Study of fluid dynamic conditions in the selected static mixers part III—research of mixture homogeneity
Bibliographic record
Abstract
Abstract This paper contains the analysis of mixture homogeneity in two types of static mixers, Koflo and Kenics, based on both experimental and CFD study. The research was also expanded to the recognition of the pipe that was used as a background. The scope of work includes the analysis of the CoV coefficients (as a measure of mixing degree) obtained for the mentioned devices and their comparison. The research showing the impact of such parameters, i.e., pressure drop or L/d ratio on the mentioned mixture homogeneity was also presented. What is more, the axial dispersion model was introduced to describe the devices as non‐ideal reactors. According to this, new relations for the CoV predictions (taking into consideration the turbulence intensity, the deviations from well‐known ideal states, and the mixing elements’ shape) were developed. As a result, it was proven that static mixers are highly efficient devices because the obtained CoVs were at around 2 % in the laminar flow and even less than that in the turbulent regime. When the pipe was used, the satisfactory value of the CoV was obtained when the Reynolds number was increased to 2600.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".