Ice Crystal Environment Modular Axial Compressor Rig: Characterization of Particle Fracture and Melt Across One Rotor Using Laser Shadowgraphy
Bibliographic record
Abstract
The National Research Council of Canada (NRC) has developed the Ice-Crystal Environment Modular Axial Compressor Rig (ICE-MACR) for simulating altitude ice crystal icing of aircraft engines in altitude facilities. Commissioning of the rig under altitude icing conditions was conducted in the NRC’s altitude icing wind tunnel (AIWT) in May-June 2019. The rig consisted a single working compressor stage with a downstream accretion test article representative of a compressor s-duct. Measuring fragmented ice particle size downstream of the working stage is critical to understand the icing conditions at the accretion test article. Particle break-up data across the stage will also be important for validation of numerical icing models. Details of a laser shadowgraph technique used to quantify ice particle size and subsequent results are presented. Results include particle size as a function of rotor speed, and radial distribution of particle size downstream of the rotor. In addition, a process to develop and assess a method of determining the particle melt fraction from the backlit microscopic (shadowgraph) images is presented. The shadowgraph derived melt ratio is compared to Multi-Element probe melt ratio measurement for validation. For the test conditions studied, the melt ratio calculated for the small particle bins (<26 µm) was found to correlate well with the Multi-Element melt ratio. As the shadowgraph data has the ability to quantify the melt ratio of different particle size ranges, it could provide a powerful complement to melt and fracture modeling.
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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 | 0.001 | 0.001 |
| 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) | 0.003 | 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".