Multi-Layer Stochastic Ice Accretion Model for Aircraft Icing
Bibliographic record
Abstract
View Video Presentation: https://doi.org/10.2514/6.2021-2629.vid This paper presents a stochastic approach to model ice accretion on airfoils under in-flight icing conditions within a multi-layer process. The stochasticity itself is introduced in the impingement and freezing steps of water particles, as suggested in the literature. The model implementation is thought for reducing the CPU and memory cost by solving the stochastic ice accretion on an advancing front grid made of Cartesian cells, called pixels. Furthermore, the impingement and freezing processes are performed with probabilities obtained from the droplet trajectory and thermodynamic modules, respectively, which are compared against pseudo-random numbers generated with a uniform distribution. Multi-layer icing is achieved by extracting a new geometry from the stochastic field solution into a B-spline at a given time in order to regenerate a body-conforming grid and to start again the overall ice accretion process for a new layer. Verification and validation are performed on two NACA0012 test cases. Numerical results are compared to experimental data and are found to be qualitatively in better agreement as the number of icing layers increases. The proposed approach successes to capture the overall ice geometries of the test cases, despite some ice height discrepancies. In particular, the ice density is shown to change along the surface, which is expected in real ice experiment. Since the ice density is a dependent variable of the problem, a calibration of the model could lead to improved ice shapes predictions.
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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) | 0.001 | 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".