MétaCan
Menu
Back to cohort
Record W2138962907 · doi:10.5897/jmer.9000041

Reliability prediction of control valves through mechanistic models

2010· article· en· W2138962907 on OpenAlexvenueno aff
M. Hari Prasad, G. R. Reddy, A. Srividya, Astha Verma

Bibliographic record

VenueMechanical Engineering Research · 2010
Typearticle
Languageen
FieldDecision Sciences
TopicRisk and Safety Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsReliability (semiconductor)Control valvesProcess (computing)Component (thermodynamics)Reliability engineeringValve actuatorControl systemGate valveEngineeringNuclear power plantControl (management)Chemical plantControl engineeringComputer scienceControl theory (sociology)Power (physics)Ball valve

Abstract

fetched live from OpenAlex

A Nuclear Power Plant (NPP) consists of normally operating and emergency standby systems and components. The failure of any operating component will lead to a change in the state of the plant. Control of the processes in the plant is an essential part of the plant operation. The most common control element in the process control systems is the control valve. The control valve manipulates a flowing fluid, such as steam, water, gas, or chemical compounds, to compensate for the load disturbance and keep the regulated process variable as close as possible to the desired set point. In order to ensure the high reliability of the system, component reliability should be ensured. In this paper working principle of control valve and different failure modes by which control valve can fail has been discussed. Reliability of the control valve has been estimated from the mechanistic models by using structural reliability methods.   Key words: Control valves, reliability, mechanistic models, FORM.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.004
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
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 head0.143
GPT teacher head0.406
Teacher spread0.263 · 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 source (direct Gemma or distilled Codex), 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

Citations6
Published2010
Admission routes1
Has abstractyes

Explore more

Same venueMechanical Engineering ResearchSame topicRisk and Safety AnalysisFrench-language works237,207