Characterization of Aircraft Icing Environments with Supercooled Large Drops for Application to Commercial Aircraft Certification
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Observations of aircraft icing environments that included supercooled large drops (SLD) greater than 100 μ m in diameter have been analyzed. The observations were collected by instrumented research aircraft from 134 flights during six field programs in three different geographic regions of North America. The research aircraft were specifically instrumented to accurately measure the microphysics characteristics of SLD conditions. In total 2444 SLD icing environments were observed at 3-km resolution. Each observation had an average liquid water content (LWC) > 0.005 g m −3 , drops > 100 μ m in diameter, ice crystal concentrations <1 L −1 , and an average static temperature ≤0°C. SLD conditions were observed approximately 5% of the in-flight time. The SLD observations were segregated into four subsets, which included conditions with maximum drop sizes <500 μ m and >500 μ m in diameter, each with median drop volume diameters <40 μ m and >40 μ m. For each SLD subset, the observations were used to develop envelopes of maximum LWC values as a function of horizontal extent and temperature. In addition, characteristic drop size distributions were developed for each SLD subset. The maximum LWC values physically represent either the 99% or 99.9% LWC values, as determined from an extreme value analysis of the data. The analysis is sufficient for simulation of SLD environments with either numerical icing accretion models or wind-tunnel icing simulations. The SLD envelopes are similar in structure and supplemental to existing aircraft icing envelopes, the difference being that the existing envelopes did not explicitly incorporate SLD conditions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it