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Record W7090350375 · doi:10.11159/jffhmt.2025.033

Computational Fluid Dynamics of a Control Valve with Three-stage Perforated Cages under Varying Perforation Size Distributions

2025· article· en· W7090350375 on OpenAlexvenueno aff

Bibliographic record

VenueJournal of Fluid Flow Heat and Mass Transfer · 2025
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics Simulations and Interactions
Canadian institutionsnot available
Fundersnot available
KeywordsComputational fluid dynamicsPerforationDynamics (music)Control valvesFluid dynamics

Abstract

fetched live from OpenAlex

The purpose of this study was to confirm, through experiments and computational fluid dynamics (CFD) analysis, the flow characteristics of a control valve with a three-stage perforated cage and to evaluate the flow state from the CFD visualization results.The control valve evaluated in this study was a size 2-inch perforated cage valve.We used a 3D metal printer to create two types of cages with different resistances in the first or second stage.The flow coefficient Cv was calculated from three differential pressure conditions, and the liquid pressure recovery factor FL was calculated from the maximum differential pressure.We calculated and compared the flow characteristics, Cv and FL, obtained from experiments and CFD analysis to confirm the validity of the CFD analysis model used in this study.We visualized the pressure distribution, velocity distribution, and void fraction obtained from the CFD analysis.The visualization results showed that the perforations in the first and second stages had non-choked turbulent flows with no cavitation, whereas perforations in the third stage had cavitation at the inlet of the perforations.We found that cavitation in the third stage could be suppressed by increasing the resistance of the first stage rather than increasing the resistance of the second stage.Specifically, increasing the resistance of the first stage reduced cavitation by 40% compared to increasing the resistance of the second stage.

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: none
Teacher disagreement score0.651
Threshold uncertainty score0.556

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 head0.216
Teacher spread0.211 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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