Ice Accretion Modeling for Snow Particles on Unheated NACA0012 Airfoil
Bibliographic record
Abstract
Safety operations under snow conditions is a certification requirement by the civil aviation regulatory agencies FAA [1,2] & EASA [3] for aircraft and their respective propulsion systems. However, snow particle interactions and accretion physics on heated/unheated, stationary/rotating components is yet to be well understood. The ICE-GENESIS Project [5] aims to support the development of new generation 3D icing engineering tools addressing Appendix C, O and Snow, for safe, efficient, and cost-effective design and certification of future aircraft and rotorcraft. Under this context the present paper showcases the predicting capability of an in-house 2D steady state Ice Accretion Tool (ICAT) for un-heated surfaces. For snow accretion predictions, GE developed sticking model and incorporated into In-house Ice Crystal Accretion Tool (ICAT) [4]. The ICAT ice thickness and shapes predictions are verified for various operating conditions (tunnel air temperature, snow density, particle distribution, particle aspect ratio, inlet liquid water ratio, etc.) against the experimental data from ICE-GENESIS partners (RTA Climatic Wind Tunnel in Vienna, Austria, and RATFac Test Cell at National Research Council in Ottawa, 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) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| 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".