Ice Crystal Environment Modular Axial Compressor Rig: Modeling of Ice and Air Conditions
Bibliographic record
Abstract
The National Research Council Canada (NRC) Ice Crystal Environment Modular Axial Compressor Rig (ICE-MACR) physically simulates the conditions in a gas turbine compressor in altitude icing wind tunnels for performing ice accretion tests. This allows significantly more instrumentation and monitoring than could be achieved on a real engine, and allows test conditions to be well controlled. However, the mixed phase environment (ice, liquid and vapour water, and air) makes measurements during the accretion tests challenging. To better understand the accretion results experimental data is supplemented by a 1-D numeric model used to calculate air temperature, humidity, water temperature and melt ratio of select accretion test runs. Numeric model results for select test points from the 2023 ICE-MACR campaign are presented. Uncertainties in initial test conditions are also examined to determine if they could have a significant affect on the results.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".