Effect of Polymer Fluid Viscoelastic Properties on the Initiation of Transition From Laminar to Turbulent Flow Regime and the Drag Reduction in the Flow Through Horizontal Pipe
Bibliographic record
Abstract
Abstract This study investigated the flow of viscoelastic fluids through horizontal pipeline mainly focusing on the effect of fluid elasticity on drag reduction and onset of transition to turbulent flow regime. In order to be able to see the sole effect of fluid elasticity (independent from shear viscosity), three non-Newtonian fluids having the same shear viscosity but different viscoelastic properties were tested in the horizontal flow loop. Those fluids were the dilute solutions of partially hydrolysed polyacrylamide (HPAM) and they were prepared by using three polymer grades of HPAM (i.e. 5 × 105, 8 × 106, 20 × 106 g/gmol) in different compositions. Experiments have shown that increasing fluid elasticity resulted in higher drag reduction in pipe flow. Moreover, fluid elasticity affected the onset of turbulent flow and an earlier transition to turbulent flow regime (as compared to water flow) was only observed for the flow of fluid having the highest elastic properties. So, understanding effects of fluid elasticity on flow dynamics might improve the performance of fluids engineered for hole cleaning/cuttings transport in oil and gas well drilling or proppant transport in hydraulic fracturing operations. Also, field efforts to find solutions to problems caused by excessive dynamic pressure losses encountered in drilling horizontal or extended reach wells or in transporting hydrocarbons through pipeline might benefit from the findings of this or further extended research on this subject.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".