Novel Two-Dimensional Modeling Approach for Aircraft Icing
Bibliographic record
Abstract
A new modeling approach to tackle the challenging problem of in-flight icing prediction is formulated and verified. With use of this new approach, termed morphogenetic modeling, the shape, structural details, and density of aircraft ice accretions are predicted by emulating the behavior of individual fluid elements. A two-dimensional, morphogenetic model is used to predict the ice accretion forming on a cylinder over a range of in-flight conditions. The model predicts rime, glaze, and simultaneous glaze and rime accretions. A partial verification of the model has been successfully accomplished. Although there are some discrepancies between experimental and predicted accretion shapes, especially for large and wet accretions, the overall agreement is good. In particular, the prediction of the stagnation line growth rate agrees well with experimental data. The results of our exploratory research are encouraging and suggest that morphogenetic modeling has the potential to advance the simulation of in-flight icing. Practical implementation of this approach will require coupling the model to an external flowfield solver, as well as to heat transfer and droplet impingement solvers. Further verification and extension to three dimensions are planned.
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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 | 0.000 |
| 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 | 0.000 |
| 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".