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Record W1966459151 · doi:10.1115/power2013-98234

Effects of Cavitation Damage on the Hydraulic Characteristics of a Gate Valve

2013· article· en· W1966459151 on OpenAlexaff
Vatsal Trivedi, Amjad Farah, Glenn Harvel

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicWater Systems and Optimization
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsCavitationGate valvePressure dropMaterials scienceMechanicsHydraulic machineryComputational fluid dynamicsTurbulenceDischarge coefficientFlow (mathematics)Valve seatHydraulic fluidFluid dynamicsMechanical engineeringEngineeringPhysics

Abstract

fetched live from OpenAlex

This paper explores the effects of cavitation damage on the hydraulic performance of a gate valve. Cavitation is the phenomenon in which a fluid evaporates to form vapor bubbles which then subsequently implode. A Computational Fluid Dynamics (CFD) model of water flowing through a partially opened 1 inch (2.54 cm diameter) gate valve was developed using Unigraphics NX 7.5. NX 7.5 utilizes a k-ε turbulent flow 2-equation model. The low pressure and high velocity regions were identified in the CFD model to predict the location of the cavitation site in the valve. In order to simulate the effects of ageing due to cavitation, a gate valve was mechanically damaged in different stages. Damaged and undamaged valves were then inserted in a hydraulic test loop to observe the flow rate and pressure drop across the valves. The valves’ hydraulic loss coefficient was then calculated and compared. The cavitation index of the damaged and the undamaged gate valves was also calculated to predict the likelihood of cavitation. The results show that the aged valve had lower loss coefficients at the corresponding opening positions compared to the ones for the non-aged valve. Also, it was observed that the aged gate valve had less likelihood of cavitation compared to the non-aged gate valve.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.260
Threshold uncertainty score0.121

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 head0.004
GPT teacher head0.160
Teacher spread0.156 · 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

Citations3
Published2013
Admission routes1
Has abstractyes

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