NRC Bell 412 aircraft fuselage pressure and rotor state data collection flight test
Bibliographic record
Abstract
The Flight Research Laboratory (FRL), of the National Research Council of Canada owns and operates a Bell 412 research helicopter that is essentially the same type aircraft as the Canadian Department of National Defense (DND) Griffon. DND has a major research thrust involving the improvement of aerodynamic and simulation models of this aircraft. In collaboration with DND, FRL has recently performed flight testing of the Bell 412 in support of this effort. In addition to the already extensive suite of inertial and engine data collected on a regular basis, the aircraft was instrumented with 256 static pressure transducers located at various positions around the fuselage, engine cowls and tail boom. Rotor flapping, lead-lag and strain quantities were also instrumented. Data was acquired in many different flight re- gimes throughout the aircraft envelope, including hover, cruise flight, climbs and descents and autorotation. Data was also collected in the hover both in front of a hangar face and in a field clear of obstacles in two different wind conditions, with a set of ground based anemometers to collect air wake data. This extensive data set is intended to serve as validation data for compu- tational fluid dynamics (CFD) models of the aircraft and to extend the knowledge base of the characteristics of airflow around a helicopter in flight. The data will also allow a better under- standing of the effects of buildings on the air wake of a helicopter, an important consideration for ship dynamic interface. This paper describes the instrumentation set up, flight testing con- ducted and provides samples of the flow field data collected.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.005 |
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".