Numerical investigation on aerodynamic characteristics of two rotating cylinders in tandem
Bibliographic record
Abstract
To solve the problem of unsteady wake and high drag in the flow around a cylinder, combined with the advantages of two cylinders in tandem in drag reduction and rotating cylinder in restraining wake oscillation, the k−ω Shear Stress Transport turbulence model was used to explore the aerodynamic characteristics and flow field structures of two rotating cylinders in tandem at different speed ratios under the condition of Re = 200, gap ratio L/D = 1.5, 2.5, and 3.5, respectively. The effectiveness of the method is verified by comparing the results with the available experiments. The streamline, periodic averaged lift and drag coefficient and the pressure coefficient on the cylinder surface are given and analyzed in detail. It was found that for the rotating single cylinder, when the speed ratio is 4.1, it has best the aerodynamic performance. For the rotating double cylinders, the higher gap ratio can increase the combined lift coefficient and reduce the combined drag coefficient under the condition of bigger speed ratios of cylinder 1. When the gap ratio is 3.5, the rear cylinder is stationary or has a higher speed ratio, it can restrain the wake oscillation of the front cylinder. When the front to rear cylinder speed ratio is 2 and 1, respectively, the aerodynamic characteristics of the rotating double cylinder system have best performances.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".