An integrated <scp>CFD</scp> methodology for tracking fluid interfaces and solid distributions in a vortexing stirred tank
Bibliographic record
Abstract
Abstract Distribution of solid phase in a solid–liquid suspension being mixed in a vortexing, unbaffled stirred tank is difficult to model numerically. The need is to be able to predict the shape of the vortex (air–water interface) and the distribution of solids in the liquid domain. Typically, the problem is approached with assumptions about the shape of the interface to capture the solid distribution through a multi‐phase Eulerian model (doi: 10.1016/j.ces.2018.07.023 ). In this work, a multi‐step modelling framework for multi‐phase systems that have a free surface along with the dispersion of secondary phase(s) in the liquid domain is proposed. To demonstrate the method, it is applied to a laboratory‐scale vortexing unbaffled system reported in the literature (doi: 10.1021/ie071225m ) and a pilot‐scale tank (doi: 10.1016/j.cej.2018.10.020 ). The predictions from the computational fluid dynamics model are compared with the experimental profiles of solid volume fractions. Using the model, the effects of solid density, particle size, particle loading, and impeller speed are investigated for the laboratory‐scale system. An interesting self‐similar nature in the axial distribution of solid is observed when the loading is varied from 0.5 to 10 volume percent.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".