A Recipe for Optimum Mixing of Polymer Drag Reducers
Bibliographic record
Abstract
Preparation of large-scale homogeneous solutions of drag reducing polymers requires an appropriate mixing procedure to ensure full disentanglement of the polymer chains without chain scission due to over-mixing. The latter is known as mechanical degradation and reduces the performance of drag reducing polymers. The dominant large-scale mixing parameters including time, impeller type, impeller speed, and impeller-to-tank diameter ratio are investigated to obtain a recipe for maximum mixing with minimum polymer degradation. Three water-based solutions of 100 ppm Superfloc A-110 (flexible structure), Magnafloc 5250 (flexible structure), and Xanthan Gum (XG) (rigid structure) are considered. The performance of the mixing parameters for each polymer is evaluated based on the solution viscosity in comparison with the highest viscosity (i.e., optimum mixing) obtained by 2 h of low-shear mixing of a small-scale polymer solution using a magnetic stirrer. The results demonstrate that optimum large-scale mixing is obtained at mean and maximum shear rates of ∼17 s−1 and ∼930 s−1, respectively, after 2–2.5 h of mixing for each of the polymers. This shear rate is obtained here using a three-blade marine impeller operating at 75 rpm and at impeller-to-tank diameter ratio of 0.5. The resulting polymer solution has the highest viscosity, which is an indication of minimal degradation while achieving complete mixing. It is also confirmed that chemical degradation due to contact with a stainless steel impeller is negligible.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".