High-Power and -Speed Induction Machines Iron Loss Calculation Incorporating the Electro-Thermal Impact
Bibliographic record
Abstract
This paper deals with the iron loss calculation and electro-thermal characterization of high-power and -speed induction machines (HSIM). In higher operating points, the iron loss may become increasingly higher as the frequency increases, and it necessitates meticulous consideration in machine design and analysis. The ever-higher frequencies and thermal limitation of components demand the fast and accurate power loss computation for an optimal design accounting for stringent electro-thermal limitations. To this aim and to circumvent the computational intensity of finite element analysis (FEA), an advanced analytical iron loss calculation method is developed for HSIM, wherein both magnetic and thermal field effects are concurrently considered. Prior works predominantly studied these aspects independently, while it is crucial to examine the interaction of these two strongly coupled physics in complex systems like HSIM. The developed model is showcased using a 94 kW, 14,000 r/min HSIM and validated FEA results. It demonstrated excellent accuracy across different operating conditions. This approach provides valuable insights for optimal design and performance improvement in HSIMs, with a focus on achieving a fast and accurate alternative to FEA that encompasses both electromagnetic and thermal factors.
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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 | 0.001 |
| Open science | 0.001 | 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".