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Acoustic noise mitigation, modal characterization, and rotor fatigue calculations in electric propulsion motors

Electric propulsion motors have emerged as a promising solution to address greenhouse gas emissions from Internal Combustion Engines (ICEs). While electric propulsion motors offer numerous advantages over Internal Combustion Engines (ICEs), they also pose certain challenges. Electric motors are prone to high-frequency tonal noise, which can be annoying to customers and become a quality concern in noise-sensitive automotive applications. The ongoing effort to increase the speed of electric propulsion motors for enhanced power density can have an adverse impact on rotors. This is due to the fact that the stress induced in the rotor is quadratically proportional to its speed. This concern becomes particularly significant for motors that rely on air barriers and thin bridges to enhance their electromagnetic performance. The thesis makes a contribution to address these challenges. First, the acoustic noise mitigation methods at the transmission stage are investigated. Then, acoustical materials are experimentally validated for their capacity to mitigate acoustic noise at the transmission stage. Then, experimental modal analysis is conducted to find out the modal characteristics of a stator-housing assembly. The mode shapes and modal frequency are compared with finite element results to evaluate the fidelity of the finite element model. Then, an equivalent damage approach is used to employ accelerated fatigue analysis for a rotor using constant amplitude load cycles. Finally, a thermomechanical fatigue analysis workflow is developed for a rotor to overcome the limitations of the constant amplitude load cycle approach, with an additional computational cost. / Thesis / Doctor of Philosophy (PhD)

Identiferoai:union.ndltd.org:mcmaster.ca/oai:macsphere.mcmaster.ca:11375/29696
Date January 2024
CreatorsAshish Kumar Sahu
ContributorsDr. Ali Emadi, Dr. Berker Bilgin, Mechanical Engineering
Source SetsMcMaster University
LanguageEnglish
Detected LanguageEnglish
TypeThesis

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