Using electrical resistance tomography (ERT) and computational fluid dynamics (CFD) to study the mixing of yield-pseudoplastic fluids in the SMX static mixer
Bibliographic record
Abstract
In this study, both electrical resistance tomography (ERT) and computational fluid dynamics (CFD) were employed to study the performance of the SMX static mixer in the mixing of a secondary fluid in a yield-pseudo plastic primary fluid. Using ERT, the effects of the primary fluid rheology, the primary fluid flow rate, and the secondary fluid type (Newtonian and non-Newtonian) were investigated. A CFD model was then developed for the fluid mixing in the SMX static mixer and was validated using the experimental pressure drop and the ERT mixing index measurements. Using the validated CFD flow model, the effects of the primary/secondary flow ratio and the secondary fluid viscosity on the mixing performance of the SMX static mixer were analyzed. The results from this study revealed that the SMX static mixer was effective for the mixing of highly viscous fluids especially at a lower primary/secondary flow ratio.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".