NRC particle detection probe: test cell to flight
Bibliographic record
Abstract
Purpose High-altitude ice crystals (HAICs) are causing one in-flight event or more per month for commercial aircraft. The effects include preventing air data probes (pitot pressure and total air temperature in particular) from functioning correctly and causing engines to roll back and shut down. The purpose of this study is to describe the process used by the National Research Council Canada (NRC) to develop and test a particle detection probe (PDP). The probe mounts on the fuselage of aircraft to sense and quantify the ice crystals in the environment. Design/methodology/approach The probe was demonstrated on the NRC Convair and Airbus A340 research aircraft as part of the European Union HAIC programme. The probe was ruggedised, adapted for easy installation in standard aircraft fittings and tested in a variety of conditions for longevity and endurance. Findings Efforts to achieve the safety requirements for flight on aircraft are discussed. The challenges, surprises and opportunities for testing on which the development group is capitalised are also presented. Practical implications It was demonstrated that the detectors gave signals proportional to the ice crystal content of clouds, and results demonstrating the functionality of the probe are presented. Originality/value This paper describes the multi-year process of developing the NRC PDP from a test cell sensor for detecting engine exhaust contaminants on an aircraft ice crystal detection probe. The work included over 20 flight tests on NRC aircraft and the Airbus HAIC test programme.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".