Comparison of thermodynamic models for ice accretion on airfoils
Bibliographic record
Abstract
Purpose This paper aims to assess the strengths and weaknesses of four thermodynamic models used in aircraft icing simulations to orient the development or the choice of an improved thermodynamic model. Design/methodology/approach Four models are compared to assess their capabilities: Messinger, iterative Messinger, extended Messinger and shallow water icing models. They have been implemented in the aero-icing framework, NSCODE-ICE, under development at Polytechnique Montreal since 2012. Comparison is performed over typical rime and glaze ice cases. Furthermore, a manufactured geometry with multiple recirculation zones is proposed as a benchmark test to assess the efficiency in runback water modeling and geometry evolution. Findings The comparison shows that one of the main differences is the runback water modeling. Runback modeling based on the location of the stagnation point fails to capture the water film behavior in the presence of recirculation zones on airfoils. However, runback modeling based on air shear stress is more suitable in this situation and can also handle water accumulation while the other models cannot. Also, accounting for the conduction through the ice layer is found to have a great impact on the final ice shape as it increases the overall freezing fraction. Originality/value This paper helps visualize the effect of different thermodynamic models implemented in the same aero-icing framework. Also, the use of a complex manufactured geometry highlights weaknesses not normally noticeable with classic ice accretion simulations. To help with the visualization, the ice shape is presented with the water layer, which is not shown on typical icing results.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".