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Record W4379161155 · doi:10.1063/5.0153390

On the thixotropy of mature fine tailings

2023· article· en· W4379161155 on OpenAlexafffund
Amir Malmir, Jourdain H. Piette, Babak Derakhshandeh, Danuta Sztukowski, Savvas G. Hatzikiriakos

Bibliographic record

VenuePhysics of Fluids · 2023
Typearticle
Languageen
FieldChemical Engineering
TopicRheology and Fluid Dynamics Studies
Canadian institutionsCanadian Natural ResourcesSuncor Energy (Canada)University of British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaCanada's Oil Sands Innovation AllianceImperial Oil Limited
KeywordsThixotropyRheologyShearing (physics)Shear rateMechanicsShear (geology)Shear stressMaterials scienceThermodynamicsGeotechnical engineeringPhysicsComposite materialGeology

Abstract

fetched live from OpenAlex

The rheological behavior of mature fine tailings (MFTs) is investigated using transient and steady shear flow fields. The structure breakdown of intact MFT samples is examined by a startup flow experiment at various shear rates. The yield stress of MFTs is estimated by the steady shear stress values at low shear rates. Oscillatory shear (strain amplitude sweep) is also used to verify the obtained yield stress value. MFT samples exhibit thixotropy and a positive hysteresis loop at short shearing time intervals in increasing and decreasing stepwise shear rate tests. The observed hysteresis loops and thixotropy disappear by increasing the shearing time intervals, as the system reaches its equilibrium steady-state structure. The time-dependent rheological behavior of MFTs is quantified by a structural kinetics model through the dimensionless structure parameter, λ. [Toorman, “Modelling the thixotropic behaviour of dense cohesive sediment suspensions,” Rheol. Acta 36, 56–65 (1997).] The kinetic parameters are estimated based on steady-state stresses, elucidating the relative effects of shear rate and Brownian motion on buildup and breakdown of the structure. The flow behavior of MFTs predicted by the structural kinetics model is in agreement with the experimental data.

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 score0.498
Threshold uncertainty score0.245

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 head0.231
Teacher spread0.219 · 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

Citations8
Published2023
Admission routes2
Has abstractyes

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