Simulation of Ice Particle Melting in the NRCC RATFac Mixed-Phase Icing Tunnel
Bibliographic record
Abstract
Ice crystals ingested by a jet engine at high altitude can partially melt and then accrete within the compressor, potentially causing performance loss, damage and/or flameout. Several studies of this ice crystal icing (ICI) phenomenon conducted in the RATFac (Research Altitude Test Facility) altitude chamber at the National Research Council of Canada (NRCC) have shown that liquid water is required for accretion. CFD-based tools for ICI must therefore be capable of predicting particle melting due to heat transfer from the air warmed by compression and possibly also due to impact with warm surfaces. This paper describes CFD simulations of particle melting and evaporation in the RATFac icing tunnel for the former mechanism, conducted using a Lagrangian particle tracking model combined with a stochastic random walk approach to simulate turbulent dispersion. Inter-phase coupling of heat and mass transfer is achieved with the particle source-in-cell method. Predictions are compared to turbulence measurements and measurements of total-water and liquid-water content (TWC, LWC) obtained with iso-kinetic and SEA multi-element probes respectively. They are also compared to measured changes in air temperature and humidity ratio resulting from particle evaporation and melting. Good agreement is obtained for these changes under (low pressure) conditions where they are large and interphase heat/mass coupling is very significant. Predicted LWC levels bracket the SEA measurements at low values but are much greater at higher LWC. These predictions suggest that the critical LWC/TWC range for ICI accretion may be higher than previously inferred from SEA measurements.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".