Hostname: page-component-78c5997874-mlc7c Total loading time: 0 Render date: 2024-11-03T03:19:18.087Z Has data issue: false hasContentIssue false
  • English
  • Français

Surrogate based design optimisation of composite aerofoil cross-section for helicopter vibration reduction

Published online by Cambridge University Press:  27 January 2016

M. S. Murugan ,
R. Ganguli  and
D. Harursampath
M. S. Murugan
Affiliation:
Aerospace Engineering, College of Engineering, Swansea University, Swansea, UK
D. Harursampath
Affiliation:
Department of Aerospace Engineering, Indian Institute of Science, Bangalore, India
Get access Rights & Permissions [Opens in a new window]

Abstract

Design optimisation of a helicopter rotor blade is performed. The objective is to reduce helicopter vibration and constraints are put on frequencies and aeroelastic stability. The ply angles of the D-spar and skin of the composite rotor blade with NACA 0015 aerofoil section are considered as design variables. Polynomial response surfaces and space filling experimental designs are used to generate surrogate models of the objective function with respect to cross-section properties. The stacking sequence corresponding to the optimal cross-section is found using a real-coded genetic algorithm. Ply angle discretisation of 1°, 15°, 30° and 45° are used. The mean value of the objective function is used to find the optimal blade designs and the resulting designs are tested for variance. The optimal designs show a vibration reduction of 26% to 33% from the baseline design. A substantial reduction in vibration and an aeroelastically stable blade is obtained even after accounting for composite material uncertainty.

Type
Research Article
Information
The Aeronautical Journal , Volume 116 , Issue 1181 , July 2012 , pp. 709 - 725
Copyright
Copyright © Royal Aeronautical Society 2012 

