An Immersed Boundary Method for Multi-Step Ice Accretion using a Level-Set
Bibliographic record
Abstract
View Video Presentation: https://doi.org/10.2514/6.2021-2630.vid The numerical prediction of in-flight ice accretion involves a sequential call to different modules including mesh generation, aerodynamics, droplet trajectories, wall heat transfer, ice accretion and geometry update. The automation of this process is critical as these solvers are embedded in a time loop which is repeated several times to obtain an accurate ice shape prediction. The robustness of ice accretion tools is often limited by the difficulty of generating meshes on complex ice shapes and also by the geometry update which can exhibit overlaps if not treated properly. As a replacement to the usual body-fitted approach, this paper investigates the application of an immersed boundary method in the ice accretion framework to avoid the mesh generation step. A level-set method is also used for the geometry update to automatically handle pathological cases. The proposed methodology is tested on 2D rime and glaze ice cases from the 1st AIAA Ice Prediction Workshop, showing good correspondence with the body-fitted approach. The new methodology also performs well for a 2D three-element airfoil configuration when a proper mesh refinement is used. The immersed boundary method combined with the level-set ice accretion provides a viable alternative to the body-fitted approach.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".