Tail Buffet of F/A-18 at High Incidence with Sideslip and Roll (Part 1)
Bibliographic record
Abstract
The unsteady pressure was measured simultaneously at 24 locations on both sides of a vertical e n of a rigid, 6% scale model of the F/A-18 aircraft in a wind tunnel. This pressure was integrated over the entire e n surface and over time to provide the mean and rms normal force, bending moment, and torsional moment on the e n in order to determine its buffet loading. Results are available for Mach numbers M =0.25, 0.60, and 0.80; angles of attack ®=25, 30, and 32.5 deg; and a sideslip angle i 15 deg < ¯ < 15 deg or a roll angle i 30 deg < ‐ ’ < 30 deg. At zero roll and sideslip the mean force and moment coefe cients generally increased with increasing angle of attack; but as the sideslip or roll angles increased some coefe cients changed sign, and their relationships to ® became more complex. A measurable trend of the mean force coefe cient to decrease with increasing Mach number was also observed. The rms normal force coefe cient increased signie cantly as ® increased from 25 to 32.5 deg, but showed no appreciable trends as the Mach number increased from 0.25 to 0.80. The wind-tunnel tests are complemented by a e ow-visualization study of the leading-edge extensions vortices of a 1:48 scale model of the F/A-18 in a water tunnel, showing the vortex burst locations at different aircraft orientations. Part 2 of this study presents statistical results of the forces and moments.
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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 | 0.000 |
| 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) | 0.004 | 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".