Optical measurement of ice crystal icing on a NACA 0018 airfoil
Bibliographic record
Abstract
View Video Presentation: https://doi.org/10.2514/6.2022-3699.vid The ingestion of high altitude ice crystals into jet engines can have a serious effect on their performance, to mitigate the risk of engine failure, the physics behind the accretion process needs to be understood. Previous research has used simplistic geometries to generate empirical models and newer research has also analysed more engine realistic geometries such as cascades and full annular geometries. The fidelity of numerical modelling of ice crystal icing has improved vastly, meaning that complex geometries can now be numerically predicted. High-quality experimental data of accretion profiles are needed for validation of these numerical models. Conventional measurement techniques can only be used on simplistic geometries or used after the accretion has formed. A transient, non-intrusive method is therefore required. A method involving stereo vision, called digital image projection (DIP) has been developed and is analysed in this paper. A simplistic geometry of a NACA 0018 airfoil was chosen to study the measurement technique so that high accuracy data could be obtained by alternative methods to validate the DIP. The experiment was conducted using the Altitude Icing Wind Tunnel (AIWT), at the National Research Center (NRC) of Canada. The results showed that the DIP system was able to measure the accretion profile growth during the test to a high degree of accuracy when compared to a separate commercial method, with the mean error being less than 0.2 mm.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.004 | 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".