Numerical Investigation of the Effect of Shock-Induced Flow Separation on Dynamic Stall in Time-Varying Freestream Conditions
Bibliographic record
Abstract
Dynamic stall on an oscillatory pitching NACA 0012 airfoil was simulated via CFD under time-varying freestream conditions, which are representative of fast forward flight for a helicopter and which lead to transonic effects at the minimum of the pitch angle. The selected flow conditions were representative of a helicopter blade section at around 80% of the blade radius at the extreme of the forward flight speed, i.e. at around Mach 0.3. A sinusoidal pitch angle variation of 15° ± 10° and an out-of-phase sinusoidal freestream variation of Mach 0.48 ± 0.3 were applied at a mean reduced frequency of 0.029. The mean Reynolds number was approximately 3.3x10⁶. First, the simulation results for steady freestream dynamic stall were carefully validated with experiment. Then, the steady and unsteady freestream dynamic stall simulations were compared. Key findings include the lift coefficient hysteresis loop changing direction for the variable freestream conditions, as well as the peak values of lift, drag and negative pitching moment coefficients increasing by factors of approximately 2∼4. Results suggest that the mechanism of dynamic stall changes dramatically due to the occurrence of transonic flow at minimum angle of attack and confirms that inclusion of time-varying freestream in dynamic stall simulations is important for the extremes of the forward flight speed.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".