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DRAG REDUCTION BY POLYMERS: A BRIEF REVIEW OF THE HISTORY, RESEARCH PROGRESS, AND PROSPECTS

2021· review· en· W3208400868 on OpenAlexaff
Xin Zhang, Xili Duan, Yuri S. Muzychka

Bibliographic record

VenueInternational journal of fluid mechanics research · 2021
Typereview
Languageen
FieldChemical Engineering
TopicRheology and Fluid Dynamics Studies
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsDragReduction (mathematics)Mechanism (biology)Process (computing)Computer scienceResearch developmentNanotechnologyBiochemical engineeringAerospace engineeringMaterials sciencePhysicsEngineeringGeologyMathematics

Abstract

fetched live from OpenAlex

Researchers have investigated the phenomenon of flow drag reduction by polymers for more than 70 years. A lot of progress has been made on understanding the physical process and its wide range of applications in engineering. There are also unsolved problems related to the mechanism of drag reduction and its degradation, and still new technologies and applications for further research and development. This review first summarizes the history of drag reduction by polymers, focusing on technology development and engineering applications. Then the research progress on drag reduction by polymer additives and its degradation problem is critically reviewed, with useful summary and analysis of the various numerical and experimental methods and correlations developed. We discuss the difficulties in understanding the mechanism behind the drag reduction and degradation phenomena and offer some new explanations. Lastly, a few promising research topics for future work are discussed.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.004
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.002

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.

Opus teacher head0.088
GPT teacher head0.414
Teacher spread0.326 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations11
Published2021
Admission routes1
Has abstractyes

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