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Record W2807413313 · doi:10.1139/cgj-2017-0228

Studies on thickened tailings deposition in flume tests using the computational fluid dynamics (CFD) method

2018· article· en· W2807413313 on OpenAlexvenueno aff
Jinglong Gao, Andy Fourie

Bibliographic record

VenueCanadian Geotechnical Journal · 2018
Typearticle
Languageen
FieldEngineering
TopicTailings Management and Properties
Canadian institutionsnot available
FundersGovernment of Western AustraliaChina Scholarship CouncilAustralian Government
KeywordsFlumeComputational fluid dynamicsGeotechnical engineeringMechanicsDeposition (geology)GeologyRheologyTailingsViscosityDimensionless quantityVolume of fluid methodFlow (mathematics)SedimentMaterials scienceComposite material

Abstract

fetched live from OpenAlex

Computational fluid dynamics (CFD) simulations of laboratory flume tests on thickened tailings were conducted to highlight the factors that may influence the slopes of final profiles achieved in such flumes. The numerical model was first validated against the analytical solution of a sheet of Bingham fluid on a flat plane at flow stoppage. It was then used to investigate the influence of several factors on the average slopes of the final profiles. The results show that an increase of volume, energy, flume width or base angle reduces the resulting slope angle. Moreover, the yield stress of the fluid generally has more influence on the final profiles than the viscosity. In addition, the viscosity tends to have less influence on the formation of the final profiles if the inertial effects are relatively weak. Finally, two dimensionless parameters are proposed to establish the relationship between the average slope, rheological properties, and geometrical parameters for planar deposition of thickened tailings in both sudden-release (S-R) and discharge flume tests. These results provide a better understanding of the deposition of thickened tailings in the field. The agreement between simulation results and laboratory observations in the literature gives confidence in the veracity of the computational results.

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)0.001
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.193
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.001
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 head0.029
GPT teacher head0.277
Teacher spread0.248 · 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 designSimulation or modeling
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

Citations13
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueCanadian Geotechnical JournalSame topicTailings Management and PropertiesFrench-language works237,207