Two-Dimensional/Infinite Swept Wing Ice Accretion Model
Bibliographic record
Abstract
A framework for icing simulations, using state-of-the-art technology for solving mesh, airflow and droplets equations, is presented. A two-dimensional Eulerian droplet flow solver has been developped with multi-timesteps approach and extended with infinite swept wing hypothesis. The overarching objective is to enable fast three-dimensional ice prediction by the calculation of several 2D computations along the swept wing span. The droplets crossflow equation is solved using the decoupled implicit Euler scheme used for the 2D system, without added complexity, nor degradation in performances. A thermodynamic module based on an iterative Messinger approach has been developped to treat multi-stagnation points. The 2D solver is validated on two cases, rime and glaze, against experimental data and other numerical codes. The infinite swept wing droplets solver is successfully validated against 3D infinite swept wing results obtained with NSMB-ICE, a 3D ice accretion solver. Finaly, a comparison is performed on a finite swept wing with NSMB-ICE results, experimental data and LEWICE results, demonstrating the feasability of the infinite swept wing approach.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".