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Tiltrotor CFD Part II - aerodynamic optimisation of tiltrotor blades

Published online by Cambridge University Press:  05 May 2017

A. Jimenez-Garcia ,
M. Biava ,
G.N. Barakos ,
K.D. Baverstock ,
S. Gates  and
P. Mullen
A. Jimenez-Garcia
Affiliation:
CFD Laboratory, School of Engineering, James Watt South Building, University of Glasgow, Glasgow, United Kingdom
M. Biava
Affiliation:
CFD Laboratory, School of Engineering, James Watt South Building, University of Glasgow, Glasgow, United Kingdom
G.N. Barakos*
Affiliation:
CFD Laboratory, School of Engineering, James Watt South Building, University of Glasgow, Glasgow, United Kingdom
K.D. Baverstock
Affiliation:
Leonardo Helicopters, Aerodynamics Department, YeovilUnited Kingdom
S. Gates
Affiliation:
Leonardo Helicopters, Aerodynamics Department, YeovilUnited Kingdom
P. Mullen
Affiliation:
Leonardo Helicopters, Aerodynamics Department, YeovilUnited Kingdom
*
George.Barakos@glasgow.ac.uk
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Abstract

This paper presents aerodynamic optimisation of tiltrotor blades with high-fidelity computational fluid dynamics. The employed optimisation framework is based on a quasi-Newton method, and the required high-fidelity flow gradients were computed using a discrete adjoint solver. Single-point optimisations were first performed to highlight the contrasting requirements of the helicopter and aeroplane flight regimes. It is then shown how a trade-off blade design can be obtained using a multi-point optimisation strategy. The parametrisation of the blade shape allowed the twist and chord distributions to be modified and a swept tip to be introduced. The work shows how these main blade shape parameters influence the optimal performance of the tiltrotor in helicopter and aeroplane modes, and how an optimised blade shape can increase the overall tiltrotor performance. Moreover, in all the presented cases, the accuracy of the adjoint gradients resulted in a small number of flow evaluations for finding the optimal solution, thus indicating gradient-based optimisation as a viable tool for modern tiltrotor design.

Type
Research Article
Information
The Aeronautical Journal , Volume 121 , Issue 1239 , May 2017 , pp. 611 - 636
Copyright
Copyright © Royal Aeronautical Society 2017 

