A study of aerodynamic performance degradation on aerofoils and aircraft wings due to accreted ice
Bibliographic record
Abstract
Abstract The aerodynamic performance of two dimensional (2D) aerofoils and finite aircraft wings is investigated when the leading edge is modified with a standard ice shape. Three ice-shapes, G1, G3 and R7 are selected for this investigation. For the aerofoils it was observed that the presence of strong vortex flows near the leading edge fundamentally changes the flow in that region forcing earlier transition and premature separation. Normal suction peaks designed to provide high aerodynamic lift and prolonged attached flow are replaced with a system of vortices which produce unwanted pressure spikes which influence the development of the boundary layer leading to earlier stall. The present study also includes the three dimensional (3D) effects due to iced leading edges on aircraft surfaces. As different from such studies elsewhere, which only considered the simple sweep back effects on constant cross section wings, the present work includes more realistic wings equipped with twist, taper and non-equal sweep back at leading and trailing edge of the wing. In absence of any experimental data on such real life type configurations, the computational fluid dynamic (CFD) results were first validated against measurements on non-swept wings. It was found that ice formations cause noticeable changes of the flow in the leading edge regions of the wing and promote strong 3D effects, which pervade across the entire span wise direction of the model.
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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".