Induction Machine Efficiency Evaluation Using the Finite Element Analysis Software and a New Mechanical Loss Formula
Bibliographic record
Abstract
The ability to consider saturation of core magnetic materials, skewed rotor bars, stator winding distribution and leakage fluxes has led to the widespread use of finite element method (FEM) in analyzing the performance of different electric machines. However, due to the FEM's inability to determine mechanical losses, stray load loss and core loss of the machine, FEM results are not reliable for efficiency estimation. This paper provides some useful points to evaluate efficiency of electric machine at different loads. For this purpose, losses of the machine are directly calculated by utilizing FEM results and empirical formulas. Furthermore, by investigating more than 100 IMs with 4 poles at different rated power, a new formula based on the rated power of the machine is proposed to estimate the mechanical losses. The new formula is used to improve the accuracy of estimated efficiencies. Experimental results are utilized to validate the proposed formula.
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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 | 0.001 | 0.000 |
| 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) | 0.004 | 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".