Simultaneous velocity-temperature measurements in the heated wake of a cylinder with implications for the modeling of turbulent passive scalars
Bibliographic record
Abstract
Two principal quantities of interest in applications of scalar mixing in turbulent flows are the mean scalar and the scalar variance. Reynolds-averaged approaches are computationally efficient numerical methods that predict these fields. They require, however, the introduction of models to close the governing equations. Oftentimes, the numerical constants employed in these models include, or depend upon, the turbulent Prandtl number (PrT) and the mechanical-to-thermal time-scale ratio (r), which are generally assumed to be constant, but flow dependent. The present work studies the sensitivity of PrT and r to differences in their injection method within the same flow. To this end, mixed velocity-temperature statistics were measured in the wake of a circular cylinder. The wake was heated by one of two ways: heating the cylinder or heating an array of fine parallel wires (called a mandoline) placed downstream of the cylinder. The experimental results demonstrate that the magnitude of PrT varies throughout the wake for both scalar fields (and is consistently greater than the typical value of 0.7 generally used in turbulence models). Likewise, the values of r vary across the wake for both scalar fields and are lower than the generally accepted value of 2. The principal result of this paper, however, is that the measured values of both PrT and r differ for the two scalar injection methods, despite being within the same flow field. Hence, both PrT and r not only depend on the type of flow but also on the scalar field injection mechanism method as well—a result that is generally not taken into account when modeling the mixing of scalars within turbulent flows.
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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) | 0.000 | 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 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".