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  • 01-01 Advanced Future Engine Technologies (Combines Combustion and Emissions, Novel Concepts, and Hybrid Aero Engines)
  • On the Shaft Speed Selection of Parallel Hybrid Aero Engines

59500 - On the Shaft Speed Selection of Parallel Hybrid Aero Engines 

The boosted turbo fan or parallel hybrid is a promising means to reduce fuel consumption of gas turbines on aircraft. With an electric drive on the low-pressure spool of the gas turbine, it requires a trade-off between the characteristics of the gas turbine and the electric power sub-systems. As the shaft speed is decreased, the gas turbine fan size can grow - for a given tip speed - and the fan pressure ratio may be reduced, which improves efficiency but increases weight and nacelle drag. At the same time, a lower shaft speed also makes the electrical drive more efficient but heavier. The objective of this paper is to expose some key aspects of this trade-off in terms of efficiency and weight. The paper applies sophisticated methodology in both addressed domains. For the gas turbine, multi-point design is used. Here, established synthesis matching schemes focusing on gas turbine performance parameters are extended with parameters from the sizing and weight estimation such as diameters and tip speeds. For the electrical machine, fully analytical sizing capturing the impact of cooling supply is used. The paper reports estimated gas path and machine geometries. It gives an understanding of the interactions between both sub-systems and allows concluding which low pressure spool speed gives the best instantaneous performance.

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On the Shaft Speed Selection of Parallel Hybrid Aero Engines

Paper Type

Technical Paper Publication

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Session: 01-01 Advanced Future Engine Technologies (Combines Combustion and Emissions, Novel Concepts, and Hybrid Aero Engines)

Paper Number: 59500

Start Time: June 7th, 2021, 09:45 AM

Presenting Author: Michael Sielemann

Authors: Michael Sielemann Modelon
Jesse Gohl Modelon
Xin Zhao Chalmers University of Technology
Konstantinos Kyprianidis Mälardalen University
Giorgio ValenteRomax Technology, Hexagon Manufacturing Intelligence
Sharmila Sumsurooah University of Nottingham













 

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