Estimation of Drag Reduction by Polymer Additives at High Reynolds Numbers Using Rheological Measurements
Bibliographic record
Abstract
In industrial applications such as hydraulic water fracking, polymers are added at dilute concentrations to the flow causing significant drag reduction (DR) and leading to a massive reduction to pump energy cost. In recent years, an alternative approach that is based on rotational flow has shown capabilities of measuring DR based purely on rheological testing. Nonetheless, there are some limiting assumptions in this approach that can lead to inaccurate interpretation of the data, especially for non-Newtonian polymer solutions, where the Reynolds number ( Re ) is evaluated at the infinite-shear or solvent viscosities. However, it is well known that the apparent viscosity of the polymer solutions is higher than that of a solvent at a stable region and lower than infinite shear viscosity at an unstable one. In this study, we propose a theoretical form of the DR expression that is based on the Re, which is estimated at the apparent local viscosity measure. The work establishes a promising approach for screening DR agents using rheological measurements. Moreover, the study presents new theoretical findings and analyses for estimating DR and extrapolating the results to high Re . Two polymer solutions of xanthan gum (XG) and partially hydrolyzed polyacrylamide (HPAM) in distilled water are tested at concentrations between 5 and 150 ppm using a concentric double-gap cylinder. The proposed approach is found to be more consistent with the theory of linear flow (i.e., flow loop), where the DR in the stable region is found to be identically zero. The transition from stable to unstable regions is also consistent with the existing linear flow theory. This enhances the role of rheological testing for DR measurements and DR agent screening, which provides a platform for the application of simple and cost-effective rheometry in the DR industry.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".