Progress towards a 3D Numerical Simulation of Ice Accretion on a Swept Wing using the Morphogenetic Approach
Bibliographic record
Abstract
We have developed an original, three-dimensional icing modelling capability, called the “morphogenetic” approach, based on a discrete formulation and simulation of ice formation physics. Morphogenetic icing modelling improves on existing ice accretion models, in that it is capable of predicting simultaneous rime and glaze ice accretions and ice accretions with variable density and complex geometries. The objective of this paper is to show preliminary results of simulating complex three-dimensional features such as lobster tails and rime feathers forming on a swept wing. The results are encouraging. They show that the morphogenetic approach can predict realistically both the overall size and detailed structure of the ice accretion forming on a swept wing. Under cold ambient conditions, when drops freeze instantly upon impingement, the numerical ice structure has voids, which reduce its density. At warmer air temperatures, when drops do not freeze on impingement, larger cavities form, due to the shadowing process, along with the development of morphological features called lobster tails. Ice feathers also appear. As far as we know, no other numerical model can simulate these complex, three-dimensional ice structures that form on a swept wing.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".