Development of a New Ice Crystal Icing Shear Adhesion Rotary Test Rig and Method
Bibliographic record
Abstract
This paper discusses the development and implementation of a new ice crystal icing (ICI) ice adhesion test system and technique. It is based on the state of the art rotary shear adhesion test rig developed at the NRC altitude icing wind tunnel (AIWT) used for supercooled liquid water (SLW) ice accretion adhesion testing. This rig was modified to use strain gauged arms for shed force measurements and implemented into the NRC ICI cascade rig at its research altitude test facility (RATFac). This permitted the exposure to a wide range of ICI conditions both in supercooled and wetbulb below freezing ICI conditions. The result is a standalone system that can be run remotely thus improving the testing efficiency by avoiding the need for accretion calibration points previously required to correlate accreted mass to icing exposure time. This technique was shown to be very repeatable with adhesion results for repeat points generally being within ±4 kPa and being highly sensitive in being able to measure shear adhesion strengths in the range of 8 to 200 kPa, with adhesion strengths down to 2 kPa measured but with increased noise levels. ICI accretion adhesion shear strengths are lower than typical SLW adhesion strengths and this new method proved to be effective in covering the full range.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.006 |
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".