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Record W2096734920 · doi:10.1002/cjce.21618

Drag reduction in turbulent pipeline flow of mixed nonionic polymer and cationic surfactant systems

2011· article· en· W2096734920 on OpenAlexaffvenueabout
Ali Asghar Mohsenipour, Rajinder Pal

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2011
Typearticle
Languageen
FieldChemical Engineering
TopicRheology and Fluid Dynamics Studies
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsPulmonary surfactantPolymerSurface tensionCationic polymerizationDragViscosityChemistryChemical engineeringMaterials sciencePolymer chemistryOrganic chemistryComposite materialThermodynamicsBiochemistry

Abstract

fetched live from OpenAlex

Abstract Turbulent drag reduction behaviour of a mixed nonionic polymer/cationic surfactant system was studied in a pipeline flow loop to explore the synergistic effects of polymeric and surfactant drag reducing additives. The nonionic polymer used was polyethylene oxide (PEO) at three different concentrations (500, 1000, and 2000 ppm). The surfactant used was cationic octadecyltrimethylammonium chloride (OTAC) at concentration levels of 1000 and 2500 ppm. Sodium salicylate (NaSal) was used as a counter‐ion for the surfactant at a molar ratio of 2 (MR = Salt/OTAC = 2). Relative viscosity and surface tension were measured for different combinations of PEO and OTAC. While the relative viscosities demonstrated a week interaction between the polymer and the surfactant, the surface tension measurements exhibited negligible interaction. The pipeline results show a considerable synergistic effect, that is, the mixed polymer–surfactant system gives a significantly higher drag reduction (lower friction factors) as compared with pure polymer or pure surfactant. The addition of surfactant to the polymer always enhances drag reduction. However, the synergistic effect in mixed system is stronger at low polymer concentrations and high surfactant concentrations. © 2011 Canadian Society for Chemical Engineering

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.657
Threshold uncertainty score0.377

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.010
GPT teacher head0.176
Teacher spread0.166 · 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

Citations38
Published2011
Admission routes3
Has abstractyes

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