Aluminum Sample Characterization on the NRC AIWT Ice Adhesion Spin Rig
Bibliographic record
Abstract
This paper presents the adhesion strength of ice on sanded and machine-finished aluminum test coupons as measured using the National Research Council of Canada (NRC) Altitude Icing Wind Tunnel (AIWT) spin rig. This rig is used to evaluate commercial and internally-developed coatings for low-adhesion properties, and the performance of ice on aluminum is required as a baseline to compare the coatings against. The tests are performed over a range of aerodynamic and icing cloud conditions, including variations in static air temperature and exposure time (and therefore accumulated ice mass). The data analysis includes an evaluation of the uncertainty in the results based on the measured ice mass repeatability and the measured shear stress repeatability. The results show the adhesive shear stress of ice, generated with a 20 μm icing cloud at a true airspeed of 80 m/s and a static air temperature of -20°C to create rime ice conditions, is 682 kPa (±13%) for aluminum coupons with a sanded finish and 579 kPa (±16%) for aluminum coupons with a machined finish. The results are comparable to those found in literature for aluminum samples tested using centrifugal methods. There was no observed trend in the adhesive shear stress with variations in temperature or ice mass, within the range of conditions tested.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".