Surface Heat Transfer Study for Ice Accretion and Anti-icing Prediction in Three Dimension
Bibliographic record
Abstract
This paper present CANICE-3D, a three dimensional ice-accretion prediction code. This code can predict ice shape over a complete aircraft. The external pow field is based on a potential formulation using commercial CMARC panel code. Droplet trajectories are calculated in 9D considering the potential solution, the atmospheric liquid water content and a mean volumetric diameter distribution of water droplets. Boundary layer correction is added on surface streamlines starting from stagnation points. Thermodynamic balance is performed to predict surface temperature and freezing fraction of water mass which considers running back water. An Anti-Icing model has been coupled in an aim to predict optimal design for its configuration. Emphasis will be presented for thermodynamics and ice accretion formulation as well as anti-icing prediction. The integration of the surface roughness for convective heat transfer coefficient calculation within the boundary layer is described. Implementation of CANICE-3D and comparison of surface heat transfer coefficient and ice accretion prediction in conjunction with experimental data.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".