Multi-Source Dual Active Bridge DC/DC Converter for More Electric Aircraft
Bibliographic record
Abstract
The topology proposed in this paper aims for a multiple sources/loads integration to improve the power density of the electrical power distribution system (EPDS) of the more-electric aircraft (MEA) architecture. The different operating modes of the proposed topology add flexibility to the system enabling power flow between different sources, while controlling the power supplied to the low voltage (LV) network on the aircraft. Better system utilization is achieved as compared to conventional solutions. The system stability is improved considering that the multi-source DAB (MSDAB) topology can be configured to stabilize the high-voltage DC (HVDC) bus in case of voltage sag. A battery charging configuration utilizing the magnetizing inductance of the transformer is proposed and discussed as one of the operating modes of the converter. The power flow to the low-voltage bus network is controlled by phase shifting the gating signals between the two bridges. A design process using genetic algorithm (GA) optimization is introduced to optimally select the converter parameters to minimize the transformer RMS current. Simulation and experiments of the control schemes were performed to validate optimal operation of the proposed 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) | 0.004 | 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".