Shaw, S. T., Numerical Study of Active Shock Control for Transonic aerodynamics, International Journal of Numerical Methods for Heat & Fluid Flow, Vol. 14 No. 4, pp 444 - 466, 2004.
    • 7Wong, W.S., Qin, N., Sellars, N., Holden, H., Babinsky, H., A combined experimental and numerical study of flow structures over three-dimensional shock control bumps, Aerospace Science and Technology ˈ Vol.12ˈ pp436-447. 2008.
    • 8Qin, N., Wong, W.S., LeMoigne, A., Three-dimensional contour bumps for transonic wing drag reduction, Proc. IMechE, Part G: J. Aerospace Engineering, Vol.222(G5), pp605-617. 2008.
    • 9Lee, D. S., Gonzalez, L. F. and Whitney, E. J., Multi-objective, Multidisciplinary Multi-fidelity Design tool: HAPMOEA - User Guide. The Univ. of Sydney, Sydney, NSW, Australia. 2007.
    • 10Hansen, N. and Ostermeier, A., Completely Derandomized Self-Adaptation in Evolution Strategies. Evolutionary Computation, 9(2), pp. 159-195, 2001.
    • 11Koza, J., Genetic Programming II. Massachusetts Institute of Technology, 1994.
    • 12Michalewicz, Z., Genetic Algorithms + Data Structures = Evolution Programs. Artificial Intelligence, Springer-Verlag, 1992.
    • 13Wakunda, J., Zell, A., Median-selection for parallel steady-state evolution strategies. In Marc Schoenauer, Kalyanmoy Deb, Günter Rudolph, Xin Yao, Evelyne Lutton, Juan Julian Merelo, and Hans-Paul Schwefel, editors, ParallelProblem Solving from Nature - PPSN VI, pages 405-414, Berlin, Springer, 2000.
    • 14Van Veldhuizen, D. A., Zydallis, J. B., Lamont, G. B., Considerations in Engineering Parallel Multiobjective Evolutionary Algorithms, IEEE Transactions on Evolutionary Computation, Vol. 7, No. 2, pp. 144-17, 2003.
    • 15Sefrioui, M., Périaux, J., A Hierarchical Genetic Algorithm Using Multiple Models for Optimization. In M. Schoenauer, K. Deb, G. Rudolph, X. Yao, E. Lutton, J.J. Merelo and H.-P. Schwefel, editors, Parallel Problem Solving from Nature, PPSN VI, pages 879-888, Springer, 2000.
    • 16Deb, K., Multi-objective genetic algorithms: Problem difficulties and construction of test problems. Evolutionary Computation Journal, 7(3), pages 205-230, 1999.
    • 17Drela, M., A User's Guide to MSES 2.95. MIT Computational Aerospace Sciences Laboratory, September 1996.
    • 18Lee, D. S., Periaux, J., Pons-Prats, J., Bugeda, G. and Onate, E., Double Shock Control Bump Design Optimisation Using Hybridised Evolutionary Algorithms. Special Session (S035 - IEEE CEC): Evolutionary Computation in Aerospace Sciences, 2010 IEEE World Congress On Computational Intelligence (WCCI 2010), Barcelona Spain, July 18-23rd 2010.
    • 19Iman, R. L., Davenport, J. M., Zigler, D. K., Latin Hypercube Sampling (Program User's Guide), OSTI 5571631 1980.
    • 18 American Institute of Aeronautics and Astronautics
  • No related research data.
  • No similar publications.

Share - Bookmark

Cite this article