2020
DOI: 10.1049/iet-est.2018.5031
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Prediction of acoustic noise and vibration of a 24/16 traction switched reluctance machine

Abstract: This study presents a numerical modelling approach for the prediction of vibration and acoustic noise for a 24/16 traction switched reluctance machine (SRM). The numerical modelling includes the simulation of electromagnetic force in JMAG, the calculation of natural frequencies and the simulation of vibration and acoustic noise in ACTRAN. Considerations in the modelling of geometries, meshing and contacts of the 24/16 SRM are discussed to ensure the accuracy of the numerical simulation. Two-dimensional fast Fo… Show more

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Cited by 12 publications
(3 citation statements)
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“…The element size and radius of the near field meshing were calculated and carefully selected to ensure simulation accuracy. The calculation of the required element size and radius of the near-field meshing is presented in [7], [8].…”
Section: Acoustic Modeling Considerationsmentioning
confidence: 99%
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“…The element size and radius of the near field meshing were calculated and carefully selected to ensure simulation accuracy. The calculation of the required element size and radius of the near-field meshing is presented in [7], [8].…”
Section: Acoustic Modeling Considerationsmentioning
confidence: 99%
“…The numerical simulation of the vibration modes of the stator and the rotor is conducted in ACTRAN. The details of the numerical modeling are presented in [7]- [8]. Fig.…”
Section: A Rotor Radial Vibration Modesmentioning
confidence: 99%
“…The growing industrialization has led to the application of the SRM in various sectors like aviation, textile factories, robotics and electric vehicles [4][5][6]. However, the SRM encounters reliability and safety issues due to presence of doubly salient and switching power properties that added several demerits of higher torque ripples generation, noise and non-linear magnetic characteristics [7][8][9]. Also, nowadays, more research is going to design new control strategies to enhance the performance of the SRM as traditional techniques lag in terms of accuracy [10][11][12][13][14][15].…”
Section: Introductionmentioning
confidence: 99%