Computational fluid dynamics (CFD) analysis of mixing in styrene polymerization
Bibliographic record
Abstract
A styrene polymerization in a lab-scale CSTR equipped with a pitched blade turbine impeller was simulated using the computational fluid dynamics (CFD) approach. The impeller motion was integrated in the geometry using the multiple reference frame (MRF) technique. The presence of non-linear source term and the highly coupled nature of transport equations of the polymerization, made the convergence difficult to achieve. The effects of the impeller speed, the input-output locations and the residence time on the polymerization in the CSTR were investigated. The CFD simulation shows that good mixing remained limited to the impeller region. Regions far from the impeller remained unmixed due to high viscosity of the polymer mass. The path lines of the particles, released at the inlet, were also generated to analyze the reaction progress as the chemicals travel throughout the reactor. The monomer conversion computed using the CFD model was compared to data reported in the literature. Conversion predicted using the CFD model is in good agreement with that obtained from the CSTR model at low residence time. However, the CFD predicted coversions were higher than those calculated from the CSTR model, at high residence time. It was found that the input-output locations had significant effect on the conversion and the homogeneity in the CSTR.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".