Design Optimization of Pulse Transformers in Series-Type Hybrid Circuit Breakers Using a Neural Network Based Surrogate Model
Bibliographic record
Abstract
Multi-objective optimization is often required in designing electromagnetic components, such as motors or transformers, under complicated trade-off considerations. Finite Element Method (FEM) simulations are usually conducted to achieve the optimal design with the penalty of long computation time. This paper presents a data-driven design optimization method that partially replaces FEM simulation with a deep neural network (DNN) based surrogate model. The proposed framework adopts the Non-dominated Sorting Genetic Algorithm II (NSGAII) method. Through the case study of a pulse transformer design in a series-type hybrid circuit breaker (S-HCB), we demonstrate that the new DNN- based optimization approach can significantly reduce the optimization time by sevenfold while preserving up to 98.66% of the average accuracy of the conventional FEM approach. The proposed optimization strategy is expected to benefit a wide range of engineering optimization problems that involve a large amount of computation.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".