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Record W2528898447

Suppression of Acoustic Resonance in Piping System Using Passive Control Devices

2014· article· en· W2528898447 on OpenAlexaff
Omar Sadek, Mahmoud Shaaban, Atef Mohany

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicHydraulic and Pneumatic Systems
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsPipingCabin pressurizationVibrationEngineeringAcousticsNoise (video)Pipeline transportStructural engineeringMechanical engineeringComputer sciencePhysics
DOInot available

Abstract

fetched live from OpenAlex

Piping systems used in industrial applications such as power stations and natural gas pipelines are liable to generation of pressure pulsations, often arising from the use of reciprocating pumps and/or dynamic instability of valves, which can act as sources of unwanted noise and vibration. In the event that the frequencies of these pulsating sources correspond to the resonant frequencies of the piping system, a very dangerous acoustic resonance condition can result, which can produce extremely large pressure fluctuations, often a several times the dynamic head of the flow in the main pipe, as well as substantial vibration of the piping system and surrounding components. These conditions, referred to as Acoustic Induced Vibrations (AIV), can lead to issues with leakage due to gasket and seal ruptures, valve failure, and in serious cases, fatigue and fracture of the piping system, related components and instrumentation. In this paper, the effectiveness of different passive noise control devices for attenuation of pressure pulsation in piping system is investigated experimentally. A small-scale pipeline system is constructed and used to examine the performance of these devices and a summary of the results is presented in the paper.

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: Empirical
Teacher disagreement score0.345
Threshold uncertainty score0.304

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 head0.009
GPT teacher head0.210
Teacher spread0.201 · 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

Citations5
Published2014
Admission routes1
Has abstractyes

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