A Novel Approach For Analyzing Mixing Quality In Solid-Liquid Stirred Tanks Via Coupled Computational Fluid Dynamics - Discrete Element Method And Electrical Resistance Tomography
Bibliographic record
Abstract
The mixing quality of a solid-liquid stirred tank operating in the turbulent regime was investigated, numerically and to an extent experimentally. Simulations were performed by coupling Computational Fluid Dynamics (CFD) and the Discrete Element Method (DEM). The results were evaluated against experimental data obtained using Electrical Resistance Tomography (ERT). This facilitated a novel and more rigorous assessment of CFD-DEM coupling – i.e. based on the spatial distribution of particle concentrations. Furthermore, a new mixing index definition was developed to quantify suspension quality to work in tandem with existing dispersion mixing indexes. This provides a more complete interpretation of mixing quality. In this work, it was found that the model underestimated suspension and dispersion due to model limitations associated with mesh size and fluid-particle interaction models. Furthermore, the predicted mixing quality was sensitive to changes in the drag model, including other fluid-particle interaction forces in simulations, and variations in certain particle properties
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".