The GLACIER Icing Facility-Lessons Learnt in Five Years of Operation
Bibliographic record
Abstract
The Global Aerospace Centre for Icing and Environmental Research Inc. (GLACIER) facility is located in Thompson, Manitoba, Canada. This facility provides icing certification tests for large gas turbine engines, as well as performance, endurance and other gas turbine engine qualification testing. This globally unique outdoor engine test and certification facility was officially opened back in 2010. The prime purpose of this facility is for icing certification of aero gas turbines. As a generic engine test facility, it includes the infrastructure and test systems necessary for the installation of both current and future gas turbine engines. The GLACIER facility completed its commissioning in the winter of 2010/2011, and has now experienced five years of full icing seasons. Rolls-Royce and Pratt and Whitney have both successfully performed certification and engineering icing testing with 5 engines completing their icing certification. Behind the successes there have been some aspects that could have gone better. Some could be considered an inevitable part of engine testing in the real world, but there have been some learning experiences that can be usefully shared with the wider community. This paper aims to examine and publicize the lessons learnt from these first five years of operation, including how the facility hardware has performed, what availability has been achieved, what we have learnt about the icing spray system and how we plan to develop the facility for the future, including the possibility of incorporating ice crystal and mixed phase testing, as well as SLD (Supercooled Large Droplet) testing.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.022 | 0.011 |
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".