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Record W2340339999 · doi:10.3390/en9040293

Influence of Droplet Size on Exergy Destruction in Flow of Concentrated Non-Newtonian Emulsions

2016· article· en· W2340339999 on OpenAlexaff
Rajinder Pal

Bibliographic record

VenueEnergies · 2016
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Mixing
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsEmulsionLaminar flowMechanicsMaterials scienceShear rateNewtonian fluidVolumetric flow rateTurbulenceNon-Newtonian fluidVolume (thermodynamics)RheologyThermodynamicsChemistryComposite materialPhysics

Abstract

fetched live from OpenAlex

The influence of droplet size on exergy destruction rate in flow of highly concentrated oil-in-water emulsions was investigated experimentally in a cone and plate geometry. The oil concentration was fixed at 74.5% by volume. At this dispersed-phase (oil) concentration, two different droplet size emulsions were prepared: fine and coarse emulsions. The fine and coarse emulsions were mixed in different proportions to vary the droplet size distribution. Although the dispersed and matrix phases of the emulsions were Newtonian in nature, the emulsions exhibited a non-Newtonian (shear-thinning) behavior due to the high droplet concentration. The shear stress—shear rate data of the emulsions could be described adequately by a power law model. At low shear rates, the exergy destruction rate per unit volume of emulsion exhibited a minimum at a fine emulsion proportion of 35%. The results from the cone and plate geometry were used to simulate exergy loss in pipeline flow of emulsions. The pumping of emulsions becomes more efficient thermodynamically upon mixing of fine and coarse emulsions provided that the flow regime is maintained to be laminar and that the Reynolds number is kept at a low to moderate value. In the turbulent regime, the exergy loss generally increases upon mixing the fine and coarse emulsions.

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 score0.315
Threshold uncertainty score0.229

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 head0.002
GPT teacher head0.177
Teacher spread0.175 · 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

Citations4
Published2016
Admission routes1
Has abstractyes

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