Liquid Water Content Instrumentation Study at the NRC AIWT
Bibliographic record
Abstract
The National Research Council Altitude Icing Wind Tunnel liquid water content calibrations have historically relied on a 2.4 mm diameter rotating cylinder for drop sizes up to 50 μm and a 6.2 mm diameter rotating cylinder for drop sizes from 50 μm to 200 μm. This study compares the facility calibration, derived from rotating cylinder measurements, to water content measurements from the Science Engineering Associates Multi-Element Probe and the National Research Council Compact Iso-Kinetic Probe over a range of airspeeds and drop sizes. The data show where the rotating cylinder measurements may start to underestimate the liquid water content (LWC), possibly due to splashing at higher airspeeds and drop sizes. The data also show that the LWC read by the Multi-Element Probe is higher than that provided by the rotating cylinders, and the Compact Iso-Kinetic Probe (CIKP) reads higher than both other methods. These trends are consistent with instrumentation comparison data from other icing wind tunnels. The study examines the effects of aerodynamic blockage on the probe measurements and the effects of placing the CIKP reference humidity sensor at different positions relative to the icing cloud. Overall, the data do not support changing the AIWT LWC calibration method from the rotating cylinder at this time.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".