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Record W2041229637 · doi:10.3390/en7117602

Exergy Destruction in Pipeline Flow of Surfactant-Stabilized Oil-in-Water Emulsions

2014· article· en· W2041229637 on OpenAlexafffund
Rajinder Pal

Bibliographic record

VenueEnergies · 2014
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Mixing
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Waterloo
KeywordsLaminar flowExergyEmulsionTurbulencePulmonary surfactantVolumetric flow rateThermodynamicsMaterials scienceReynolds numberChemical engineeringChemistryMechanicsPetroleum engineeringGeologyPhysicsEngineering

Abstract

fetched live from OpenAlex

Exergy destruction in adiabatic pipeline flow of surfactant-stabilized oil-in-water emulsions is investigated in five different diameter pipes. The dispersed-phase (oil droplets) concentration of the emulsions is varied from 0% to 55.14% vol. The emulsions are Newtonian in that the viscosity is independent of the shear rate. For a given emulsion and pipe diameter, the exergy destruction rate per unit pipe length increases linearly with the increase in the Reynolds number on a log-log scale in both laminar and turbulent regimes. However the slope in the turbulent regime is higher. The exergy destruction rate increases with the increase in the dispersed-phase concentration of emulsion and decreases with the increase in the pipe diameter. New models are developed for the prediction of exergy destruction rate in pipeline flow of surfactant-stabilized oil-in-water emulsions. The models are based on the single-phase flow equations. The experimental data on exergy destruction in pipeline flow of emulsions shows excellent agreement with the predictions of the proposed models.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.132
Threshold uncertainty score0.300

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 head0.004
GPT teacher head0.176
Teacher spread0.172 · 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 designSimulation or modeling
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

Citations9
Published2014
Admission routes2
Has abstractyes

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