Effect of Salinity on Drag Reduction of Additives and Mixtures under Turbulent Flow
Bibliographic record
Abstract
The performance of partially hydrolyzed polyacrylamide (HPAM), which is the most commercially used polymer in drag reduction (DR) applications, is affected by several factors. These factors include Reynolds number, polymeric concentration, molecular weight, and, more importantly, salinity conditions, which can dramatically impact the polymeric structure and its behavior. In the current work, a deep analysis is done on the performance of HPAM at salinity levels mimicking industrial conditions by using an industrial-scale fluid flow loop and a rotational rheometer. The impact of salinity on DR performance and degradation rates of HPAM was investigated at various molecular weights and a fixed concentration and then fitted with exponential decay models. Then, measurements of DR of the additives alone at different concentrations as well as blends of two salt-resisting polymers, i.e., xanthan gum (XG) and poly(ethylene oxide) (PEO), were analyzed in tap water and in brine at different mass ratios. Our results showed that the presence of salts led to the drop of the DR of HPAM to almost half its value in tap water, while PEO was found to have an increase in the DR, and XG maintained nearly the same performance. The HPAM–XG mixtures had higher levels of improvement in both media and a slight improvement in the DR in brine over those of HPAM measurements alone. In the case of the HPAM–PEO mixture, the DR is substantially increased compared to HPAM alone. The results of this work confirm that conventional solutions, represented by the physical mixing of HPAM with inexpensive and environmentally friendly additives, are possible and can lead to a massive reduction in energy and freshwater consumption in industrial applications such as hydraulic fracturing.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 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".