Implementation of a Non-Intrusive Ultrasound Ice Accretion Sensor to an ALF502R-5 Vane Segment Ice Crystal Component Test
Bibliographic record
Abstract
View Video Presentation: https://doi.org/10.2514/6.2022-3697.vid Ultrasound ice accretion sensors (UIAS’s) were successfully utilized on an ALF502R-5 vane segment to detect and characterize accretion for a wide range of ice crystal icing (ICI) conditions conducted in the National Research Council of Canada (NRC) cascade rig. Their data along with visual observations of the accretion surface showed the rig environment to be effective in simulating the accretion observed in the engine in both coverage and morphology, although the initial growth rate in the rig was slower than that seen in the engine. This work also examined the accretion shed characteristics of ICI where in some scenarios, the UIAS and temperature data were able to show accreted ice lifting off the surface, but not shedding, and then continued to grow while being pinned to other component features. A new UIAS detection algorithm was also investigated and applied to a range of ICI test conditions. In all cases, the UIAS’s were very sensitive to accretion and provided early detection where only small islands of localized ice growth were visible at the point of detection in both wetbulb>0oC and supercooled ICI environments.
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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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".