Laminar mixing of Newtonian and <scp>non‐Newtonian</scp> fluids in <scp>SMX</scp> static mixer
Bibliographic record
Abstract
Abstract This report is focused on understanding the mixing performance of Newtonian and non‐Newtonian fluids in an SMX static mixer through computational fluid dynamics (CFD) study in Simcenter STAR‐CCM+. For this, the standard SMX mixer with four mixer elements of radius 26 mm was chosen. The aspect ratio ( L / D ) was taken as 1. The model constructed was compared using pressure drop ratio values available in literature. First, the design was validated for Newtonian fluid, then for shear‐thinning fluid (non‐Newtonian). Mixing patterns produced through particle tracking approach revealed how blending improved as the fluid moved downstream and interacted with increasing number of mixer elements. A sensitivity study was undertaken to investigate the effect of particle injection mechanism on the mixing process. Two injection methods, vertical and horizontal, were explored to check the influence of injection position on mixing. The results indicate that for the given configuration irrespective of the injection location, at least four mixing elements are needed to achieve good mixing for both Newtonian and shear‐thinning fluids investigated in this study. Statistical reports were used to quantify mixing to understand the mixing behaviour for Newtonian and non‐Newtonian fluids with change in Reynolds number. At high Reynolds number, non‐Newtonian fluids exhibited better mixing abilities. Further, efforts were taken to optimize the various design parameters to produce the most optimum solution with a single mixer element for Newtonian fluid.
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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 | 0.000 |
| 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 | 0.001 | 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".