Hostname: page-component-586b7cd67f-g8jcs Total loading time: 0 Render date: 2024-11-27T20:20:59.453Z Has data issue: false hasContentIssue false
  • English
  • Français

A framework for optimising aspects of rotor blades

Published online by Cambridge University Press:  27 January 2016

C. S. Johnson  and
G. N. Barakos
G. N. Barakos*
Affiliation:
School of Engineering, University of Liverpool, Liverpool, UK
*
[email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

This work presents a computational framework for the optimisation of various aspects of rotor blades. The proposed method employs CFD combined with artificial neural networks, employed as metamodels, and optimisation methods based on genetic algorithms. To demonstrate this approach, two examples have been used, one is the optimal selection of 4- and 5-digit NACA aerofoils for rotor sections and the other is the optimisation of linear blade twist for rotors in hover. For each case, an objective function was created and the meta-model was subsequently used to evaluate this objective function during the optimisation process. The obtained results agree with real world design examples and theoretical predictions. For the selected cases, the artificial neural network was found to perform adequately though the results required a substantial amount of data for training. The genetic algorithm was found to be very effective in identifying a set of near-optimal parameters. The main CPU cost was associated with the population of the database necessary for the meta-models and this task required CFD computations based on the Reynolds-averaged Navier-Stokes equations. The framework is general enough to allow for several design or optimisation tasks to be carried out and it is based on open-source code made available by the authors.

Type
Research Article
Information
The Aeronautical Journal , Volume 115 , Issue 1165 , March 2011 , pp. 147 - 161
Copyright
Copyright © Royal Aeronautical Society 2011 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

