Bibliographic record
Abstract
For the last few decades, Canada's National Research Council (NRC) has been at the forefront in analyzing dynamic systems and developing tools to construct aircraft models based on flight test data. With a fixed and rotary-wing aircraft fleet available, NRC has the capability to perform leading edge R&D System Identification (SI); this worldleading SI technology has been developed and has assisted industry partners, Department of National Defense (DND), and various universities in aircraft simulation and development. As a result, NRC has gained extensive experience in modeling aircraft using SI techniques. In collaboration with CAE, this paper demonstrates the acceleration of the NRC's current flight modeling techniques, highlighting recent advances in Artificial Intelligence (AI) and Machine Learning (ML). A new Bayesian ML software is being developed to identify a 6 degrees of freedom (6-DoF) quasisteady model using simulated flight test data. To achieve this, data from the CAE Sample electric Vertical Take-Off and Landing (eVTOL) simulation platform vehicle during hover maneuvers is utilized. Additionally, this paper presents results on extending the model to include rotor dynamics using the classical SI approach for comparison purposes. In summary, all methods provide a high-fidelity model; with the higher model structure, the vertical acceleration match was noticeably better.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".