Analytical and numerical investigations of mixing fluids in microchannel systems of different geometrical configurations
Bibliographic record
Abstract
Abstract This work presents theoretical and numerical studies related to micromixing phenomena using two different shapes of microchannel systems (i.e., X‐ and Y‐shaped, respectively). In this study, we consider a system that consists of a primary fluid, an aqueous phase (Fluid A), and a secondary fluid (distributed phase), Rhodamine B, in water (Fluid B). In this study, a two‐dimensional closed‐form generalized analytical model is developed and solved using the method of separation variables to understand the fluid flow mixing behaviour under the influence of a convective–diffusive mass transport process. In addition, numerical simulations are also performed by solving the continuity, momentum, and mass transport equations for the two proposed microchannel systems under different flow conditions to understand the relative effects on micromixing phenomena resulting from the convection and diffusion mass transport. Results obtained from the numerical simulations evaluate the mixing performance by varying the inlet flow velocity of the secondary fluid stream (Fluid B: Rhodamine B in water). The numerical result in terms of the radial concentration distribution profile as a function of channel width based on the operating Reynolds number by varying inlet feed flow velocity (Fluid B) shows that more effective mixing has been carried out by the X‐shaped microchannel compared to the Y‐shaped microchannel. Moreover, the proposed generalized analytical model was validated with the obtained numerical results in terms of the normalized concentration distribution as a function of the normalized channel width. A good agreement between the analytical and obtained numerical results ( for both shapes of microchannel systems has been observed.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".