Measurement of Liquid Water Content for Supercooled Large Drop Conditions in the NRC’s Altitude Icing Wind Tunnel
Bibliographic record
Abstract
<div class="section abstract"><div class="htmlview paragraph">As a result of new regulations pertaining to the airworthiness of aircraft exposed to in-flight icing conditions where maximum water drop size is greater than 100 microns (referred to as Supercooled Large Droplet (SLD) conditions), updates are required to the test facilities and simulations that will enable manufactures to certify their products under these new rules. While a number of facilities report achieving some of the conditions specified in the new regulations, questions remain as to the suitability of the instrumentation used to measure the Liquid Water Content (LWC) and drop size distributions of the SLD icing cloud. This study aims to provide baseline LWC data through ice accretion measurement techniques on a NACA 0012 airfoil and rotating cylinders of varying diameters. This forms part of a collaborative effort between the NRC, NASA Glenn and the Italian Aerospace Research Centre (CIRA), to examine the suitability of current instruments in accurately measuring LWC in clouds where SLD conditions are present.</div><div class="htmlview paragraph">Calculation of LWC’s from leading edge airfoil ice thickness measurements on the NACA 0012 airfoil were within ±10% of the tunnel LWC setting over the full range of MVD’s tested (20μm to over 300μm). When using rotating cylinders of varying size to measure LWC, it was found that smaller diameter cylinders underestimated LWC of icing clouds containing larger drop diameters compared to the tunnel setting whereas LWC measurement from larger cylinders provided values within 10% of the tunnel setting across the range of MVD’s tested.</div></div>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".