Back EMF, Torque–Angle, and Core Loss Characterization of a Variable-Flux Permanent-Magnet Machine
Bibliographic record
Abstract
An appropriate torque-angle selection can improve the torque-to-current ratio of a machine, converter size, and provide an optimal motor operation. A precise information of the back electromotive force (EMF) helps estimating the magnet flux linkage. An accurate determination of the core loss leads to a better machine design and efficiency estimation. This paper presents the back EMF, flux linkage, torque-angle, and core loss characterization of a variable-flux permanent-magnet machine. The magnetic properties of AlNiCo 9 magnet and the process of magnetization and demagnetization are also described. The no-load back EMF, torque-angle curves, and no-load core losses are measured and simulated for a 7.5-hp variable-flux machine for three different magnetization levels. Static torque-angle curves are obtained by varying the current advance angle and the rotor position. Simulations are performed using three different machine design softwares to validate the design, software accuracy, and machine models. The core losses are also obtained using an analytical method, which is first utilized to calculate losses in M19G29 laminations, and then implemented to estimate the core losses of the prototyped variable-flux machine. The experimental results are found to be in a good agreement with the simulation and the core losses compared well with the analytical data.
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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) | 0.001 | 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 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".