Experimental Study and Turbulence Dissipative Scale Modelling of the Rapid Micromixing of Impinging, Paper-Thin Sheets of Liquids
Bibliographic record
Abstract
Previous studies have shown that the impingement of thin liquid sheets produces high energy dissipation rates due to the release of kinetic energy in a very small volume of liquid (0.0001 to 0.01 g), even though flowrates are on the order of L/min. Rapid micromixing occurs because the dissipated energy leads to a substantial reduction in the initial segregation size scale of the liquids, which is the single-sheet thickness at impingement (~ 100 μm). In the present study, the micromixing was investigated by following the progress of an acid-base neutralization accompanied by a change in fluorescence intensity of a fluorophore. Micromixing was modeled using a framework that assumes diffusion and reaction of species occur within slabs of fixed thickness (2L). The slabs are fixed in size because the released kinetic energy is dissipated within one turnover time of the large energy-containing eddies produced in the turbulent impingement zone. A simulation, which included a module for calculating the fluorescence intensity, determined 2L for experimental energy dissipation rates ranging from 40,000 to 7,700,000 W/kg. 2L was found to lie in the range of turbulent dissipative scales less than the Taylor microscale but greater than the Kolmogorov microscale. 2L is a function of the energy dissipation rate, kinematic viscosity, large-eddy Reynolds number and fluctuating turbulent velocity. For some correlations, 2L follows the same relationship as the Taylor microscale, but for others, the relationship is analogous to the Kolmogorov microscale.
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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 | 0.000 | 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) | 0.001 | 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".