Summary From the 2nd AIAA Ice Prediction Workshop
Bibliographic record
Abstract
The AIAA 2nd Ice Prediction Workshop (IPW-2) assembled varied specialists to perform numerical validation benchmarks towards assessing the state-of-the-art in aircraft ice accretion simulation capabilities. The workshop committee, equally diverse, chose to focus on three experimental tests cases, two swept and one unswept wing, in various icing conditions. The first two cases were selected because of their inherent 3D nature as well as their extensive datasets, while one original unswept wing low-Reynolds-number case representative of Unmanned Aerial Vehicle provided a blank test with data acquired as the workshop developed. All cases contained important tunnel-wall effects. Geometries were supplied, structured and unstructured grids created, and available experimental datasets and flow conditions provided to workshop participants. An effort was placed in standardizing data post-processing, a challenge for 3D configurations. In addition to ice accretion, data such as pressure distribution, stagnation line location, collection efficiency, freezing fraction, heat transfer coefficient, surface temperature, ice mass, minimum and maximum cross section of ice were analyzed experimentally and/or numerically. Results from various Computational Fluid Dynamics workflows were provided by academia, research centers and industries. The data was analyzed via code-to-code and code-to-experiment comparison plots that are available publicly. The paper formally presents the test cases and present some highlights. A methodology and technology gap assessment conclude on the current state-of-the-art and presents possible future workshops directions to improve our understanding and modeling of the subject experimentally and numerically.
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.047 | 0.033 |
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".