In-flight icing simulation capabilities of NRC's altitude icing wind tunnel
Bibliographic record
Abstract
AIAA 2001 0094: An overview of the icing cloud characteristics needed to simulate in-flight icing is presented. The wider range of conditions that result from the need to test at sub-scale conditions in a wind tunnel are shown to create additional challenges for icing wind tunnels, over and above those that are encountered in nature. A detailed description of the NRC Altitude Icing Wind Tunnel (AIWT) is presented, providing background information for the discussion of recent calibrations, flow quality surveys and icing cloud investigations. The instrumentation used for these studies is described and individual measurement uncertainties are documented. The aerodynamic calibration began with measurements of total and static pressure corrections. This was followed by planar surveys of the flow quality in the test section. The calibrations were conducted at sea-level conditions. The effects on test section flow quality of spraying air through the settling chamber spray bars are documented. Spray air generally impacts the flow quality by modifying the velocity uniformity and flow angularity. Surveys of the icing cloud consisted of droplet size calibration, liquid water content (LWC) uniformity and LWC calibration. From these studies, it was found that the AIWT has acceptable LWC uniformity. New spray bars, under development at this time, should improve the icing cloud uniformity even further. Preliminary investigations of a single prototype spray bar in the AIWT show improved spray on-off transients and greater uniformity in LWC distribution. Future investigations are planned to identify the cause of reduced flow quality near the starboard wall of the test section.
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.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".