Investigation of mixing performance and flow characteristics of oil–water two‐phase flow in a novel swirling shear‐type static mixer
Bibliographic record
Abstract
Abstract Multiphase flow mixing plays a crucial role in industrial applications, enhancing reaction efficiency, product quality, and process stability. However, traditional static mixers often face challenges such as high pressure loss and stringent fluid property requirements. This study introduces a novel static mixer based on swirling and shear effects to address these issues. An experimental setup was developed to evaluate the mixer, which incorporates intersecting baffle plates and multiple columns of cutting teeth. Both experiments and computational fluid dynamics (CFD) simulations were conducted to analyze the internal flow field and mixing performance of the oil–water two‐phase system. The mixing performance of the sequential and cross arrangements of cutting teeth was compared. The results show that the combination of the baffle plate and cutting teeth significantly influences the oil–water flow pattern, creating a complex vortex system that enhances mixing efficiency. Notably, the cross arrangement of cutting teeth provides more uniform mixing and a lower pressure drop compared to the sequential arrangement. These findings offer valuable insights into the optimal design and scale‐up of the newly developed static mixer for industrial applications.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".