Modular Assembly and Real-Time Hardware Emulation of On-the-Move Multidomain Multimachine System on More-Electric Aircraft
Bibliographic record
Abstract
Multi-domain and multi-machine are two significant features of the on-the-move powertrains on more electric aircraft (MEA). To successfully simulate the dynamic behaviors of MEA, not only should the multi-disciplinary characteristics be incorporated, but their interfacing issue should be considered. This article presents a modular assembly methodology to model the multi-domain multi-machine system on MEA and achieves real-time emulation on field programmable gate array (FPGA) hardware. The various domain (pneumatic, hydraulic, and mechanical) parts are viewed as modules and interfaced with the electrical domain through machine drive system. State-space model of the power electronic based multi-machine drive system is developed accordingly and the eigenvalue distribution is analyzed. This article also derives practical bounds on real and imaginary part of the eigenvalues to facilitate parallel computation. An 100-machine drive system is then constructed and a Monte Carlo test is performed to validate the effectiveness of the eigenvalue bounds. High fidelity real-time emulation of the MEA multi-domain multi-machine is realized on FPGA. Pneumatic, hydraulic, and mechanical domain characteristics along with the related electrical domain waveforms are exhibited and their comparisons with MATLAB/Simulink are provided. High agreement on these transients waveforms suggests that this modular assembly approach could be a helpful scheme for the modeling and design of MEA powertrains.
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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) | 0.002 | 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".