Parametric investigation of aerodynamic performance degradation due to icing on a symmetrical airfoil
Bibliographic record
Abstract
Ice accretion on lifting surfaces induces an aerodynamic penalty in lift and drag on an aircraft. This performance degradation depends on the geometric features, type, and surface characteristics of the accreted ice on the airfoil. In the present work, we propose a set of two-parameter, low-order models to represent some of the typical ice topologies: glaze, rime, and horn. The parametric space is swept for all types of ice to isolate the aerodynamic changes causing performance degradation on a canonical symmetrical airfoil, which is the representative airfoil used by the National Research Council of Canada's platform for ice accretion and coatings tests with ultrasonic readings platform for in-flight icing tests. The three ice topologies show a self-similar trend between the stall angle of attack and the ice thickness, with the horn-type of ice imparting the greatest drag and lift penalty due to strong boundary layer separation. The relative effect of ice roughness plays a secondary role in performance degradation, and in some cases, the roughness causes a thicker and more resilient boundary layer, which can, under very specific icing conditions, enhance the aerodynamic performance.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".