Bibliographic record
Abstract
Over the past few years, we have developed a unique approach to simulate aircraft icing numerically; we call this method morphogenetic modelling. Previously, we developed a successful two-dimensional version of the model; the objective of our present research is to show that the morphogenetic modelling approach can be extended to three-dimensional in-flight icing. In this paper, we focus on the simulation of three-dimensional, discrete rime structures forming on the fuselage of a helicopter. The numerical model consists of three components: an airflow solver, a drop trajectory solver, and a morphogenetic ice growth model. The velocity field of the flow is computed using the Euler equations, while the drop trajectories are computed using a Lagrangian approach. Computation of drop impact locations determines the local collision efficiency distribution. The morphogenetic model deals with the processes occurring on the impinging surface. Depending on atmospheric conditions, impinging drops may freeze instantly or they may move along the surface before freezing. By building the ice accretion one particle at a time, the morphogenetic model simulates the time evolution of the accretion shape in a natural way that mimics real world behaviour.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".