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Record W2322022494 · doi:10.1021/ef4022637

Thixotropic Rheological Behavior of Maya Crude Oil

2014· article· en· W2322022494 on OpenAlex
Sepideh Mortazavi-Manesh, John M. Shaw

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueEnergy & Fuels · 2014
Typearticle
Languageen
FieldChemical Engineering
TopicRheology and Fluid Dynamics Studies
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsThixotropyRheologyRheometerShear thinningMaterials scienceShear rateShear (geology)Shear stressNewtonian fluidPetroleum engineeringMechanicsComposite materialGeologyPhysics

Abstract

fetched live from OpenAlex

Heavy oil and bitumen exhibit non-Newtonian rheological behaviors at lower temperatures. Thixotropy is one such behavior. Thixotropy affects the efficiency and length scale of mixing during blending operations and flow behaviors in pipes and pipelines following flow disruption, where it affects the pressure required to reinitiate flow. In the present work, thixotropic behaviors of Maya crude oil are explored systematically using a stress-controlled rheometer. Maya crude oil is shown to be a shear-thinning fluid below 313 K. The thixotropic behaviors are identified and explored using transient stress techniques (hysteresis loops, stepwise change in the shear rate, and startup experiments). The magnitude of the thixotropy effect is larger at lower temperatures. Relationships are identified between rest times and other thixotropic parameters, such as hysteresis loop area and stress decay, in startup experiments. Stress growth, which occurs as a result of a step-down in the shear rate, is shown to correlate with the temperature. The results also provide a benchmark data set for validation of rheological models for heavy oil that are immerging in the literature.

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.

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 score0.542
Threshold uncertainty score0.488

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 head0.216
Teacher spread0.207 · 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