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Record W4293115997 · doi:10.11159/icmie22.155

Flow Visualization in the Impeller and Diffuser of a Centrifugal Pump using Time-Resolved Particle Image Velocimetry

2022· article· en· W4293115997 on OpenAlexvenueno aff
Rodolfo Marcilli Perissinotto, William Denner Pires Fonseca, Rafael Franklin Lázaro de Cerqueira, William Monte Verde, Jorge Luiz Biazussi, Erick de Moraes Franklin, Antonio Carlos Bannwart, Marcelo S. Castrot

Bibliographic record

VenueProceedings of the World Congress on Mechanical, Chemical, and Material Engineering · 2022
Typearticle
Languageen
FieldEngineering
TopicCavitation Phenomena in Pumps
Canadian institutionsnot available
Fundersnot available
KeywordsImpellerCentrifugal pumpParticle image velocimetryDiffuser (optics)Flow visualizationParticle tracking velocimetryVelocimetryFlow (mathematics)MechanicsVisualizationMaterials scienceOpticsMechanical engineeringPhysicsTurbulenceEngineering

Abstract

fetched live from OpenAlex

The present paper describes an experimental study on the flow dynamics within a centrifugal pump impeller.A transparent pump prototype made of acrylic parts was firstly developed for flow visualization purposes.Then, single-phase flow experiments were conducted in different impeller rotational speeds and water flow rates.A time-resolved particle image velocimetry (TR-PIV) system was used as the flow visualization method.As a result, velocity fields were obtained in the whole impeller.They reveal that the flow behaviour is dependent on the pump operational condition.When the pump works at the best efficiency point (BEP), the flow is uniform and the streamlines follow the blade curvature.However, when the machine works at off-design conditions, the flow becomes complex, with the presence of turbulent structures which cause a reduction in the pump performance.This type of result may be useful to validate numerical simulations and support the proposition of new mathematical models, new impeller geometries, among other applications.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.230
Threshold uncertainty score0.505

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 head0.006
GPT teacher head0.202
Teacher spread0.196 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of the World Congress on Mechanical, Chemical, and Material EngineeringSame topicCavitation Phenomena in PumpsFrench-language works237,207