Assessing Mixing Performance of a Static Mixer Using Computational Fluid Dynamics
Bibliographic record
Abstract
Static mixers are in-line motionless devices that can be placed into a pipe to promote the blending of miscible fluids or dispersion of immiscible liquids. These inserts are characterized by the mixing performance and the pressure drop they create. New designs of static mixers are continuously proposed to meet certain requirements of the final product. Instead of manufacturing numerous prototypes of different designs and conducting costly experiments to assess the characteristics of the inserts, it is suggested to use computational fluid dynamics (CFD) to visualize and quantify new insert designs. In this study, we demonstrate how CFD can be efficiently used to quantify the mixing performance of a six-element Kenics mixer. A system of two miscible liquids is numerically replicated by considering a single-phase incompressible flow coupled with the solution of a passive scalar equation that replicates the injection of similar fluid with dye in it. A commercial CFD package STAR-CCM+, Siemens PLM was used to perform simulations.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".