Characterisation of the mixing of non‐newtonian fluids with a scaba 6SRGT impeller through ert and CFD
Bibliographic record
Abstract
Abstract An attempt has been made to study the mixing of yield‐pseudoplastic fluids with a Scaba 6SRGT impeller using electrical resistance tomography (ERT) and computational fluid dynamics (CFD). The ERT system with four sensor planes, each containing 16 equispaced stainless steel electrodes, was used to measure the mixing time. The multiple reference frames (MRF) technique and the modified Herschel–Bulkley model were applied to simulate the impeller rotation and the rheological behaviour of the non‐Newtonian fluids, respectively. To validate the model, the CFD results for the power consumption were compared to the experimental data. The validated model was then employed to obtain further information regarding the averaged impeller shear rate, impeller circulation, and pumping capacities. The CFD and ERT data were utilised to investigate the effect of the impeller power, fluid rheology, and impeller size on the mixing time. The mixing time results obtained in this study were in good agreement with those reported in the literature. © 2011 Canadian Society for Chemical Engineering
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".