Modeling and Control of Dynamic Stall Loads on a Smart Airfoil at Low Reynolds Number
Bibliographic record
Abstract
This article describes the development and testing of a modified, semi-empirical ONERA dynamic stall model for an airfoil with a trailing edge flap—a “smart airfoil”—pitching at reduced frequencies up to 0.1. The Reynolds number is 105. The model reconstructs the load fluctuations associated with the shedding of multiple dynamic stall vortices (DSVs) in a time-marching solution, which makes it suitable for real-time control of a trailing edge flap (TEF). No other model captures the effect of the DSVs on the aerodynamic loads on smart airfoils. The model was refined and tuned for force measurements on a smart NACA 643-618 airfoil model that was pitching with an inactive TEF and was validated against the measurements when the TEF was activated. A substantial laminar separation bubble can develop on this airfoil, which is challenging for modelers of the unsteady response. A closed-loop controller was designed offline in SIMULINK, and the output of the controller was applied to the TEF in a wind tunnel. The results indicated that the model has a comparable accuracy for predicting loads with the active TEF compared to inactive TEF loads. In the fully separated flow regime, the controller performed worse when dealing with the development of the laminar separation bubble and DSVs.
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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) | 0.001 | 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".