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Record W4386737367 · doi:10.1021/acsomega.3c03660

Optimization of a Multiphase Mixed Flow Field in Backfill Slurry Preparation Based on Multiphase Flow Interaction

2023· article· en· W4386737367 on OpenAlexaff
Rugao Gao, Weijun Wang, Keping Zhou, Yanlin Zhao, Chun Yang, Qifan Ren

Bibliographic record

VenueACS Omega · 2023
Typearticle
Languageen
FieldEngineering
TopicTailings Management and Properties
Canadian institutionsMcGill University
FundersNatural Science Foundation of Hunan ProvinceNational Natural Science Foundation of China
KeywordsMechanicsTurbulenceSlurryTurbulence kinetic energyDissipationLift (data mining)Multiphase flowMaterials scienceFlow (mathematics)Mixing (physics)Lift coefficientComputational fluid dynamicsRheologySimulationEngineeringThermodynamicsReynolds numberComputer sciencePhysicsComposite material

Abstract

fetched live from OpenAlex

The paper analyzes the dynamic behavior during the preparation of cemented backfill slurry by combining the structural performance analysis of the double-shaft mixer and the Euler multiphase flow field computational fluid dynamics model. Considering the interaction between phases and gas phase disturbances, the transient kinetic parameters and the interaction between gas and liquid phases were introduced. According to the modified lift model, the user-defined function of the net lateral lift coefficient and the turbulence energy equation was adjusted. Taking the parameters of flow field velocity, gas phase mixing, uniformity, and turbulent energy dissipation as the evaluation indexes of the mixing effect, the double-shaft mixer at a rotation velocity of 45 rpm and with a blade installation angle of 25° is the optimal design in this study. Experimental tests were carried out and confirmed that the refined two-fluid model of interphase interaction can provide a basis for the performance evaluation of material mixing equipment.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.070
Threshold uncertainty score0.497

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 head0.017
GPT teacher head0.249
Teacher spread0.231 · 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

Citations12
Published2023
Admission routes1
Has abstractyes

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