Numerical simulation of ice accretion using Messinger-based approach: Effects of surface roughness
Bibliographic record
Abstract
In-flight icing on an aircraft's surface can be a major hazard in aeronautics's safety. Numerical simulations of ice accretion on aircraft is a common procedure to anticipate ice formation when flying in a supercooled water droplets cloud. Numerical simulations bring a better understanding of ice accretion phenomena, performance degradations and lead to even more efficient thermal de-icing systems' designs. Such simulations imply modelling the phase change of water and the mass and energy transfers. The Messinger model developed in the 1950′ is still used today as a reliable basis for new models development. This model estimates the ice growth rate using mass and energy balances coupled to a runback water flow. The main parameter introduced with this approach is the freezing fraction, denoting the fraction of incoming water that effectively freezes on the airfoil. The objective of the present work is to model an ice accretion on an airfoil using a Messinger-based approach and to make a sensitivity analysis of roughness models on the ice shape. The test case will be performed on a 2D NACA0012 airfoil. A typical test case on a NACA0012 airfoil under icing conditions will be run and confronted with the literature for verification prior to further investigations. Ice blocks profiles comparisons will highlight the differences implied by the choice of the roughness correction, which impact the heat transfer coefficient.
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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 | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".