Real-Time Simulation of a More Electric Aircraft Power Generation and Distribution System
Bibliographic record
Abstract
More Electric Aircraft (MEA) technology is leading aircraft manufacturers to replace traditional hydraulic and pneumatic systems by electrical components, resulting in weight and maintenance cost reduction, and in the increase of Mean Time Between Failures (MTBF). This has for effect of increasing the complexity of the Electrical Power Generation and Distribution System (EPGDS) of the aircraft and degrading its power quality. As a result, testing and validation must be performed early in the design stages, which has traditionally been done via the use of physical testbeds which involve significant amount of hardware. However, because of the electrical nature of MEA, virtual testbeds are now increasingly used, which offer greater flexibility and are less costly than conventional testbeds. As such, OPAL-RT is developing real-time simulators that integrate MEA systems models into a real-time co-simulation platform. This paper provides simulation results that showcase OPAL-RT's real-time simulation capabilities in the context of EPGDS simulation, applied to a generic example inspired from public domain publication regarding the Boeing 787 EPGDS.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".