Integrated Electrical System Model of a More Electric Aircraft Architecture
Bibliographic record
Abstract
A primary challenge in performing integrated system simulations is balancing system simulation speeds against the model fidelity of the individual components composing the system model. Traditionally, such integrated system models of the electrical systems on more electric aircraft (MEA) have required drastic simplifications, linearizations, and/or averaging of individual component models. Such reductions in fidelity can take significant effort from component engineers and often cause the integrated system simulation to neglect critical dynamic behaviors, making it difficult for system integrators to identify problems early in the design process. This paper utilizes recent advancements in co-simulation technology (DHS Links) to demonstrate how integrated system models can be created wherein individual component models do not require significant simplification to achieve reasonable integrated model simulation speeds. Such techniques enable the system integrator to observe system dynamics and interactions at fidelities which were previously impractical. This paper utilizes the electrical power system of an MEA to illustrate the capabilities and performance of the proposed approach. Specifically, the paper identifies the system modeling approach, addresses key challenges which were overcome to enable system level modeling at this fidelity, discusses the component models, and presents results from the integrated system model.
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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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.003 |
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".