Ice Crystal Accretion Test Rig Development for a Compressor Transition Duct
Bibliographic record
Abstract
Ingestion of ice crystals into aircraft gas turbine engines have been shown to trigger partial or complete power loss. Although the ice crystal phenomenon has been recognized since the early 1950's, it was not until the mid-1990's that significant attention had been given to it with a key event being a flight campaign conducted with a small commuter aircraft which demonstrated ice crystals to be responsible for engine power loss. Although flight and engine testing have revealed ice crystals to be detrimental to gas turbine engine operation, these are not ideal test platforms to observe the ice crystal phenomenon due to limited access for instrumentation and visual observations. This paper discusses the development of an ice crystal test system used to simulate the ice crystal environment seen in a gas turbine compressor while maintaining visual accessibility and ease of instrumentation. This test system consists of a method to produce a range of ice crystal environments, the ability to vary airflow conditions in the rig and a static test section which simulates a gas turbine compressor transition duct. This system has been successful in producing a wide range of ice crystal test conditions and has shown significant ice accretion can occur on surfaces above 0°C while allowing for visual observations and recording of temperature data during the accretion phenomenon. © 2010 by Her Majesty the Queen in Right of Canada.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".