Design Optimization Methodology for Planar Transformers for More Electric Aircraft
Bibliographic record
Abstract
Isolated DC-DC converters are considered the building blocks of modern aircraft electrical power networks. The high-frequency transformer utilized in such converters is the major contributor to the size and weight besides the thermal management system. In this paper, an optimization design methodology aims to minimize the transformer core size and improve the converter performance through optimized winding configurations. The transformer core selection is based on optimizing the maximum flux density while considering different magnetic materials and number of cores in parallel. The trade-offs between the converter efficiency and core weight in selecting an optimum switching frequency are presented. Multi-layer minimum gradient (MLMG) winding configurations are proposed to eliminate the high-frequency oscillations (HFO) caused by the transformer parasitics. The proposed configurations resulted in a reduction of the intra-winding capacitance by 15 times with 20$\%$improvement in the transformer volume as compared to a similar conventional double-layers spiral configurations. Numerical simulations are performed in ANSYS Maxwell to validate the proposed design. The effect of the different configurations on the converter performance is verified in the PLECS simulation environment. PLECS simulation results are validated experimentally for the conventional and proposed configurations highlighting the improvements on the performance of the converter.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".