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References

REFERENCES

1. Jones, W.T., Nielsen, E.J., Lee-Rausch, E.M. and Acree, C.W. Multi-point adjoint-based design of tilt-rotors in a noninertial reference frame, Proceedings of the 10th AIAA Multidisciplinary Design Optimization Conference, 2014, AIAA, National Harbor, Maryland, US, pp 1-18.CrossRefGoogle Scholar
2. Droandi, G. and Gibertini, G. Aerodynamic shape optimisation of a proprotor and its validation by means of CFD and experiments, Aeronautical J, 2015, 119, (1220), pp 1223-1250.CrossRefGoogle Scholar
3. Johnson, W. Helicopter Theory, Princeton University Press, New Jersey, 1980.Google Scholar
4. McCormick, B.W. Aerodynamics Aeronautics and Flight Mechanics, 1979, Wiley, New York, US.Google Scholar
5. Lyu, Z., Xu, Z. and Martins, J.R.R.A. Benchmarking optimization algorithms for wing aerodynamic design optimization, Proceedings of the 8th International Conference on Computational Fluid Dynamics (ICCFD8), Paper No. ICCFD8-2014-0203, 2014, Chengdu, China, pp 1-18.Google Scholar
6. Zingg, D.W., Nemec, M. and Pulliam, T.H. A comparative evaluation of genetic and gradient-based algorithms applied to aerodynamic optimization, European J Computational Mechanics, 2008, 17, (1), pp 103-126, DOI: 10.3166/remn.17.103-126.CrossRefGoogle Scholar
7. Walsh, J., Bingham, G. and Riley, M. Optimization methods applied to the aerodynamic design of helicopter rotor blades, Proceedings of the 26th AIAA/ASME/ASCE/AHS Structures, Structural Dynamics and Materials Conference, 1985, AIAA, Orlando, Florida, US.CrossRefGoogle Scholar
8. Zibi, J., Defresne, G. and Costes, M. A numerical procedure for aerodynamic optimization of helicopter rotor blades, Proceedings of the 18th European Rotorcraft Forum Proceedings, 1992, ERF, Avignon, France.Google Scholar
9. Pape, A.L. and Beaumier, P. Numerical optimization of helicopter rotor aerodynamic performance in hover, Aerospace Science and Technology, 2005, 9, (3), pp 191-201, DOI: 10.1016/j.ast.2004.09.004.CrossRefGoogle Scholar
10. Choi, S., Potsdam, M., Lee, K., Iaccarino, G. and Alonso, J.J. Helicopter rotor design using a time-spectral and adjoint-based method, AIAA J, 2008, 51, (2), pp 412-423, DOI: 10.2514/6.2008-5810.Google Scholar
11. Dumont, A., Pape, A.L., Peter, J. and Huberson, S. Aerodynamic shape optimization of hovering rotors using a discrete adjoint of the Reynolds-averaged Navier-Stokes equations, J American Helicopter Society, 2011, 56, (3), pp 1-11, DOI: 10.4050/JAHS.56.032002.CrossRefGoogle 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, 1998, 12, (2), pp 173-192, DOI: 10.1017/S0890060498122084.CrossRefGoogle 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, DOI: 10.1023/A:1012817410590.CrossRefGoogle Scholar
14. Imiela, M. High-fidelity optimization framework for helicopter rotors, Aerospace Science and Technology, 2011, 23, (1), pp 2-16, DOI: 10.1016/j.ast.2011.12.011.CrossRefGoogle Scholar
15. Johnson, C.S. and Barakos, G.N. Optimising aspects of rotor blades in forward flight, Proceedings of the 49th Aerospace Sciences Meeting Including the New Horizons Forum and Aerospace Exposition, 2011, AIAA, Orlando, Florida, US, pp 1-28.CrossRefGoogle Scholar
16. Cho, J. and Lee, S.-C. Propeller blade shape optimization for efficiency improvement, Computer and Fluids, 1998, 27, (3), pp 407-419, DOI: 10.1016/S0045-7930(97)00035-2.CrossRefGoogle Scholar
17. Marinus, B.G., Roger, M. and der Braembussche, R.A.V. Aeroacoustic and aerodynamic optimization of aircraft propeller blades, Proceedings of the 16th AIAA/CEAS Aeroacoustic Conference, 2010, AIAA, Stockholm, Sweden, pp 1-17.CrossRefGoogle Scholar
18. Leon, E.R., Le Pape, A., Desideri, J.A., Alfano, D. and Costes, M. Concurrent aerodynamic optimization of rotor blades using a nash game method, Proceedings of the 69th American Helicopter Society (AHS), Paper No. 69-2013-168, 2013, Phoenix, Arizona, US, pp 1-12.Google Scholar
