Validation and Verification of Multi-Steps Icing Calculation Using CANICE2D-NS Code
Bibliographic record
Abstract
A newly developed Navier-Stokes based two-dimensional ice accretion and anti-icing simulation code, CANICE2D-NS is presented. The method is devised to be fully automated for use within a multi-step approach capable of analyzing long ice accretion accumulation times in a quasi-steady formulation. An efficient single-block structured Navier-Stokes CFD code, NSCODE, have 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 acceleration of the convergence of the steady and quasi-steady iterative procedures. Effects of uniform surface roughness in quasi-steady ice accretion simulation are analyzed through different validation test cases, including code to code comparisons with the same framework coupled with another Navier-Stokes solver. The efficiency of the J-multigrid approach to solve the flow equations on complex iced geometries is demonstrated. Finally, results on up to 160 quasi time-steps calculations are presented and analyzed. 1 Ph.D. student, kazem.hasanzadeh@polymtl.ca. 2 Postdoctoral student, ali.mosahebi@gmail.com. 3 Assistant Professor, eric.laurendeau@polymtl.ca. 4 Professor, ion.paraschivoiu@polymtl.ca.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".