CEAS Aeronautical Journal

, Volume 10, Issue 3, pp 665–685 | Cite as

Aerodynamic design optimization of a helicopter rotor blade-sleeve fairing

  • P. PölzlbauerEmail author
  • D. Desvigne
  • C. Breitsamter
Original Paper


Within the Clean Sky 2 project full fairing rotor head aerodynamic design optimization (FURADO), the aerodynamic design optimization of a full-fairing rotor head by means of CFD simulations is conducted. The rotor head is developed for a novel compound helicopter known as rapid and cost-effective rotorcraft (RACER). The FURADO project deals with three main components, the blade-sleeve fairing, the full-fairing beanie and the pylon fairing. Within the scope of the present publication, the two-dimensional aerodynamic design optimization of the RACER blade-sleeve fairing sections is at focus. The optimized two-dimensional shapes represent the supporting structure for the three-dimensional blade-sleeve fairing. For the optimization purpose, a sophisticated optimization tool chain allowing for automated aerodynamic design optimization is developed and the demonstration of its functionality is presented. The actual tool chain represents a modular software package, which has been developed within the FURADO project. In the first part of the publication, an introduction to the tool chain and the applied methods is given and subsequently, the results concerning the design optimization of a selected rotor blade-sleeve section are illustrated. The modules of the tool chain comprise state of the art software regarding computer aided design, mesh generation and numerical flow simulation. Concerning the optimization approach, a multi-objective genetic algorithm is employed to cover a wide range of the design space and to allow for the independent evaluation of multiple objective functions without weighting. Further, the gradual development of the Pareto-frontier can be observed and the most promising designs are thoroughly investigated.


Design optimization CFD Helicopter aerodynamics Blade-sleeve fairing Drag reduction RACER 


\((C_{\text {d}})_{\text {ref}}\)

Reference drag coefficient (Symmetric reference geometry)

\((C_{\text {d}})_{\text {rel}}\)

Normalized drag coefficient, \((C_{\text {d}})_{\text {rel}}=\frac{C_{\text {d}}}{|(C_{\text {d}})_{\text {ref}}|}\)

\((C_{\text {l}})_{\text {ref}}\)

Reference lift coefficient (Symmetric reference geometry)

\((C_{\text {l}})_{\text {rel}}\)

Normalized lift coefficient, \((C_{\text {l}})_{\text {rel}}=\frac{C_{\text {l}}}{|(C_{\text {l}})_{\text {ref}}|}\)


Angle of attack


Thermal conductivity

\(\mu _{\infty }\)

Dynamic viscosity


Kinematic viscosity


Azimuthal rotor blade position

\(\rho _{\infty }\)


\(\tau _{\text {w}}\)

Wall shear stress


Design variable vector


Optimization objective vector, \(\mathbf {z}=f(\mathbf {x})\)


Bézier curve function


Bernstein basis polynomial

\({B}_{{i}}\text {P}_{{jx}}\)

x-position of the Bézier control point

\({B}_{{i}}\text {P}_{{jz}}\)

z-position of the Bézier control point


Chord length

\({C}_{{\text {d}}}\)

Drag coefficient, \(C_{\text {d}}=\frac{D}{q_{\infty }\cdot S_{\text {ref}}}\)

\({C}_{{\text {f}}}\)

Skin-friction coefficient, \(C_{f}=\frac{\tau _{\text {w}}}{q_{\infty }}\)

\({C}_{{\text {l}}}\)

Lift coefficient, \(C_{\text {l}}=\frac{L}{q_{\infty }\cdot S_{\text {ref}}}\)

\({C}_{{\text {p}}}\)

Pressure coefficient, \(C_{{\text{p}}} = \frac{{p - p_{\infty } }}{{q_{\infty } }}\)

\({C}_{{\text {p}}}\)

Specific heat capacity






Objective function


Inequality constraint


Equality constraint






Mach number


Rotor speed

\({p}_{{\infty }}\)

Ambient pressure


Non-dominated set of candidates


Bézier curve control point

\({q}_{{\infty }}\)

Dynamic pressure, \(q_{\infty }=\frac{\rho _{\infty }\cdot V_{\infty }^{2}}{2}\)


Rotor radius




Reference area, \(S_{\text {ref}}=c\cdot 0.001m\)


Total simulation time


Local coordinate

\({T}_{{\infty }}\)

Ambient temperature


Time-averaging interval


Turbulence intensity, \({\text{Tu}}=\frac{\sqrt{\frac{2}{3}k}}{U}\)


Mean velocity, \(U=\sqrt{U_{x}^{2}+U_{y}^{2}+U_{z}^{2}}\)


Friction velocity, \(u_{\tau }=\sqrt{{\tau _{\text {w}}}/{\rho }}\)


Velocity magnitude

\({V}_{\infty }\)

Freestream velocity

x, y, z

Cartesian coordinates


Dimensionless wall distance, \(y^{+}=\frac{u_{\tau }\cdot y}{\nu }\)



The authors would like to thank the European Union for the funding of the FURADO research project within the framework of Clean Sky 2 and under the Grant agreement number 685636. Further, the fruitful collaboration and valuable support of the project partner Airbus Helicopters is highly acknowledged. Special thanks are addressed to ANSYS for providing the flow simulation software. The authors gratefully acknowledge the Gauss Centre for Supercomputing e.V. ( for funding this project by providing computing time on the GCS Supercomputer SuperMUC at Leibniz Supercomputing Centre (


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Copyright information

© Deutsches Zentrum für Luft- und Raumfahrt e.V. 2018

Authors and Affiliations

  1. 1.Technical University of MunichGarchingGermany
  2. 2.Airbus HelicoptersMarignane CedexFrance

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