Ice Crystal Icing Test Design and Execution for the ALF502 Vane Segment in the NRC RATFac Cascade Rig
Bibliographic record
Abstract
Understanding the behaviour of ice crystal ice (ICI) accretion and shedding inside an aircraft engine is important for safe and reliable engine operation in flight and to meet new airworthiness regulations. A significant advancement in this understanding came from two engine test campaigns carried out on a Honeywell ALF502 turbofan, led by the Ice Crystal Consortium (ICC) and NASA. However, it is often desirable to conduct smaller scale component level tests to both decrease costs and increase the amount of data obtainable, given a component is more accessible when removed from an engine and therefore easier to instrument and observe. That was the purpose of the work discussed in this paper where a segment of an ALF502 low pressure exit guide vane ring was installed in the NRC RATFac ICI cascade rig. The existing cascade rig was modified to accommodate the vane segment which allowed for the instrumentation already available on the rig to be used to characterize the ICI environment. The test article fixturing was designed to match the engine aerodynamics as much as possible but also allow for a large suite of instrumentation including temperatures, pressures, video, ultrasound ice accretion sensors and systems for wall heating and anti-icing. The test was able to characterize accretion versus a wide range of ICI conditions, determine the effect of wall heating and define accretion boundaries. With the wide range of test parameters that were independently and precisely controlled and the instrumentation available to measure them, the understanding of accretion in the vane segment was expanded relative to that seen in the engine test. Accretion was seen on the test article in the location observed in the engine with similar morphology. Further analysis of the test data could demonstrate the added value of a rotating rig test.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".