Bibliographic record
Abstract
Although natural and industrial flows often transport and mix multiple scalars, relatively few studies of turbulent multi-scalar mixing have been undertaken. In the present work, a novel three-wire thermal-anemometry-based probe – capable of simultaneously measuring velocity, helium concentration and temperature – is used to investigate the evolution of multiple scalars ( $\phi _1$ , $\phi _2$ , $\phi _3$ ) and velocity in turbulent coaxial jets. The jets consist of (i) a centre jet containing a mixture of helium and air ( $\phi _1$ ), (ii) an annular jet containing pure (unheated) air ( $\phi _2$ ) and (iii) a coflow of (pure) heated air ( $\phi _3$ ). Axial measurements are made at three different momentum flux ratios ( $M=0.77, 2.1, 4.2$ ). Increasing $M$ was observed to result in complex, competing effects. Larger momentum flux ratios cause the potential core of the centre jet to decrease in size, greater scalar fluctuations and more rapid correlation of $\phi _1$ and $\phi _2$ . However, at the same time, certain statistics, including those describing the velocity field, evolve more slowly. Moreover, the flow near the beginning of the fully merged region appears to be less mixed at higher values of $M$ . The present work finally demonstrates that differences can be observed in the evolution and mixing of coaxial jets between those in which $M<1$ and those in which $M>1$ , thereby presenting an opportunity by which the mixing process in coaxial jets may be controlled.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".