Acoustic noise mitigation, modal characterization, and rotor fatigue calculations in electric propulsion motors
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".