Numerical modelling of a low Reynolds number plunging airfoil flow field characteristics
Bibliographic record
Abstract
Complex viscous mechanisms such as leading edge vortices play a dominant role in the generation of instantaneous force and moment in low Reynolds number flows. The dependence of the corresponding fluid flow characteristics on the governing flow and system parameters in unsteady motions, e.g. plunging, adds to the inherent complexity of the problem. The respective fluid dynamics of such a flow is investigated here via computational fluid dynamics based on a finite volume method. The governing equations are the unsteady, incompressible two-dimensional Navier–Stokes equations. The flow field and vortical patterns around a thin ellipsoidal plunging airfoil are examined in detail with and without freestream velocity, and the effects of Reynolds and Strouhal numbers on the flow characteristics are explored. It is shown that both Reynolds and Strouhal numbers increase the aerodynamic performance in nonzero freestream velocity simulations. Increasing Reynolds and Strouhal numbers causes the airfoil to generate thrust for some time intervals of the plunging period. This thrust generation is penalized with higher peaks of drag coefficient when Strouhal number increases. However, the same penalty in the Reynolds number effect simulations is negligible compared to that of the Strouhal number effects. Increasing Strouhal number causes the airfoil to experience negative pitching moment with higher peak values for longer time intervals, but Reynolds number does not change the time at which negative pitching moment is exerted on the airfoil, but the peaks of pitching moment depend on the governing Reynolds number. The lift coefficient changes noticeably versus Strouhal number, where there is significant lead/lag at the peak lift coefficient for zero-freestream velocity simulations. Reynolds number effects on the lift coefficients mostly occur around the time at which the peak lift coefficient is obtained for both zero and nonzero freestream velocity cases. All of these effects are caused by the complex vortical patterns around the airfoil, described throughout the present article.
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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 | 0.001 | 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".