Multi-Layer Stochastic Ice Accretion Model for Aircraft Icing
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it