Numerical Multi-Phase Thermal Analysis to Determine End-Winding Heat Transfer Coefficient of an Interior Permanent Magnet Motor
Bibliographic record
Abstract
Permanent magnet synchronous motors (PMSMs) are widely used in electrified vehicles due to their high-power density, high torque capability, high energy efficiency over a wide range of speed within a compact design. Thermal analysis is of primary importance for the design of an electric machine. Cooling mechanisms influence the machine's electromagnetic performance, durability, and reliability. Computational Fluid Dynamics (CFD) and Lumped Parameter Thermal Network (LPTN) are the most common techniques for analyzing thermal performance. Lumped parameter thermal networks (LPTNs) have proven to reduce the simulation time compared to computational fluid dynamics (CFD). However, implementing CFD simulations to calculate the heat transfer coefficients improves the accuracy of LPTN results. In this paper, a multi-phase oil splash CFD modeling was performed to determine the end-winding heat transfer coefficient using Ansys CFX, considering the rotational effect of the motor. The volume of fluid (VOF) and multiple reference frame (MRF) techniques are implemented.
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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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".