19. Wilke, G. Applying multi-objective variable-fidelity optimization techniques to industrial scale rotors: Blade designs for CleanSky, Proceedings of the 41st European Rotorcraft Forum (ERF), Paper No. 41-2015-0038, 2015, Munich, Germany, pp 1-14.Google Scholar
20. Droandi, G. and Gibertini, G. Aerodynamic blade design with multi-objective optimization for tiltrotor aircraft, Aircraft Engineering and Aerospace Technology, 2013, 87, (1), pp 19-29, DOI: https://doi.org/10.1108/AEAT-01-2013-0005.CrossRefGoogle Scholar
21. Massaro, A. and D’Andrea, A. Multi-point aerodynamic optimization by means of memetic algorithm for design of advanced tiltrotor blades, Proceedings of the 39th European Rotorcraft Forum, 2013, ERF, Moscow, Russia, pp 1-14.Google Scholar
22. Booth, E.R., McCluer, M. and Tadghighi, H. Acoustic characteristics of an isolated tiltrotor model in the DNW, Proceedings of the 55th American Helicopter Society, 1999, AHS, Annual Forum, Montreal, Canada, pp 1-20.Google Scholar
23. Anderson, W.K. and Bonhaus, D.L. An implicit upwind algorithm for computing turbulent flows on unstructured grids, Computers and Fluids, 1994, 23, (1), pp 1-21, DOI: 10.1016/0045-7930(94)90023-X.CrossRefGoogle Scholar
24. Nielsen, E.J. Aerodynamic Design Sensitivities on an Unstructured Mesh Using the Navier-Stokes Equations and a Discrte Adjoint Formulation, PhD Thesis, Virginia Polytechnic Institute and State University, Department of Aerospace and Ocean Engineering, December 1998.CrossRefGoogle Scholar
25. Biava, M., Woodgate, M. and Barakos, G.N. Fully implicit discrete adjoint methods for rotorcraft applications, AIAA J, 2016, 54, (2), pp 735-749.CrossRefGoogle Scholar
26. Biava, M. and Barakos, G.N. Optimisation of ducted propellers for hybrid air vehicles using high-fidelity CFD, Aeronautical J, 2016, 120, (1232), pp 1632-1657.CrossRefGoogle Scholar
27. Kraft, D. Algorithm 733: TOMP-Fortran Modules for Optimal Control Calculations, ACM Transactions on Mathematical Software, 1994, 20, (3), pp 262-281.CrossRefGoogle Scholar
28. Lawson, S.J., Steijl, R., Woodgate, M. and Barakos, G.N. High performance computing for challenging problems in computational fluid dynamics, Progress in Aerospace Sciences, 2012, 52, (1), pp 19-29, DOI: 10.1016/j.paerosci.2012.03.004.CrossRefGoogle Scholar
29. Steijl, R. and Barakos, G.N. Sliding mesh algorithm for CFD analysis of helicopter rotor-fuselage aerodynamics, Int J for Numerical Methods in Fluids, 2008, 58, (5), pp 527-549, DOI: 10.1002/d.1757.CrossRefGoogle Scholar
30. Steijl, R., Barakos, G.N. and Badcock, K. A framework for CFD analysis of helicopter rotors in hover and forward flight, Int J Numerical Methods in Fluids, 2006, 51, (8), pp 819-847, DOI: 10.1002/d.1086.CrossRefGoogle Scholar
31. Leishman, J.G. Principles of Helicopter Aerodynamics, Cambridge, 2000.Google Scholar
32. Johnson, W. Calculation of the aerodynamic behavior of the tilt rotor aeroacoustic model (TRAM) in the DNW, Proceedings of the 57th American Helicopter Society Annual Forum, 2001, AHS, Washington, D.C., US.Google Scholar
33. Acree, C. Rotor design options for improving XV-15 Whirl-Flutter stability margins, NASA TP–2004-212262, March 2004, Ames Research Center, Moffett Field, California, US.Google Scholar
34. Maisel, M.D., Giulianetti, D.J. and Dugan, D.C. The history of the XV-15 tilt rotor research aircraft: From concept to flight, NASA SP-2000-4517, 2000, Washington, DC, US.Google Scholar
35. Menter, F.R. Two-equation Eddy-Viscosity turbulence models for engineering applications, AIAA J, 1994, 32, (8), pp 1598-1605, DOI: 10.2514/3.12149.CrossRefGoogle Scholar
36. Guntur, S. and Sorensen, N. An evaluation of several methods of determining the local angle of attack on wind turbine blades, Proceedings of the Science of Making Torque from Wind, 2012, Oldenburg, Germany, pp 1-11.Google Scholar