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**Harvard**

Capitao Patrao, A., Grönstedt, T., Avellán, R., Lundbladh, A. och Montero Villar, G. (2016) *An Optimization Platform for High Speed Propellers*.

** BibTeX **

@conference{

Capitao Patrao2016,

author={Capitao Patrao, Alexandre and Grönstedt, Tomas and Avellán, Richard and Lundbladh, Anders and Montero Villar, Gonzalo},

title={An Optimization Platform for High Speed Propellers},

booktitle={Swedish Aerospace Technology Congress 2016},

abstract={To improve the efficiency by which current power plants translate jet energy into useful thrust the use of turboprop and in particular open rotor aircraft are being revisited. One challenge in association with developing new powerplants for such aircraft is high speed propeller design in general and noise prediction in particular.
The Boxprop was invented in 2009 by GKN Aerospace in order to mitigate the effects of the tip vortex on noise and to improve upon the aerodynamics of a conventional propeller blade. The Boxprop is composed of a double-bladed propeller joined at the tips, and the design has the potential to eliminate the tip vortex, and thereby decrease that particular noise source. The complex and highly three-dimensional shape of an advanced propeller blade is challenging to model with classical propeller design methods, requiring instead more sophisticated optimization methods.
This paper presents an optimization platform developed for high speed propellers, and illustrates its use by performing a reduced aerodynamic optimization of the Boxprop. The optimization process starts by performing a Latin Hypercube Sampling of the design space, and analyzes the resulting geometries using CFD. A meta-model employing radial basis functions is then used to interpolate on the obtained CFD results, which the GA uses to find optimal candidates along the obtained Pareto front. These designs are then evaluated using CFD, and their data added to the meta-model. The process iterates until the meta-model converges.
The results of this paper demonstrate the capability of the presented optimization platform, and applying it on the Boxprop has resulted in valuable design improvements and insights. The obtained designs show less blade interference, more efficiently loaded blades, and less produced swirl. The methodology for geometry generation, meshing and optimizing is fast, robust, and readily extendable to other types of optimization problems, and paves the way for future collaborative research in the area of turbomachinery.
},

year={2016},

}

** RefWorks **

RT Conference Proceedings

SR Electronic

ID 243255

A1 Capitao Patrao, Alexandre

A1 Grönstedt, Tomas

A1 Avellán, Richard

A1 Lundbladh, Anders

A1 Montero Villar, Gonzalo

T1 An Optimization Platform for High Speed Propellers

YR 2016

T2 Swedish Aerospace Technology Congress 2016

AB To improve the efficiency by which current power plants translate jet energy into useful thrust the use of turboprop and in particular open rotor aircraft are being revisited. One challenge in association with developing new powerplants for such aircraft is high speed propeller design in general and noise prediction in particular.
The Boxprop was invented in 2009 by GKN Aerospace in order to mitigate the effects of the tip vortex on noise and to improve upon the aerodynamics of a conventional propeller blade. The Boxprop is composed of a double-bladed propeller joined at the tips, and the design has the potential to eliminate the tip vortex, and thereby decrease that particular noise source. The complex and highly three-dimensional shape of an advanced propeller blade is challenging to model with classical propeller design methods, requiring instead more sophisticated optimization methods.
This paper presents an optimization platform developed for high speed propellers, and illustrates its use by performing a reduced aerodynamic optimization of the Boxprop. The optimization process starts by performing a Latin Hypercube Sampling of the design space, and analyzes the resulting geometries using CFD. A meta-model employing radial basis functions is then used to interpolate on the obtained CFD results, which the GA uses to find optimal candidates along the obtained Pareto front. These designs are then evaluated using CFD, and their data added to the meta-model. The process iterates until the meta-model converges.
The results of this paper demonstrate the capability of the presented optimization platform, and applying it on the Boxprop has resulted in valuable design improvements and insights. The obtained designs show less blade interference, more efficiently loaded blades, and less produced swirl. The methodology for geometry generation, meshing and optimizing is fast, robust, and readily extendable to other types of optimization problems, and paves the way for future collaborative research in the area of turbomachinery.

LA eng

LK http://publications.lib.chalmers.se/records/fulltext/243255/local_243255.pdf

OL 30