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Performance improvement of a compound helicopter rotor head by aerodynamic design optimisation of a blade-sleeve fairing

Part of: 31st Congress of The International Council of the Aeronautical Sciences (ICAS)

Published online by Cambridge University Press:  14 March 2019

P. Pölzlbauer Open the ORCID record for P. Pölzlbauer [Opens in a new window] ,
C. Breitsamter  and
D. Desvigne
P. Pölzlbauer*
Affiliation:
Technical University of Munich, Department of Mechanical Engineering, Chair of Aerodynamics and Fluid Mechanics, Garching, Germany
C. Breitsamter
Affiliation:
Technical University of Munich, Department of Mechanical Engineering, Chair of Aerodynamics and Fluid Mechanics, Garching, Germany
D. Desvigne
Affiliation:
Airbus Helicopters, Marseille-Provence International Airport, Marignane Cedex, France
*
[email protected]
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Abstract

Within the present publication, the rotor head of a compound helicopter known as Rapid And Cost-Effective Rotorcraft (RACER) is investigated. In particular, the aerodynamic design optimisation of the RACER blade-sleeve fairings (BSFs) is conducted. For this purpose, an isolated rotor head is generated featuring a full-fairing beanie, the BSF and a truncated rotor blade (RB). Moreover, a single RB is investigated at two different azimuthal rotor positions, which correspond to the advancing and the retreating RB case. For this purpose, an averaged circumferential velocity is determined in the blade-sleeve region and superposed with the RACER cruise speed in order to estimate the prevailing flow conditions. The automated aerodynamic design optimisation is performed by means of a previously developed optimisation tool chain. A global multi-objective genetic optimisation algorithm is applied for the given problem. During preliminary work, a 2D aerodynamic design optimisation of selected blade-sleeve sections was conducted. These optimised aerofoils represent the design variables for the current optimisation problem. The shape modification of the 3D fairing is realised by exchanging specific aerofoils at certain spanwise sections.

Type
Research Article
Information
The Aeronautical Journal , Volume 123 , Issue 1268 , October 2019 , pp. 1456 - 1475
Copyright
© Royal Aeronautical Society 2019 

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References

REFERENCES

Breitsamter, C., Grawunder, M. and Reß, R. Aerodynamic design optimization for a helicopter configuration including a rotating rotor head, Congress of the International Council of the Aeronautical Sciences. St. Petersburg, Russia, 2014, pp 0686.1–0686.11.Google Scholar
Desvigne, D. and Alfano, D. Rotor-head/fuselage interactional effects on helicopter drag: Influence of the complexification of the rotor-head geometry. European Rotorcraft Forum, Moscow, Russia, 2013.Google Scholar
Graham, D., Sung, D., Young, L., Louie, A. and Stroub, R. Helicopter hub fairing and pylon interference drag. Technical Memorandum 101052, NASA-Ames Research Center, Moffet Field, CA, US, 1989.Google Scholar
Sheehy, T. and Clark, D. A method for predicting helicopter hub drag. Final Report AD-A021 201, United Technologies Corporation, Stratford, CT, US, 1976.CrossRefGoogle Scholar
Sung, D., Lance, M., Young, L. and Stroub, R. An experimental investigation of helicopter rotor hub fairing drag characteristics. Technical Memorandum 102182, NASA-Ames Research Center, Moffet Field, CA, US, 1989.Google Scholar
Khier, W. Numerical analysis of hub and fuselage interference to reduce helicopter drag. European Rotorcraft Forum, Amsterdam, Netherlands, 2012.Google Scholar
Khier, W. Computational investigation of advanced hub fairing configurations to reduce helicopter drag. European Rotorcraft Forum, Southampton, UK, 2014.Google Scholar
Eurocopter: The x3 revolution. Rotor J, 2010, 88, pp 14–19.Google Scholar
Airbus Helicopters: Press-Release Airbus Helicopters, 2017. URL https://www.airbus.com/newsroom/press-releases/de/2017/06/Airbus-Helicopters-reveals-Racer-high-speed-demonstrator-configuration.html.Google Scholar
Pölzlbauer, P., Desvigne, D. and Breitsamter, C. Aerodynamic Design Optimization of a Helicopter Rotor Blade-Sleeve Fairing, Deutscher Luft- und Raumfahrtkongress, 2017, Munich, Germany.CrossRefGoogle Scholar
Arora, J. Introduction to Optimum Design. Elsevier Science, 2004, New York.CrossRefGoogle Scholar
Grawunder, M., Reß, R., Stein, V., Breitsamter, C. and Adams, N. Flow Simulation of a Five-Bladed Rotor Head. Springer International Publishing, 2014, Cham, pp 235–243.CrossRefGoogle Scholar
Böhm, W., Farin, G. and Kahmann, J. A survey of curve and surface methods in CAGD, Computer Aided Geometric Design, 1984, 1, (1), pp 1–60. DOI https://doi.org/10.1016/0167-8396(84)90003-7. URL http://www.sciencedirect.com/science/article/pii/0167839684900037.CrossRefGoogle Scholar
Salomon, D. Curves and Surfaces for Computer Graphics, Springer-Verlag New York, Inc., 2005, Secaucus, NJ, US.Google Scholar
Sederberg, T. Computer aided geometric design. Course notes, Department of Computer Science, Brigham Young University, 2016.Google Scholar
Goldberg, D. Genetic Algorithms in Search, Optimization and Machine Learning, 1st edn. Addison-Wesley Longman Publishing Co., Inc., 1989, Boston, MA, US.Google Scholar
Kalyanmoy, D. Multi-Objective Optimization Using Evolutionary Algorithms John Wiley & Sons, Inc., 2001, New York, NY, US.Google Scholar
Organization, I. C. A. Manual of the ICAO Standard Atmosphere Extended to 80 Kilometres (262 500 Feet), 3rd edn, International Civil Aviation Organization, 1993, Montreal, Quebec.Google Scholar
Menter, F. Two-equation eddy-viscosity turbulence models for engineering applications, AIAA J, 1994, 32, (8): 1598–1605; URL https://doi.org/10.2514/3.12149.CrossRefGoogle Scholar
ANSYS Inc. Fluent. User Guide v17.0, Ansys Inc., 2016, Canonsburg, PA, US.Google Scholar
Lakshmipathy, S. and Togiti, V. Assessment of alternative formulations for the specific-dissipation rate in RANS and variable-resolution turbulence models, 20th AIAA Computational Fluid Dynamics Conference, 2011, Honolulu, Hawaii.CrossRefGoogle Scholar
Gerhold, T. Overview of the hybrid rans code tau. MEGAFLOW – Numerical Flow Simulation for Aircraft Design 89 of Notes on Numerical Fluid Mechanics and Multidisciplinary Design, 2005, pp 81–92.CrossRefGoogle Scholar
Adams, B., Ebeida, M., Eldred, M., Jakeman, J., Maupin, K. and Monschke, J. Dakota, a multilevel parallel object-oriented framework for design optimization, parameter estimation, uncertainty quantification, and sensitivity analysis. User Manual v6.4, Sandia National Laboratories, 2016, Albuquerque, NM, US.Google Scholar