Low Resistance Heat Paths Application to Electric Machines Rotor Cooling
Bibliographic record
Abstract
Thermal non-uniformities in electric machines manifested as local hotspots and high temperature gradients, pose significant risks to machine safety, leading to heightened thermal stresses and an increased likelihood of component failures. In this research, a newly developed 150 kW high-speed machine exhibited significant thermal imbalances, leading to hot spots near bearing seats. To mitigate these challenges, the paper proposes the use of integrated low-resistance heat paths in the steel rotor, replacing traditional void entities with lightweight, highly conductive materials such as aluminum or copper. Computational Fluid Dynamics (CFD) simulations demonstrate the effectiveness of an aluminum insert, resulting in a significant 47°C reduction in maximum shaft temperature and improved thermal uniformity. Structural analysis guides the optimization of fit parameters, defining a transition fit for maximum stress within yield strength. This comprehensive approach offers a strategic solution for enhancing rotor thermal management in high-speed electrical machines.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".