Dynamic & CFD modeling of a continuous-flow mixer using fluids with yield stress
Bibliographic record
Abstract
A continuous-flow mixer was designed and built in the Mixing Technology Lab, Chemical Engineering Department at Ryerson University to study mixing of xanthan gum solutions in water, a pseudoplastic fluid possessing yield stress. The extent of flow non-ideality was quantified using a dynamic model that incorporated the extent of channeling and the effective mixed volume within the mixing vessel. Dynamic tests were made using a frequency-modulated random binary input of a brine solution. The same experiments were simulated using Fluent, a Computational Fluid Dynamics (CFD) package. CFD flow fields were used to obtain the system dynamic response to a tracer injection applied at conditions indentical to the experimental conditions. The extent of channeling and effective mixed volume were determined and then compared with the parameters obtained experimentally. Experimental and CFD results show that the extent of non-ideal flow is significantly affected by impeller speed, impeller type, feed flow rate, fluid rheology, and exit location. The performance of continuous mixed vessels can be improved by increasing impeller speed, decreasing feed flow rate, and decreasing solution concentration. However, decreasing feed flow rate and solution concentration reduces the production capacity of the process. Increasing impeller speed may require modification to the motor and can cause air entrainment. Therefore, other remedies such as relocating the exit location and using the proper type of impeller may be taken into consideration. The results show that the extent of non-ideal flow was reduced using the bottom output and flow efficiency in the vessel was enhanced using A320 impeller.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".