Electrical machines for aerospace applications are characterized by their high-power-density and fault tolerance capability. Therefore, PM machines with high current densities are the widely used option for such application. This paper presents a study of the influence of the manufacturing and drive effects on a PM machine designed for high-speed, high- power-density aerospace flooded pump application. Three models have been created, the original model (ORI), a model with the properties of the characterized stator core material (CHM), and the final model with the characterized material and the high-speed measured current waveform, i.e. drive effect, (CHMDE). A global genetic algorithm multi-physics optimization consisting of electromagnetic, mechanical and thermal models has been used to optimize machines. Finally, a comparison between the three optimized models and the original machine is given.

(2021). Influence of manufacturing and drive effects in high-speed, high-power-density pm machine for flooded pump application . Retrieved from http://hdl.handle.net/10446/224322

Influence of manufacturing and drive effects in high-speed, high-power-density pm machine for flooded pump application

Giangrande, P.;
2021-01-01

Abstract

Electrical machines for aerospace applications are characterized by their high-power-density and fault tolerance capability. Therefore, PM machines with high current densities are the widely used option for such application. This paper presents a study of the influence of the manufacturing and drive effects on a PM machine designed for high-speed, high- power-density aerospace flooded pump application. Three models have been created, the original model (ORI), a model with the properties of the characterized stator core material (CHM), and the final model with the characterized material and the high-speed measured current waveform, i.e. drive effect, (CHMDE). A global genetic algorithm multi-physics optimization consisting of electromagnetic, mechanical and thermal models has been used to optimize machines. Finally, a comparison between the three optimized models and the original machine is given.
2021
Al-Ani, M.; Al-Timimy, A.; Giangrande, Paolo; Degano, M.; Lindh, P.; Zhang, H.; Galea, M.; Gerada, C.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10446/224322
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