Gas Dispersion in Highly Viscous Fluids with a Coaxial Mixer through Tomography and CFD
Bibliographic record
Abstract
The hydrodynamics of the gas-liquid flow in a bioreactor containing a highly viscous fluid was investigated using the non-invasive flow visualization technique called electrical resistance tomography (ERT) and computational fluid dynamics (CFD).The bioreactor was furnished with a coaxial mixer [1] composed a close clearance impeller (e.g. an anchor impeller) and a central impeller (e.g. a pitched blade turbine).The dynamic gas disengagement theory coupled with the tomography data [2] was employed to determine the local and global gas holdups, number of bubble size classes, contribution of each class of bubbles, and the bubble sizes within the bioreactor.We also developed a computational fluid dynamics model (CFD) using the Eulerian-Eulerian method coupled with the population balance model to assess the behaviour of bubbles in the viscous fluids.To model the rotation of both impellers (i.e. the central impeller and the anchor impeller), the sliding mesh technique was used.The bubble size distribution within the aerated coaxial mixer was estimated using the Multiple Size Group (MUSIG) model including the bubble breakage and coalescence kernels.The Laakkonen breakage kernel [3] was utilized to simulate the breakage frequency.The coalescence rate was also determined through Abrahamson's model [4].The modified Tomiyama model [5] based on the Brucato model was used to estimate the drag force.To validate the CFD model, the simulated Sauter mean bubble diameters were compared with the experimental values obtained from the tomography images.In this study, the tomography data and the validated CFD model were employed to assess the effects of the speed ratio (central impeller speed / anchor speed), rotating mode (co-rotating and counter-rotating modes), fluid viscosity, and gas flow rate on the gas holdup, mixing time, power consumption, and bubble size distribution.The data obtained in this study, enabled us to develop two correlations for the gas flow number and power number.The results revealed that at the higher fluid viscosity, the anchor power consumption increased at the speed ratios higher than the critical value of 10.The CFD results showed that the breakage was more pronounced at the bottom of the tank while the coalescence was more dominant at the top of the tank.It was found that at the speed ratio of 10, the volume fraction of the large bubbles decreased while the volume fraction of the small bubbles increased.The turbulent kinetic energy achieved in the counter-rotating mode was less than that attained in the co-rotating mode.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".