Solid distribution and mixing time in stirred tanks: The case of floating particles
Bibliographic record
Abstract
Abstract This work concerns floating particle distribution and liquid mixing dynamics in a solid‐liquid stirred tank. Measurements of local solids concentration distribution at steady‐state conditions and of liquid homogenization in the presence of dispersed particles at transient conditions are collected with up‐pumping and down‐pumping pitched blade turbines. Electrical resistance tomography is the selected experimental technique for the data acquisition in the opaque solid‐liquid mixture. For the time dependent conductivity data processing, the same method commonly applied to the mixing time determination from planar laser induced fluorescence measurements is considered. The method is successfully extended to the characterization of liquid mixing with floating particles. The local data obtained by the ERT technique over the whole vessel volume are fully exploited. The robustness of the method for tackling conductivity variations close to the liquid free surface and due to the particles movement is assessed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".