FENSAP-ICE Modeling of the Ice Particle Threat to Engines in Flight
Bibliographic record
Abstract
A series of turbofan engine malfunctions characterized by flameout and rollbacks at high altitudes have been reported and analyzed by flight safety agencies and concerned industries1. Conclusions pointed the source of these incidents to be an ice accretion build-up in the low-pressure compressor of the turbofan explained by the presence of ice crystals in the flying environment. In order to provide a numerical tool to analyze such situations, a new capability is developed within FENSAP-ICE2 that provides an unsteady model for ice crystals accretion in jet engines. The first step of this study is concentrated on adapting FENSAP-ICE to turbomachinery problems. A 3D unsteady parallel approach for rotor-stator interaction is developed, allowing the treatment of multi-stage blade motion in mixed relative and absolute frames of reference via a finite element interpolation method at interfaces3. The approach is demonstrated using the NASA compressor stage 35. As a first approximation, physical correlations of ice crystals are substituted in the droplet module of FENSAP-ICE, DROP3D, to obtain the unsteady ice crystals impingement zones in a turbomachinery stage, using flux-based collection efficiency. Particular attention is paid to the effects of unsteady rotor-stator interaction across the crystals’ trajectories.
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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 | 0.001 | 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) | 0.004 | 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".