Upscaling drag reduction of rotational rheometer to linear pipe flow
Bibliographic record
Abstract
Drag reduction (DR) is a phenomenon associated with adding small amounts of drag-reducing agents to reduce friction causing a reduction in the pressure gradient needed for pumping a solution under turbulent conditions. Traditionally, DR has been measured with linear flow devices, such as flow loops. Recent developments in rheological measurement techniques have enabled the evaluation of DR using rotational rheometers. However, due to differences in flow nature and length scale, direct comparison between outcomes is not possible. This study introduces nondimensional quantities, namely dimensionless pressure difference for pipe flow and dimensionless torque for rheometers, as the basis for comparing the DR results of linear and rotational flow. Theoretically, the DR expressions derived from these dimensionless quantities showed similar structures, featuring a factor with quadratic viscosity and linear density terms. Experimentally, DR tests were carried out using an industrial-scale pipe flow and a laboratory-scale rotational rheometer, using solutions of tap water with high-viscosity partially hydrolyzed polyacrylamide at two molecular weights. Samples tested with the rheometer were collected from flow loop experiments, ensuring the same polymer solutions were tested with both devices. Results showed that DR, expressed as a function of Reynolds number using nondimensional measurements, follows similar behavior for both instruments. The experimental DR results from the rheometer were extrapolated to the pipe flow scale, showing overall agreement between extrapolated and experimental results. These findings suggest that rotational rheometers could effectively replace linear flow instruments for screening polymer solutions in DR applications.
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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 | 0.002 | 0.004 |
| 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 | 0.000 | 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".