Drag Reduction by Polymer Additives: State-of-the-Art Advancements in Experimental and Numerical Approaches
Bibliographic record
Abstract
This comprehensive literature review delves into various aspects of the drag reduction (DR) phenomenon. To address respective aspects as well as track authors and research outputs, a bibliometric analysis is conducted using VOSviewer software. Hence, the review encompasses an examination of the state-of-the-art mechanisms and methodologies for DR measurements and quantifications, employing both linear and rotational devices. Recent advancements in DR measurements and the application of closed-form relations via rotational flow devices as standalone testing techniques are presented, with a discussion on their potential to replace linear flow devices. Furthermore, the review highlights the drawbacks associated with commercially used polymers and explores recent research addressing these issues. Potential avenues for improving the DR performance of these polymers, such as chemical modifications to enhance DR performance and shear resistance, are examined. The review also explores the role and future possibilities of incorporating nanoparticles in DR. In addition, a thorough analysis of current numerical models used in simulating DR, along with their applications and limitations, are addressed. In summary, this review aims to comprehensively cover the advances in various aspects of the DR fields, catering to both industrial and academic audiences, and offering introductory and in-depth insights into the world of DR.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".