Neural network-driven optimization of electromagnetic and thermal performance in traction induction machines through rotor design modifications
Bibliographic record
Abstract
Squirrel-cage induction machines (SCIMs) are widely used in traction and industrial applications owing to their robustness, simple construction and cost-effectiveness. However, temperature rise within the machine can negatively impact performance, reduce reliability and shorten operational lifespan, making thermal considerations essential during the design process. Traditional methods for optimizing rotor bar number and shape focus on electromagnetic performance, often overlooking thermal effects, limiting practical effectiveness. Considering both electromagnetic and thermal behaviours substantially increases computational demands, making iterative finite element analysis (FEA) impractical. This article introduces a neural network-based modelling and optimization framework for SCIMs in traction applications. By evaluating multiple rotor bar configurations under fixed design parameters, the framework efficiently refines rotor bar dimensions, enhancing performance while controlling losses and temperature. Generalizability is demonstrated through a case study with distinct specifications. Benchmarking against direct FEA optimization shows substantial computational savings with comparable accuracy, offering an effective approach for thermally resilient machine design.
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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) | 0.001 | 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 | 0.001 | 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".