Magnetization and Demagnetization Energy Estimation and Torque Characterization of a Variable-Flux Machine
Bibliographic record
Abstract
This paper examines the required energy for the magnetization and demagnetization of magnets in a spoke type AlNiCo-based variable-flux machine and studies the torque characteristics of this machine at different magnetization levels. The low coercivity magnet in this machine can be magnetized or demagnetized using a short-time current pulse with negligible Ohmic loss. An advanced method is proposed to estimate the required energy for magnet demagnetization or magnetization to a specific level. A test procedure is developed to measure the energy that is injected to the variable-flux machine during the demagnetization and magnetization procedures. Since this machine has the ability to operate at various magnetization levels, it is of great importance to obtain the torque characteristics such as torque mean value, the peak to peak value of the torque, as well as the torque ripple, at different operating conditions. A static torque measurement test procedure using a variable speed drive system is developed to measure the torque waveform of the variable-flux machine at different magnetization levels. The verified finite element model of the variable-flux machine is used to analyze the harmonic content of the back-emf and the no-load air gap flux density at different levels of magnetization.
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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 | 0.001 | 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".