Numerical Icing Simulations of Cylindrical Geometry and Comparisons to Flight Test Results
Bibliographic record
Abstract
There is growing interest in government and industry to use numerical simulations for the Certification by Analysis of aircraft ice protection systems as a cheaper and more sustainable alternative to wind-tunnel and flight testing. The ice accretion on a cylindrical test article mounted under the wing of the National Research Council of Canada’s Convair-580 research aircraft during a flight test in Appendix O icing conditions was simulated using Ansys FENSAP-ICE™. A multishot simulation with input parameters averaged over the full icing period led to an increased level of liquid catch and ice accretion (by mass), and a broader ice profile when compared to a simulation with shot-averaged input parameters. An additional simulation using Ansys’ proprietary “extended icing data with vapor solution” method for calculating heat fluxes at the icing surface resulted in a broader ice profile in comparison to the classical technique, which produced a similar amount of accretion by mass. No combination of simulation settings, input parameters, and multishot methods tested in this study generated the same level of surface detail observed during flight testing, however, the amount of ice accretion, general location of ice features, and formation processes were in good agreement with the experimental 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".