Immersed Boundary Methodology for 3D Multi-Step Ice Accretion Simulations
Bibliographic record
Abstract
The in-flight ice accretion simulations are typically performed using a quasi-steady formulation through a multi-step approach. As the ice grows, the geometry changes, and an adaptation of the fluid volume mesh used by the airflow and droplet-trajectory solver is required. Re-meshing or mesh deformation are generally employed to do that. The geometries formed are often complex ice shapes increasing the difficulty of the re-meshing process, especially in three-dimensional simulations. Consequently, difficulties are encountered when trying to automate the process. Contrary to the usual body-fitted mesh approach, the use of Immersed Boundary Methods (IBMs) addresses, or significantly mitigates, the mesh update problem, enabling the automation of the entire simulation process. Previous work by the authors introduced immersed boundary techniques for calculating droplet trajectories. This current study integrates these IBMs into IGLOO3D, the ONERA ice accretion simulation suite. Adjustments were made to the calculation of the heat transfer coefficient, accounting for the inviscid nature of the airflow simulation. Additionally, various smoothing algorithms were explored to handle the shrinkage phenomena while preventing chaotic oscillations. Multi-step simulations were conducted under different icing conditions in 2D scenarios, and the methodology was further evaluated in a 3D rime ice case. All cases studied are part of the 1st Ice Prediction Workshop, providing a basis for comparison with experimental data and other icing codes. The simulations are notable for their low computational cost, and their results are deemed satisfactory, especially under rime icing conditions.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".