Advancements in CHAMPS for Multi-Layer Ice Accretion on Aircraft
Bibliographic record
Abstract
The numerical simulation of ice accretion on aircraft is a complex problem that is difficult to simulate robustly, especially in 3D. The process, which combines multiple different solvers, is prone to fail whenever the geometry deformation due to ice is too complex. Thus, the more ice layers, the more fragile is the simulation. This paper aims at studying, and possibly reducing, the dependency on the number of layers by considering i) the impact of the deforming surface on the impingement and ii) a local roughness modeling that can better position the ice horns. The method called Impact Angle Correction (IAC) method in the literature is implemented and consists in setting in an additional loop the components solved on the surface, namely the thermodynamic exchanges and the geometry update, to consider the change in the surface normal vectors. For each of these ice sub-layers, the impingement water mass is recomputed by considering all droplet bins after each deformation of the surface. Two-dimensional results show that this method can reduce the dependency on the number of full ice layers. A local roughness model is also implemented to impact the convective heat transfer simulation on the surface depending on local icing data. This local roughness could allow to better capture the ice horn locations, angle and height. Two-dimensional results presented in this work show in particular that lower horns are better captured when using a local roughness model. Three-dimensional glaze results show the effect of the coupling of both models for single-layer ice accretion, which can help capturing small-scale ice features.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.029 | 0.011 |
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".