Quasi-Steady Convergence of Multistep Navier–Stokes Icing Simulations
Bibliographic record
Abstract
A newly developed two-dimensional ice accretion and antiicing simulation code, CANICE2D-NS, is presented. The method is used to predict iced airfoil shapes and performance degradation with a multistep approach. A multiblock Navier–Stokes code, NSMB, has been coupled with the CANICE2D icing framework, supplementing the existing panel method-based flow solver. Attention is paid to the roughness implementation within the turbulence model and to the convergence of the steady and quasi-steady iterative procedure. The new coupling allows fully automated multilayer icing simulation, whereas also permitting flow analysis and performance prediction of iced airfoils. Effects of uniform surface roughness in quasi-steady ice accretion simulation are analyzed through different validation test cases. The results demonstrates the benefits and robustness of the new framework in predicting ice shapes and aerodynamic performance parameters, as well as iced airfoil surface pressure coefficients. Finally, the convergence of the quasi-steady algorithm is verified and identifies the need for an order of magnitude increase in the number of multitime steps in icing simulations.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".