Ice Crystal Fragmentation and Accretion on the Rotating Test Rig of ICE-MACR
Bibliographic record
Abstract
Over the last decades turbofan engines have been experiencing undesirable in-flight events related to ingestion of ice crystals, such as power loss and compressor mechanical damage [8-9], hence FAA and EASA have issued engine certification regulations for the design of engines to operate in ice crystal conditions. To simulate the ice crystal icing (ICI) in an engine compressor environment, the National Research Council of Canada (NRC) developed the Ice Crystal Environment Modular Axial Compressor Rig (ICE-MACR) [1-3] consisting of single or 2-stage compressors followed by an extension duct and a S-duct with test article vanes. The MUSIC-haic project was a research and innovation action funded by the European Union through the H2020 program which aimed to build on past research projects and existing multi-disciplinary tools to provide the aeronautic industry with an ICI numerical capability usable for both design and certification purposes [22]. Under this context the present paper analyses three different rotational speeds for fragmentation and accretion predictions using GE’s in-house modeling tools. The fragmentation analysis involves 3D CFD aero simulation of the complete ICE-MACR single-stage airflow passage followed by Lagrangian particle tracking. The predictions from this analysis are comparable with test observations. For accretion predictions, GE’s inhouse Ice Crystal Accretion Tool (ICAT) [4] was used at different radial span locations on the test article vane and the final 3D ice shape was predicted using a pseudo-3D approach. The ice thickness and shape predictions are verified at different melt-ratios/severity regimes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".