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Record W1537123184 · doi:10.1115/fedsm2013-16215

Comparison of 2D and 3D Predictions of Erosion Wear in Centrifugal Slurry Pump Casings

2013· article· en· W1537123184 on OpenAlexfundno aff
Krishnan V. Pagalthivarthi, John M. Furlan, Robert Visintainer

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnvironmental Science
TopicErosion and Abrasive Machining
Canadian institutionsnot available
FundersGlobal Institute for Water Security, University of Saskatchewan
KeywordsSlurryCentrifugal pumpErosionMaterials sciencePetroleum engineeringMechanical engineeringEngineeringImpellerGeologyComposite material

Abstract

fetched live from OpenAlex

This paper deals with the prediction of erosion wear rate in slurry pump casings, with an emphasis on the comparison of two-dimensional and three-dimensional results. The two-dimensional analysis is carried out in the mid-plane of the pump normal to the pump axis. The dense solid-liquid flow field in the pump casing is modeled using an Eulerian-Eulerian model with a penalized finite element formulation to discretize the continuum equations. Erosion wear due to particle impact and sliding abrasion depends on the local velocity, shear stress, concentration of the particles and the empirically determined (particle-size dependent) wear rate coefficients used to relate the erosion wear to the local flow field properties.The wear rate predictions from the 2D and 3D codes are compared to analyze the three dimensional flow effects on the wear rate. Operating conditions under which the 2D solution departs significantly from the 3D solution are explored with the aim of determining the limitations of the 2D simulations. Three pumps with different casing width-to-depth ratio are analyzed. Flow conditions and the pump casing width-to-depth ratio affect the differences between the 2D and 3D casing wear predictions.Copyright © 2013 by ASME

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 score0.133
Threshold uncertainty score0.998

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)0.0020.000

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 head0.015
GPT teacher head0.267
Teacher spread0.252 · 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

Citations8
Published2013
Admission routes1
Has abstractyes

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