SLD Instrumentation in Icing Wind Tunnels – Investigation Overview
Bibliographic record
Abstract
View Video Presentation: https://doi.org/10.2514/6.2021-2647.vid A collaborative effort to better understand cloud characterization probes in Supercooled Large Drop (SLD) conditions, as well the ability to simulate these conditions in several icing wind tunnels, was undertaken by NASA, NRCC, CIRA, ECCC, FAA and Met Analytics, Inc. Both drop sizing and liquid water content, LWC, were measured with various probes using current to emerging technologies. To ensure the best possible data quality from the newest probes, the probe manufacturers, SEA, Inc. and Artium, Inc. were invited to support testing and data analysis efforts. A common set of probes was identified to test in each of the three participating facilities: NRCC’s Altitude Icing Wind Tunnel, NASA’s Icing Research Tunnel and CIRA’s Icing Wind Tunnel. From the common set of probes, a subset were identified to use for comparison across the three facilities. These were the CDP-2 and 2D-S for drop sizing, and the Multi-wire for LWC. The LWC value was also checked by measuring the ice accretion thickness under hard rime conditions on a NACA-0012 airfoil. A common test matrix with sweeps in both LWC and median volume diameter, MVD, was developed. Each facility achieved these conditions as determined by their own calibration. The MVD ranged from 20 to at least 200 um, and LWC ranged from 0.5 to 3 g/m3. The comparison probes tested at common conditions in each facility were intended to allow for a direct comparison, and check of potential facility bias.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".