An Optimized GaN-Based DAB Converter for More Electric Aircraft
Bibliographic record
Abstract
Reliability, efficiency, and control optimization are the key features of modernized aircraft. This paper proposes a control algorithm across various voltages and load conditions that maximizes the power transmission efficiency between the high voltage DC (HVDC) link and the low voltage (LV) network aboard the aircraft. The algorithm is developed for a Gallium Nitride (GaN)-based dual active bridge (DAB) converter, for more electric aircraft (MEA). GaN is considered for maximized efficiency, weight reduction and improved thermal performance. The dual phase shift (DPS) and extended phase shift (EPS) modulation techniques are optimized using Genetic Algorithm (GA) and verified through simulation. The optimization algorithm aims at minimizing the backflow power, peak current, and converter losses. Efficiency results of the DAB converter are presented and compared under different modulation techniques. The results are validated on a 4 kW GaN-Silicon (Si) DAB 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 | 0.000 | 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) | 0.001 | 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".