Bibliographic record
Abstract
The helicopter flight simulators domain is one of major interest among multiple applications of system identification in the aeronautical industry. This document presents the steps of building and certification of a complete mathematical model of a Bell 427 helicopter flight simulator. \n \nThis research was performed in the context of a CRIAQ project and done by École de Technologie Supérieure in collaboration with the Canadian National Research Council(NRC) and Bell Helicopter Textron. The complete mathematical model has to pass the certification requirements in according with the FAA AC 120 - 63, and had to be validated with flight test data. From flight test data at different gross weight and different flight conditions obtained from Bell Helicopter Textron, the stability and control derivatives were estimated by the Maximum Likelihood Estimation Method and assembled in a fully coupled, 6 degree of freedom (6DoF) modified state space model at National Research Council (NRC). The validation procedure is incorporated in NRC software referred to as POM (Proof of Match). The flight condition cases analysed in this document, are lateral motion, hover out of ground effect, hover in ground effect, and autorotation. \n \nThe complete model is still in a development phase and does not include the transition model, engine model and landing ground dynamics model, yet it is capable of simulating the up and away flight cases for short periods of time which are referred to as "snapshots" and trimmed helicopter states. The validation process demonstrates that in this stage the model can be certified as a level D simulator.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 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".