Prediction of 2D Airfoil Ice Accretion by Bisection Method and by Rivulets and Beads Modeling
Bibliographic record
Abstract
The paper presents recent developments in wet and dry ice accretion simulation at AMIL (Anti-Icing Materials International Laboratory) in a joint project with CIRA (Italian Aerospace Research Center). The thermodynamic model of ice accretion is similar to existing ones developed by LEWICE in USA, DRA in British, ONERA in France and Ecole Polytechnique de Montreal in Canada. However, this paper introduces an analytical model to calculate the surface roughness in the wet regime based on the residual, runback and shedding liquid water mass on an airfoil surface. Also, a new geometric model to build the ice surface, based on panel angle bisections, is presented. In the wet regime, the empirical LEWICE correlation used to determine the equivalent sand-grain roughness is replaced by two analytical formulations to calculate the local roughness height. The first one considers the maximal height that the bead can reach before moving, while the second computes the wave height on the water film. The maximum bead height before moving is determined from the equilibrium between aerodynamic, gravitational and surface tension forces. The bead behavior in dry and wet regimes was described analytically Based on the work of Al-Khalil and Hansman, which led to the determination of a water surface state (film, rivulets or beads). A mass balance is used to determine the residual and runback mass of water when the water state and the maximal bead height before moving are known. The water shedding mass is equal to the runback water mass for the lower surface and is zero for the upper surface of the airfoil respectively. A geometrical model based on panel bisection allows the ice growth in normal direction to the surface. This method simulates the ice surface accretion continually without gaps between panels. The accretion model is validated with icing profiles obtained experimentally in wind tunnel by Shin and Bond for a NACA0012 wing profile with a 0.5334 m chord, a 20 µm median volume droplet diameter, a 1 g/m³ liquid water content and a 65 m/s airspeed. These results cover both ice accretion regimes in the -4.4° to -28.3°C temperature interval. The roughness calculated analytically is in the same order of magnitude as LEWICE correlation. The use of analytical models for roughness generated the complex icing shapes (horn) as the ones observed experimentally. However, in most cases, the accreted ice was slightly bigger than the measured.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".