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Record W3039949322 · doi:10.1063/5.0014255

On the breakup of a permeating oil droplet in crossflow filtration: Effects of viscosity contrast

2020· article· en· W3039949322 on OpenAlexaff
Amgad Salama

Bibliographic record

VenuePhysics of Fluids · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicMembrane Separation Technologies
Canadian institutionsUniversity of Regina
Fundersnot available
KeywordsBreakupViscosityMechanicsFiltration (mathematics)PhysicsContrast (vision)ThermodynamicsOpticsMathematics

Abstract

fetched live from OpenAlex

The critical velocity of dislodgment of a permeating oil droplet in crossflow filtration is an important parameter in the analysis of the filtration of produced water systems using membrane technology. In this work, the effects of the viscosity contrast between the droplet and the surrounding fluid on the critical velocity of dislodgment are investigated. In the limit when the viscosity of the droplet approaches infinity, the gripping of the crossflow field on the droplet is maximum. When the viscosity contrast is finite, the smaller the viscosity contrast is, the smaller the gripping becomes. In order to highlight this effect, a comprehensive computational fluid dynamics study is conducted. A permeating droplet in the crossflow field is considered with the viscosity contrast ranging within two orders of magnitude. For each scenario, the critical velocity of dislodgment is determined by increasing the velocity incrementally until breakup occurs for every viscosity contrast. It is found that an increase in the viscosity contrast results in a decrease in the critical velocity of dislodgment. This represents a direct manifestation of the effect of the gripping of the droplet by the crossflow field, which increases as the viscosity contrast increases. Modification of the critical velocity of dislodgment, therefore, needs to be considered to account for this effect of viscosity contrast. The formula that was developed to estimate the critical velocity of dislodgment has been modified, and comparison with simulation gives a very good match.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.238

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 head0.012
GPT teacher head0.228
Teacher spread0.215 · 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

Citations22
Published2020
Admission routes1
Has abstractyes

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