Reynolds Number Effects on Turbulent Wakes Generated by Rectangular Cylinders With Streamwise Aspect Ratios Between 1 and 4
Bibliographic record
Abstract
Abstract The effects of streamwise aspect ratio and Reynolds number on the separated shear layer and near wake of rectangular cylinders in uniform flow are investigated experimentally using a particle image velocimetry system. Four length-to-height ratios (AR = 1, 2, 3, and 4) were examined at Reynolds numbers (based on freestream velocity and cylinder height) of 3000, 7200, 14,700, and 21,000. The results show that the separated shear layer is either shed directly into the wake region (AR1 and AR2) or reattaches onto the cylinder (AR4), regardless of the Reynolds number. Meanwhile, a transitional regime occurs for AR3 where mean flow reattachment on the cylinder is highly dependent on the Reynolds number. The peak magnitudes of the Reynolds stresses, turbulent kinetic energy, turbulence production, and its transport are highest for AR1 owing to stronger vortex shedding. Aspect ratio and Reynolds number also have significant effects on shear layer transitioning from laminar to turbulence but the transition lengths, when normalized by the corresponding value at Re = 3000, follow a universal power decay law. The wake characteristics, including the recirculation length and wake formation lengths, are independent of Reynolds number for AR1 but decrease with Reynolds number for the longer cylinders, while AR2 shows the largest values. The probability density functions and joint probability density functions are used to examine the effects of Reynolds number on the fluctuating velocities and momentum transport in the shear layer and near-wake region.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".