Improvement of mass and heat transfer efficiency in a scale‐up microfluidic mixer designed by <scp>CFD</scp> simulation
Bibliographic record
Abstract
Abstract Due to scale effects, directly enlarging the size of the micromixer is an easy way to reduce the efficiency of mass and heat transfer in the continuous flow chemical process. It is urgently needed to solve the problem of mass and heat transfer efficiency of the scale‐up mixer. A scale‐up microfluidic mixer with a porous structure was designed to improve the mass and heat transfer efficiency using computational fluid dynamics (CFD) simulations. The effects of rotation angle, porosity, and baffle spacing were studied to optimize the mixer structure. Compared with the 1 mm mixer without structure, the scale‐up mixer has a higher mixing efficiency and an 80% reduction in energy consumption at Re ≥ 700. A Nusselt number was used to evaluate the heat transfer efficiency of the mixer during fluid heating. The results show that the porous baffle promotes the generation of secondary flow and enhances the heat transfer effect, making its Nu increase by three times compared with the unstructured mixer. The scale‐up microfluidic mixer with a porous structure can effectively improve the mass and heat transfer performance. This study can provide a reference for the design or development of a novel scale‐up mixer.
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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".