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 Johnson, W. Helicopter Theory, Princeton University Press, New Jersey, USA, 980.Google Scholar
2 Friedmann, P.P. Rotary-wing aeroelasticity: current status and future trends, AIAA J, 2004, 42, (10), pp 1953–1972.Google Scholar
3 Ganguli, R. A survey of recent developments in rotorcraft design optimization, J Aircr, 2004, 41, (3), pp 493–510.Google Scholar
4 Booker, A.J., Dennis, J.E., Frank, P.D., Serafini, D.B., Torczon, V. and Trosset, M.W. A Rigorous Framework for optimization of expensive functions by surrogates, Struct multi optim, 1999, 17, (1), pp 1–13.Google Scholar
5 Ganguli, R. Optimum design of helicopter rotor for low vibration using aeroelastic analysis and response surface methods, J Sound Vib, 2002, 258, (2), pp 327–344.Google Scholar
6 Murugan, M.S. and Ganguli, R. Aeroelastic stability enhancement and vibration suppression in a composite helicopter rotor, J Aircr, 2005, 42, (4), pp 1013–1024.Google Scholar
7 Glaz, B., Friedmann, P.P. and Liu, L. Surrogate based optimization of helicopter rotor blades for vibration reduction in forward flight, Struct Multi Optim, 2008, 35, (4), pp 341–363.Google Scholar
8 Murugan, M.S., Ganguli, R. and Harursampath, D. Aeroelastic analysis of composite helicopter rotor with random material properties, J Aircr, 2008, 45, (1), pp 306–322.Google Scholar
9 Murugan, M.S., Ganguli, R. and Harursampath, D. Stochastic aeroelastic analysis of composite helicopter rotor, J Am Helicopter Soci, 2011, 56, (1), Art. 012001.Google Scholar
10 Murugan, M.S., Harursampath, D. and Ganguli, R. Material uncertainty propagation in helicopter nonlinear aeroelastic response and vibration analysis, AIAA J, 2008, 46, (9), pp 2332–2344.Google Scholar
11 Saijal, K.K., Viswamurthy, S.R. and Ganguli, R. Optimization of helicopter rotor using polynomial and neural network metamodels, J Aircr, 2011, 48, (2), pp 553–566.Google Scholar
12 Reisenthal, P.H., Love, J.F., Lesieutre, D.J. and Childs, R.E. Cumulative global metamodels with uncertainty – a tool for aerospace integration, Aeronaut J, 2006, 110, (1108), pp 375–384.Google Scholar
13 Rajagopal, S. and Ganguli, R. Conceptual design of UAV using Kriging based multi-objective genetic algorithm, Aeronaut J, 2008, 112, (1137), pp 653–662.Google Scholar
14 Venter, G., Haftka, R.T. and Starnes, J.H. Construction of response surface approximation for design optimization, AIAA J, 1998, 36, (12), pp 2342–2249.Google Scholar
15 Setiawan, R., Syngellakis, S. and Hill, M.A. Metamodeling approach to mechanical characterization of anisotropic plates, J Compos Mat, 2009, 43, (21), pp 2333–2349.Google Scholar
16 Simpson, T.W., Booker, A.J., Ghosh, D., Giunta, A.A., Koch, P.N. and Yang, R.J. Approximation methods in multidisciplinary analysis and optimization: A panel discussion, Struct Multi Optim, 2004, 27, (5), pp 302–313.Google Scholar
17 Sacks, J., Welch, W.J., Mitchell, T.J. and Wynn, H.P. Design and analysis of computer experiments, Stat Sci, 1989, 4, pp 409–435 Google Scholar
18 Gu, R. and Kokta, B.V. Maximization of the mechanical properties of Birch-polypropylene composites with additives by statistical experimental design, J Thermoplast Compos Mater, 2010, 23, (2), pp 239–263.Google Scholar
19 Gillet, A., Francescato, P. and Saffire, P. Single and multi-objective optimization of copmosite Structures: The influence of design variables, J Compos Mater, 2010, 44, (4), pp 457–480.Google Scholar
20 Satheesh, R., Narayana Naik, G. and Ganguli, R. Conservative design optimization of laminated composite structures using genetic algorithms and multiple falure criteria, J Compos Mater, 2010, 44, (3), pp 369–387.Google Scholar
21 Kim, S.H. and Kim, C.G. Optimal design of composite stiffened panel with cohesive elements using micro-genetic algorithm, J Compos Mater, 2008, 42, (2)1, pp 2259–2273.Google Scholar
22 Barwey, D. and Peters, D.A. Optimization of composite rotor blades with advanced structural and aerodynamic modeling, Math Comput Modell, 1994, 19, (3-4), pp 193–219.Google Scholar
23 Cesnik, C.E.S. and Hodges, D.H. VABS: A new concept for composite rotor blade cross-sectional modeling, J Am Helicopter Soci, 1997, 42, (1), pp 27–38.Google Scholar
24 Lemanski, S.L., Weaver, P.M. and Hill, G.F.J. Design of composite helicopter rotor blades to meet given cross-sectional properties, Aeronaut J, 2005, 109, (1100), pp 471–475.Google Scholar
25 Leihong, L., Volovoi, V.V. and Hodges, D.H. Cross-sectional design of composite rotor blades, J Am Helicopter Soci, 2008, 53, (3), pp 240–251.Google Scholar
26 Jun, L. and Xianding, J. Coupled bending-torsional dynamic response of axially loaded slender composite thin-walled beam with closed cross-section, J Compos Mater, 2004, 38, (6), pp 515–534.Google Scholar
27 Kuttenkeullar, J. Finite element based modal method for determination of plate stiffnesses considering uncertainties, J Compos Mater, 1999, 33, (8), pp 695–711.Google Scholar
28 Kim, S.J., Yoon, K.W. and Jung, S.N. Shear correction factors for thin-walled composite BoxBeam considering nonclassical behaviours, J Compos Mater, 1996, 30, (10), pp 1132–1149.Google Scholar
29 Fukunaga, H. and Sekine, H. Laminate design for elastic properties of symmetric laminates with extension-shear or bending-twisting coupling, J Compos Mater, 1994, 28, (8), pp 708–731.Google Scholar
30 Bir, G. et al, University of Maryland Advanced Rotorcraft Code(UMARC) Theory Manual, UM-AERO Report, 92-02, 1992.Google Scholar
31 Chandra Sekhar, D. and Ganguli, R. Fractal boundaries of basin of attraction of Newton-Raphson method in helicopter trim, Comp Math Appl, 2010, 21, (12), pp 1157–1167.Google Scholar
32 Myers, R.H. and Montgomery, D.C. Response Surface Methodology: Process and Product Optimization using Designed Experiments, Wiley, New York, USA, 1995.Google Scholar
33 Queipo, N., Haftka, R.T., Shyy, W., Goel, T. and Vaidyanathan, R. Surrogate-based analysis and optimization, Prog Aerosp Sci, 2005, 41, pp 1–25.Google Scholar
34 Knill, D.L., Giunta, A.A., Baker, C.A., Grossman, B., Mason, W.H., Haftka, R.T. and Watson, L.T. Response surface models combining linear and Euler aerodynamics for supersonic transportdesign, J Aircr, 1999, 36, pp 75–86.Google Scholar
35 Madsen, J.I., Shyy, W. and Haftka, R.T. Response surface techniques for diffuser shape optimization, AIAA J, 2000, 38, pp 1512–1518.Google Scholar
36 McKay, M.D., Bechman, R.J. and Conover, W.J. A Comparison of three methods for selecting values of input variables in the analysis of output from a computer code, Technometrics, 1979, 21, (2), pp 239–245.Google Scholar
37 Ganguli, R. and Chopra, I. Aeroelastic optimization of an advanced geometry helicopter rotor, J Am Helicopter Soci, 1996, 41, (1), pp 18–28.Google Scholar
38 Ganguli, R. and Chopra, I. Aeroelastic tailoring of composite coupling and blade spanwise geometry of a helicopter rotor using optimization methods, J Am Helicopter Soci, 1997, 42, (3), pp 218–228.Google Scholar
39 Michalewicz, Z. Genetic Algorithms + Data Structures = Evolution Programs, AI Series SpringerVerlag, New York, USA, 1994.Google Scholar
40 Holland, H.J. Adaptation in Natural and Artificial Systems, University of Michigan Press, Ann Arbor, USA, 1975.Google Scholar
41 Goldberg, D.E. Genetic Algorithms in Search, Optimization and Machine Learning, Addison-Wesley, New York, USA, 1989.Google Scholar
42 Murugan, M.S., Suresh, N., Ganguli, R. and Mani, V. Target vector optimization of composite box-beam using real coded genetic algorithm: a decomposition approach, Struct Multi Optim, 2007, 33, (2), pp 131–146.Google Scholar
43 Houck, C.R., Joines, J.A. and Kay, M.G. Genetic algorithm for function optimization: A Matlab implementation, ACM Trans Math Softw, 1996, 22, pp 1–14.Google Scholar
44 Onkar, A.K., Upadhyay, C.S. and Yadav, D. Stochastic finite element buckling analysis of laminated plates with circular cutout under uniaxial compression, J Appl Mech, 2007, 74, (4), pp 798–809.Google Scholar