Transient thermal analysis of a copper rotor induction motor using a lumped parameter temperature network model
Bibliographic record
Abstract
Due to the advantages of higher efficiency, low manufacturing cost and lower machine weight, Copper Rotor Induction Motor (CRIM) is a suitable cost effective alternative choice over permanent magnet motor in EV/HEV traction applications [1]. However, temperature rise issue is a critical factor that has direct effects on machine parameters such as effective resistances and inductances as well as magnetic properties of the machine materials. In this paper, a lumped parameter thermal network (LPTN) model is proposed to predict transient thermal behaviour in a Totally Enclosed Fan Cooled (TEFC) CRIM considering non-existent of forced convection heat transfer in stator end-winding due to smooth rotor geometry. The model also takes into consideration of various losses as heat sources that are determined from motor loading experiments. In order to validate thermal model, a 20-hp CRIM is tested under varying speed and loading conditions to measure the actual operating temperature rise and compared with calculated temperature rise.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 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".