A Penalization Method for 2D Ice Accretion Simulations
Bibliographic record
Abstract
Numerical tools for 3D in-flight icing simulations are not straightforward to automate when seeking robustness and quality of the results. Difficulties arise from the geometry and mesh updates which need to be treated with care to avoid folding of the geometry, negative volumes or poor mesh quality. This paper aims at solving the mesh update issue by avoiding the re-meshing of the iced geometry. An immersed boundary method (here, penalization) is applied to a 2D ice accretion suite for multi-step icing simulations. The suggested approach starts from a standard body-fitted mesh, thus keeping the same solution for the first icing layer. Then, instead of updating the mesh, a penalization method is applied including: the detection of the immersed boundary, the penalization of the volume solvers to impose the boundary condition and the extraction of the surface data from the field solution. Although the current work is intended for 3D simulations, the content of this paper is limited to 2D applications to show the feasibility of the method. Tests are performed on multi-step rime and glaze ice cases where it is observed that the penalization method is able to produce sensible ice shapes compared to a body-fitted approach. However, accurate results are only achieved if the mesh is a priori refined in the zone where ice accretion is expected. The paper will further discuss the computational time, mesh refinement requirements, accuracy, limitations and recommendations for improvement of the penalization method applied to icing.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".