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Record W2495124673 · doi:10.1002/app.44191

Thermo‐responsive polymers for drag reduction in turbulent Taylor–Couette flow

2016· article· en· W2495124673 on OpenAlexafffund
Pallavi Bhambri, Ravin Narain, Brian A. Fleck

Bibliographic record

VenueJournal of Applied Polymer Science · 2016
Typearticle
Languageen
FieldChemical Engineering
TopicRheology and Fluid Dynamics Studies
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsLower critical solution temperatureDragPolymerMaterials scienceTaylor–Couette flowAdsorptionChemical engineeringPolymerizationPolymer chemistryReynolds numberTurbulenceChemistryThermodynamicsComposite materialPhysicsOrganic chemistryCopolymerEngineering

Abstract

fetched live from OpenAlex

ABSTRACT The addition of a small amount of high molecular weight polymer to a solvent can substantially decrease friction losses by approximately 80%. This phenomenon known as drag reduction (DR) is used extensively in oil recovery during hydraulic fracturing and in many other applications to reduce the pumping costs. However, because of long chain length, these polymers get adsorbed on the surface of reservoir, diminishing the effectiveness of fracking. In the current study, a thermo‐responsive polymer, i.e., poly(N‐isopropylacrylamide) (PNIPAM) is investigated as a drag reducing agent (DRA), which collapses reversibly above 33 °C known as lower critical solution temperature (LCST), thereby preventing it from getting adsorbed beyond this temperature. Free radical polymerization was used to synthesize the PNIPAM and a Taylor–Couette (TC) setup with a rotating inner cylinder was utilized for measuring the DR. The effect of concentration, Reynolds number ( Re ), and temperature on DR were studied and a maximum of 50% DR was observed at 400 PPM concentration. PNIPAM demonstrated significant decrease in DR beyond LCST, validating its thermo‐responsive nature that could be beneficial for DR in oil recovery or in providing a control modality to DR technologies.DR versus temperature for PNIPAM solution (500 PPM) at Re = 100,000 demonstrating responsive behavior with temperature © 2016 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2016 , 133 , 44191.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.084
Threshold uncertainty score0.346

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.007
GPT teacher head0.231
Teacher spread0.223 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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

Citations14
Published2016
Admission routes2
Has abstractyes

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