1. Walsh, J.L. Performance optimisation of helicopter rotor blades, NASA Technical Memorandum, TM-104054, NASA, April 1991.Google Scholar
2. Le Pape, A. and Beaumier, P. Numerical optimisation of helicopter rotor aerodynamics performance in hover, J Aerospace Science and Technology, April 2005, 9, (3), pp 191–201.Google Scholar
3. Imiela, M. High-fidelity optimisation framework for helicopter rotors, 35th European Rotorcraft Forum, CD-ROM. Paper 101172, Hamburg, Germany, September 2009.Google Scholar
4. Glaz, B., Friedmann, P.P. and Liu, L. Surrogate based optimisation of helicopter rotor blades for vibration reduction in forward flight, structural multidisciplinary optimisation, DOI: 10•1007/s00158-007-0137-z, 2008, 35, (4), pp 341–363.Google Scholar
5. Tatossian, C., Nadarajah, S.K. and Castonguay, P. Aerodynamic shape optimization of hovering rotor blades using a non-linear frequency domain approach, AIAA 2008-322, 46th AIAA Aerospace Sciences Meeting and Exhibit, Reno, Nevada, USA, 7-10 January 2008.Google Scholar
6. Allen, C.B., Rendall, T.C.S. and Morris, A.M. CFD-based twist optimization of hovering rotors, AIAA 2010-676 48th AIAA Aerospace Sciences Meeting Including the New Horizons Forum and Aerospace Exposition, Orlando, Florida, USA, 4-7 January 2010.Google Scholar
7. Caradonna, F.X. and Tung, C. Experimental and Analytical studies of a model helicopter rotor in hover, NASA Technical Memorandum, NASA-TM-81232, NASA, September 1981.Google Scholar
8. Caradonna, F.X. The application of CFD to rotary wing problems, NASA Technical Memorandum, NASA-TM-10280, NASA, March 1992.Google Scholar
9. Keys, C., Tarzanin, F. and McHugh, F. Effect of twist on helicopter performance and vibratory loads, 13th European Rotorcraft Forum, paper 2-7, Arles, France, 8-11 September, 1987.Google Scholar
10. Martin, P.B. and Leishman, J.G. Trailing vortex measurements in the wake of a hovering rotor blade with Various tip shapes, 58th Annual Forum of the American Helicopter Society, Montreal, Canada, 11-13 June 2002.Google Scholar
11. Vanderplaats, G.N. CONMIN – A FORTRAN Program for Constrained Function Minimisation, User’s Manual, NASA Technical Memorandum, TM X-62,282, NASA, August 1973.Google Scholar
12. Schwabacher, M., Ellman, T. and Hirsh, H. Learning to Set Up Numerical Optimisations of Engineering Designs, Artificial Intelligence for Engineering Design, Analysis and Manufacturing J, 12, (2), pp 173–192, April 1998.Google Scholar
13. Chen, C. and Lee, H. An efficient gradient Forecasting Search Method Utilising the Discrete Difference Equation Prediction Model, Applied Intelligence J, 2002, 16, (2), pp 43–58.Google Scholar
14. Mohammadi, B. and Pironneau, O. Shape optimisation in fluid mechanics, Annual Reviews of Fluid Mechanics, 2004, 36, pp 255–79.Google Scholar
15. Watanabe, T., Matsushima, K. and Nakahashi, K. Aerodynamic shape optimisation of a near-sonic passenger plane using computational fluid dynamics, Proceedings of IMechE, Part G: J Aerospace Engineering, 2008, DOI: 10.1243/09544100JAERO349, 222, (78), pp 1025–1035.Google Scholar
16. Zhao, H., Wang, S., Han, W. and Feng, G. Aerodynamic design by jointly applying S2 flow surface calculations and modern optimisation methods on multistage axial turbines, Frontiers of Energy Power Eng, 2008, China, 2, (1), pp 93–98.Google Scholar
17. Samad, A. and Kim, K.Y. Shape optimisation of an axial compressor blade by multiobjective genetic algorithm, Proceedings of IMechE, Part A: Journal of Power and Energy, DOI: 10.1243/09576509JPE596, June 2008, 222, (6), pp 599–611.Google Scholar
18. Mengistu, T. and Ghaly, W. Aerodynamic Optimisation of Turbomachinery Blades using Evolutionary Methods and ANN-based Surrogate Models, Optimisation Engineering J, DOI: 10.1007/s11081-007-9031-1, 2007, 9, (3), pp 239–255.Google Scholar
19. Le Pape, A. Numerical aerodynamic optimisation of helicopter rotors: multi-objective optimisation in hover and forward flight conditions, 31st European Rotorcraft Forum, [CD-ROM] Paper 98, Florence, Italy, 13-15 September 2005.Google Scholar
20. Hirsh, R. GADO: A Genetic Algorithm for Continuous Design Optimization, PhD thesis, State University of New Jersey, New Jersey, USA, 1998.Google Scholar
21. Steijl, R., Barakos, G. and Badcock, K. A framework for CFD Analysis of helicopter rotors in hover and forward flight, Int J Numerical Methods in Fluids, January 2006, DOI: 10.1002/fld.1086, 51, pp 819–847.Google Scholar
22. Wilcox, D.C. Turbulence Modelling for CFD, 2nd ed, ISBN-10: 1928729088, DCW Industries, USA, 2006.Google Scholar
23. Steijl, R., Barakos, G.N. and Badcock, K.J. Computational Study of the advancing side lift-phase problem, J Aircraft, DOI: 10.2514/1.22044, 45, (1), January-February 2008.Google Scholar
24. Leishman, J.G. Principles of Helicopter Aerodynamics, 2nd ed, ISBN-10: 0521858607, Cambridge Aerospace Series, New York, USA, 2006.Google Scholar
25. ANSYS Inc. ICEM-CFD Hexa Mesh Generation Software, http://www.ansys.com/products/icemcfd.asp.Google Scholar
26. Samarasinghe, S. Neural Networks for Applied Sciences and Engineering – From Fundamentals to Complex Pattern Recognition, Auerbach Publications, ISBN-10: 0-8493-3375-X, New York, USA, 2006.Google Scholar
27. Spentzos, A., Barakos, G., Badcock, K. and Richards, B. Modelling 3-dimensional dynamic stall of helicopter blades using computational fluid dynamics and neural networks, Proceedings of IMechE Part G: J Aerospace Engineering, DOI: 10.1243/09544100JAERO101, 2005, 220, (6), pp 605–618.Google Scholar
28. Regnier, J., Sareni, B. and Roboam, X. System Optimisation by Multi-Objective Genetic Algorithms and Analysis of the Coupling between Variables, Constraints and Objectives, Int J for Computation and Mathematics in Electrical and Electronic Eng, DOI 10.1108/03321640510598157, 2005, 24, (3), pp 805–820.Google Scholar