Application of the Morphogenetic Approach to 1<sup>st</sup> AIAA Ice Prediction Workshop Test Cases
Bibliographic record
Abstract
View Video Presentation: https://doi.org/10.2514/6.2022-3609.vid This paper presents the application of the morphogenetic approach for numerical modelling of ice accretion to the test cases of the 1st AIAA Ice Prediction Workshop. The morphogenetic approach differs in a number of key ways from traditional icing codes, allowing it to generate distributed and non-uniform features ice shapes as seen experimentally. Computational fluid dynamics (CFD) and drop trajectory modelling were added prior to the morphogenetic ice accretion code. Making use of the natural surface roughness generated by the morphogenetic approach, a novel technique was implemented to capture the increase in surface roughness with accreted ice, allowing the CFD solution to capture this transient behavior. Time dependency was also simulated for one case, capturing well the growth of the glaze horn features, while other rime cases were successfully simulated using a single time step.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".