Low-Order Method for Predicting Aerodynamic Performance Degradation Due to Ground Icing
Bibliographic record
Abstract
Alow-ordermethod to computethe degradation in the maximum lift coefe cient of an aircraft due to distributed surfaceroughnesshasbeendeveloped.ThealgorithmisappliedtotheFokkerF28Mk1000,anaircraftinvolvedinan accidentinwhichgroundicingwasdeterminedtoplayasignie cantrole.Themethodconsistsoftwocomplementary portions. The e rst uses a low-orderpanel method to computethequasi-steady aerodynamiccharacteristics, in and out of groundeffect,ofthecompleteaircraftgeometryincluding one- ortwo-elemente aps,a boundary-layerfence, nacelles, and a horizontal tail. The numerical model then, in the second portion, generates an engineering estimate of the maximum lift coefe cient for the aircraft cone guration. The method is based on the assumptions that the pressure difference between the suction peak and the trailing edge of an airfoil is a maximum at the maximum lift coefe cient and that the condition in which one spanwise station of a wing in a stripwise analysis violates this maximum pressure difference rule is the point at which the maximum lift of the entire aircraft is generated. The application of the pressure difference rule is modie ed to account for the presence of the boundary-layer fence and can estimate (on the conservative side ) the effects of distributed surface roughness on the maximum lift coefe cient using an appropriate maximum allowable pressure difference.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".