Using Computational Fluid Dynamics To Study the Dynamic Behavior of the Continuous Mixing of Herschel−Bulkley Fluids
Bibliographic record
Abstract
A continuous-flow mixer was designed and built to study the mixing of xanthan gum solution, a pseudoplastic fluid possessing yield stress. The extent of flow nonideality was quantified using a dynamic model that incorporated two parameters: channeling and fully mixed volume in the vessel. Dynamic experiments were made using the frequency-modulated random binary input of a brine solution to determine the magnitude of nonideal flow parameters. The same experiments were simulated using a computational fluid dynamics (CFD) package (Fluent 6.2). CFD flow fields were used to obtain the system dynamic response to a tracer injection applied at conditions identical to the experimental ones. The extents of channeling and effective mixed volume were determined using the CFD model and then compared with the parameters obtained experimentally. Validated CFD flow fields enabled us to effectively monitor the effect of various operating conditions on flow nonideality, to relate flow pattern and cavern dimension to flow nonideality, to compare the efficiency of impellers, and to provide a pictorial synopsis of continuous-flow mixing operation.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".