Modeling Transverse Gusts Using Pitching, Plunging, and Surging Airfoil Motions
Bibliographic record
Abstract
Three model motions were developed to replicate the aerodynamic response of a transverse gust. These motions included a pure plunging and two three-degree-of-freedom motions that approximated the angle-of-attack distribution produced by the gust. Using inviscid models and viscous flow simulations, the responses of the gust and model motions were compared as a function of the nondimensional reduced frequency. The inviscid model was found to overestimate the influence of the rotational added mass in the three-degree-of-freedom motions. In contrast, the viscous flow simulations showed that the two primary sources of discrepancy between the gust and model motions lie in the nonlinear angle-of-attack distribution caused by the gust and the wake development during the model motions. Flow simulations showed that all three motions experienced greater than 90% agreement in lift for gusts with reduced frequencies less than 0.5, indicating that, under this reduced frequency, 1) the effect of the gust convection is minimal, and 2) a pure-plunging motion may suffice for modeling gusts. However, at higher reduced frequencies, the pure-plunging motion experiences greater than 10% worse agreement than the three-degree-of-freedom motions. Overall, the motions provide a good approximation with greater than 90% accuracy in lift for gusts of reduced frequencies less than .
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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) | 0.001 | 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".