Using Computational Fluid Dynamics Modeling and Ultrasonic Doppler Velocimetry To Study Pulp Suspension Mixing
Bibliographic record
Abstract
In this study, the flow field of a cylindrical pulp mixing chest equipped with a side-entering impeller was modeled using commercial computational fluid dynamics (CFD) software (Fluent) with the rheology of the pulp suspension approximated using the Herschel−Bulkley model. To validate the model, CFD results for the power and velocity field were compared to experimental data. Ultrasonic Doppler velocimetry (UDV), a noninvasive fluid flow measurement technique for opaque systems, was used to measure pulp suspension velocity. In order to calculate the mixing time, an unsteady state solver in Fluent was applied to monitor the tracer species concentration as a function of time in the tank. The validated CFD model provided useful information regarding the mixing time and the formation of the cavern around the impeller in the mixing of pulp suspension. The size of cavern predicted by the CFD model was in good agreement with that calculated using Solomon's model.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".