NRC Particle Detection Probe: Results and Analysis from Ground and Flight Tests
Bibliographic record
Abstract
High altitude ice crystals are causing in-service events in excess of one per month for commercial aircraft. The effects include air data probes malfunctioning (pitot pressure and total air temperature in particular), and uncommanded engine power loss or flameout events. The National Research Council Canada (NRC) has developed a particle detection probe (PDP) that mounts on the fuselage of aircraft to sense and quantify the ice crystals in the environment. The probe is low-power and non-intrusive. This paper presents the results of ground and flight testing of this probe. Results are presented for ground testing in a sea level ice crystal wind tunnel and an altitude icing tunnel capable of generating both ice crystal and super-cooled liquid. The PDP was operated on several flight campaigns and the results of two will be presented. The first was on board a NRC Convair 580 aircraft in French Guiana (FG) and the second was on an Airbus operated A340 research aircraft in Darwin and Reunion Island. The flight campaigns included fully glaciated, mixed phase and fully liquid conditions. Results will be presented and compared across operating conditions at typical altitudes for commercial aircraft. Aircraft installation effects on the PDP measurements will also be examined. NRC has developed preliminary algorithms that provide reliable detection of ice crystal environment along with the ability to estimate the concentration of ice particles being encountered by the aircraft. However, the algorithms themselves are considered proprietary and therefore will not be discussed.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".