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Record W2051484498 · doi:10.1115/fedsm2002-31284

PIV Investigations of Particle Velocities in the Tongue Region of a Slurry Pump

2002· article· en· W2051484498 on OpenAlexfundno aff
Pathom Charoenngam, A. Subramanian, Jaikrishnan R. Kadambi, Mark P. Wernet, John M. Sankovic, Graeme Addie, Robert Courtwright

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnvironmental Science
TopicErosion and Abrasive Machining
Canadian institutionsnot available
FundersGlobal Institute for Water Security, University of SaskatchewanCase Western Reserve University
KeywordsImpellerSlurryCasingKinetic energyCentrifugal pumpMaterials scienceParticle (ecology)Particle image velocimetryMechanism (biology)MechanicsMechanical engineeringComposite materialGeologyEngineeringPhysicsTurbulence

Abstract

fetched live from OpenAlex

Transport of solid-liquid slurries in pipeline transport over short and medium distances is very important in many industries, including mining related processes. The particle image velocimetry technique was successfully utilized to investigate the velocities and kinetic energy fluctuations of slurry particles at the tongue region of an optically-clear centrifugal pump. The experiments were conducted using 500 micron glass beads at volumetric concentrations of 2.5% and 5% at pump speeds of 725 and 1000 rpm. The fluctuation kinetic energy increased approximately 200% to 500% as the pump speed was increased from 725 rpm to 1000 rpm. The directional impingement mechanism is more significant at the pressure side of the blade, tongue and the casing. This mechanism becomes more important as the speed increases. This suggests that the impeller, tongue and the casing of the slurry pump can wear out quickly, especially with an increase in speed. In this paper the emphasis is on the tongue region. The random impingement mechanism caused by the fluctuation kinetic energy of the solids can play an important role on the erosion of the tongue area.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.043
Threshold uncertainty score1.000

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)0.0010.000

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 head0.047
GPT teacher head0.234
Teacher spread0.187 · 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.

Study designObservational
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

Citations3
Published2002
Admission routes1
Has abstractyes

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