Passive flow control in the wake of bed-mounted cylinders: a review and new experimental insights
Bibliographic record
Abstract
Controlling wake flow behind bed-mounted circular cylinders is required in many engineering applications. This study investigates several passive flow control mechanisms, including the use of helical strakes, meshes, foam coverings, slots, and holes. A detailed review of existing experimental and numerical studies is conducted to examine the principles, applications, and effects of each method. While extensive research exists on individual flow control techniques, comparative studies between different methods are notably lacking. Additionally, variations in approach flow conditions across studies highlight the need for a systematic evaluation under identical or near-identical inflow conditions. To address these gaps, particle image velocimetry measurements were employed to capture and compare unsteady flow dynamics in the wake of various types of bed-mounted circular cylinders. Experiments were conducted at Reynolds number of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="m1"><mml:mrow><mml:mn>1.45</mml:mn><mml:mo>×</mml:mo><mml:msup><mml:mn>10</mml:mn><mml:mn>4</mml:mn></mml:msup></mml:mrow></mml:math> , based on the cylinder diameter. The results show that the plain cylinder’s wake flow is dominated by a large recirculation region and downstream bifurcation, while the meshed cylinder shows minimal deviation from these characteristics. The cylinders with holes generate localized turbulence and spiraling flow patterns, with diminishing effects downstream. The helical strakes induce unique channelized flow and sustained turbulence downstream, whereas the single and cross-slotted cylinders enhance near-wake turbulence and velocity via high-velocity jets, particularly in the cross-slotted configuration, making it ideal for heat transfer applications. In contrast, foam-covered cylinders effectively reduce turbulence and flow fluctuations, offering potential for applications requiring flow stability and drag reduction.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".