Modelling Unsteady Ice Accretion and Water Runback in Aero Engines
Bibliographic record
Abstract
High altitude ice crystals have led to instances of ice accretion on stationary compressor surfaces in aeroengines. Rollback, surge and stall events are known to have been instigated through such accretions due to aerodynamic losses related to ice growth, damage and flameout due to ice shedding. The prevalence of these events has led to a change in certification requirements for icing conditions. Development of accurate numerical models allows the costs of certification and testing to be minimised. Ice crystal icing (ICI) accretion is modelled as a coupled heat transfer and phase change continuity and energy balance at the surface. The Extended Messinger Model (EMM) incorporates a temperature gradient across the ice and water layers. This model was updated previously to model mixed phase ICI with an additional water layer at the surface to generate a temperature gradient from a warmed surface to the ice interface (>0°C), the updated model is EMM-Crystals (EMM-C). Within this paper, the EMM-C is modified to generate unsteady ice accretion. This update is relevant due to the intrinsic 3D flows present in a compressor stage and documented testing of engine stages and flight tests showing the complex 3D shapes of mixed phase warmed surface ICI accretion. A substrate heat transfer model is implemented for turbomachinery specific flow cases. This work has been validated against ICI experiments comprising of a cantilevered prismatic stator test piece, a swan neck duct linear cascade and existing experimental results from test campaigns performed at the Research Altitude Test Facility (RATFac) in 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".