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Record W1977191126 · doi:10.1021/ef800277g

Modeling the Effect of Shear Stress on the Composition and Growth of the Deposit Layer from “Waxy” Mixtures under Laminar Flow in a Pipeline

2008· article· en· W1977191126 on OpenAlexafffund
Nitin V. Bhat, Anil K. Mehrotra

Bibliographic record

VenueEnergy & Fuels · 2008
Typearticle
Languageen
FieldChemistry
TopicPetroleum Processing and Analysis
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsLaminar flowDeformation (meteorology)Shear stressShear (geology)Materials scienceDeposition (geology)Stress (linguistics)Composite materialMineralogyMechanicsChemistryGeology

Abstract

fetched live from OpenAlex

A mathematical model based on the moving boundary problem formulation for solids deposition was modified to account for the effect of shear stress in laminar flow of paraffinic mixtures. The effect of shear stress on the composition and growth of the deposit layer was incorporated via a recently proposed approach involving one-dimensional deformation of a cubical cage, leading to the release of a fraction of the liquid from the deposit. Numerical solutions were obtained for the growth of the deposit layer with time, both radially and axially. Predictions were obtained for the effect of different cubical-cage deformation angles on the composition and solid/liquid phase ratio in the deposit layer. Whereas an increase in the deformation angle was predicted to cause wax enrichment in the deposit, the deposit-layer thickness was primarily dependent upon the heat-transfer and phase equilibrium considerations. The predictions also indicated that an increase in the deformation angle delayed the deposition process, because of a corresponding increase in the average solid-phase fraction within the deposit, and it caused the deposit to become enriched in heavier n -alkanes and depleted in lighter n -alkanes.

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 score0.016
Threshold uncertainty score0.272

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 head0.009
GPT teacher head0.210
Teacher spread0.201 · 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

Citations30
Published2008
Admission routes2
Has abstractyes

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