Determination of effective air-gap length of reluctance synchronous motors from experimental data
Bibliographic record
Abstract
Because of many advantages of reluctance synchronous motors (RSM) over other motors, their usage in AC drives have been gaining importance for many industry applications. The performance of RSM is greatly dependent on the effective air-gap lengths of the machine along its d and q axes. In this paper, an attempt has been made to obtain the effective air-gap lengths of the machine from the experimental values of direct and quadrature axis reactances by considering the higher permeance and winding space harmonics of a RSM. These reactances are then compared with the coefficients of the magnetizing inductances of the stator windings, obtained by using winding function approach (WFA), to determine the air-gaps. A comparative study has been carried out with different permeance and winding space harmonics. The q-axis air-gap length seems to be much more sensitive to the operating point and the leakage inductance compared to the d-axis air-gap length. Experimental values near no-load have been considered to minimize flux barrier effects, as WFA cannot simulate these effects without changing the length of the pole arc. The effective air-gaps of another salient pole synchronous machine with damper bars have also been determined and compared with various permeance and winding space harmonics, when run as a RSM (without field excitation). This machine did not have flux barriers and hence the results seem to be more accurate.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".