Integrating mechanical mixing, headspace, and rheology in a computational model for a fermentation process
Bibliographic record
Abstract
Abstract Although computational fluid dynamics (CFD) modelling has been applied to study bioprocesses where mechanical mixing and aerations are key, the effect of having a free surface has usually been ignored. This work studies a mixing tank for bioprocess applications to analyze the impact that the assumption of a flat liquid level has upon the modelling results, with and without aeration. The methodology takes an experimental and modelling approach. A dual impeller mixer with a speed of 400 rpm is used for batch systems with both non‐Newtonian and Newtonian fluids to characterize the axis torque and surface vortex formation. Several multi‐phase CFD models are applied to study the modelling and numerical effects of the headspace on the results. Regarding modelling accuracy, the CFD models are shown to be able to capture the effect of the free surface on the fluid dynamics of the stirred tank for different fluid rheologies. Therefore, the simplification of the liquid level as a flat and fixed surface should not be applied, especially for a process with aeration or when unaerated conditions can lead to a surface vortex. Regarding the numerical accuracy, it is concluded that the mixture model does not predict the interface shape as well as the Eulerian model. However, only the mixture model shows to be numerically stable. Overall, this work provides validated CFD configurations able to predict the effect of a free surface on the mixing mechanisms in stirred reactors.
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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".