Power consumption in a moving baffle oscillatory baffled reactor: A <scp>CFD</scp> study
Bibliographic record
Abstract
Abstract Oscillatory baffled reactors (OBRs) can perform high‐intensity mixing under low power consumption, and thus are viable replacements for stirred tank reactors in biological, chemical, and polymer processes. This study simulated the flow inside a moving baffle OBR with single orifice baffles using computational fluid dynamics (CFD). The effect of operational and geometrical parameters along with the fluid density and viscosity on average power consumption and maximum power consumption was investigated, and appropriate correlations for both average and maximum power consumption were obtained. It was found that average and maximum power consumption are independent of viscosity, and amplitude has a greater impact on maximum power consumption than on average power consumption. These correlations were then compared with available power models (that showed an acceptable level of discrepancies) in the literature. Lower power consumption values obtained from CFD results compared to those obtained from quasi‐steady state model (QSM) and eddy enhancement model (EEM) models (developed for stationary baffle OBRs) under the same operating conditions, along with higher axial dispersion of moving baffle OBRs compared to stationary baffle types under the same operating conditions, indicated that a moving baffle OBR is a more efficient mixing device than a stationary baffle OBR in terms of power consumption. The ratio of average power consumption to maximum power consumption was proven to be independent of the type of fluid and a very weak function of oscillation frequency.
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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 | 0.001 | 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 | 0.001 |
| 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".