Interactional Aerodynamic Insight Obtained from Wind Tunnel Testing and Computational Analysis
Bibliographic record
Abstract
Interactional aerodynamic interactions between various rotorcraft components can make a large contribution to steady and unsteady loads, performance, and vibration. Wind tunnel results from a powered model test have been analyzed to identify trends in the unsteady aerodynamic pressures on the horizontal stabilizer. Flow velocity measurements were also made behind the fuselage, rotor hub, and blades. Velocity components in all three directions were separated into time-averaged, periodic, and broadband components to identify factors contributing to unsteady tail loads and provide validation data for analysis. Computational Fluid Dynamics (CFD) has been applied to four configurations of the wind tunnel model. The calculated steady rotor and fuselage forces and the unsteady tail pressures have been compared to experiment. CFD has also been applied to a flight test configuration and the results compared to measured stabilizer accelerations. When all relevant components are included, the CFD analysis captures many key features, but there remains room for improvement in resolving the quantitative details.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".