Using tomography, CFD, and dynamic tests to study the continuous-flow mixing of yield-pseudoplastic fluids
Bibliographic record
Abstract
The major technological challenges faced by modern chemical industries are non-ideal flows such as dead zones and channeling encountered in the mixing of fluids with complex rheology. These cause sub-optimal mixing and lead to low quality products and high costs of raw materials. Therefore, the core objectives of this study were to develop methodology and tools to design an efficient continuous-flow mixing system for the fluids with complex rheology using electrical resistance tomography (ERT), computational fluid dynamics (CFD), and dynamic tests. The xanthan gum solution, which is a pesudoplastic fluid with yield stress, was used to study the dynamic behavior of the continuous-flow mixing process. The power consumption, cavern size, mixing time, and the extents of channelling and the fraction of fully mixed volume were successfully determined using dynamic tests, ERT tests, and CFD simulations and used as mixing quality criteria. A novel and efficient method was developed for flow visualization in the continuous-flow mixing of opaque fluids using 2D and 3D tomograms. A unique study on identifying the sources of flow non-ideality in non-Newtonian fluids with yield stress was done by visualizing the flow pattern inside the continuous-flow mixing vessel using 2D and 3D tomograms. The deformation of the cavern was analyzed and quantified in the continuous-flow mixing system for yield-pseudoplastic fluids using ERT. Moreover, the cavern volume was compared with the fully mixed volume and it was found that the latter was higher due to the extra momentum induced by the inlet-outlet flow. A novel study on exploring the effect of the rheological parameters of the pseudoplastic fluids with yield stress on the non ideal flows in a continuous-flow mixing system was performed using CFD. The CFD results revealed that the mixing quality was improved when the degree of the shear thinning was increased. The ratio of the residence time to the batch mixing time was evaluated to achieve ideal mixing for the continuous-flow mixing of yield-pseudoplastic fluids using dynamic tests and ERT. It was found that the ratio of residence time to the batch mixing time should be at least 8.2 or higher to achieve ideal mixing.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".