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Record W2896695416

Control of Resonant Excitation in Piping Systems

2018· article· en· W2896695416 on OpenAlexafffund
Thomas Lato, Atef Mohany

Bibliographic record

Venuee-scholar@UOIT (University of Ontario Institute of Technology) · 2018
Typearticle
Languageen
FieldEngineering
TopicVibration and Dynamic Analysis
Canadian institutionsOntario Tech University
FundersNatural Sciences and Engineering Research Council of CanadaUniversity Network of Excellence in Nuclear EngineeringCANDU Owners Group
KeywordsPipingAcousticsResonance (particle physics)VibrationNoise (video)Transmission (telecommunications)Work (physics)Pipeline (software)EngineeringMechanical engineeringComputer sciencePhysicsElectrical engineering
DOInot available

Abstract

fetched live from OpenAlex

Acoustic resonance is a phenomenon which is known to have severe repercussions in a variety of industrial systems. Acoustic resonance can cause high levels of vibrations leading to damage or premature failure of critical components. Although acoustic resonance affects a broad spectrum of industrial equipment, piping systems will be of focus in this work. Both passive and active damping techniques were previously investigated. However, there is a need to investigate the practicality of such devices when implemented in industrial systems. Herschel-Quincke (HQ) tubes have been selected for experimental study throughout this work. The experimental setup consists of an open-air loop pipeline system which is capable of exciting a standing wave with a fundamental frequency of 30 Hz and a target dominant fifth mode of 150 Hz. Transmission loss measurements were performed by means of the two source-location method. Insertion loss measurements were performed with a straight pipe used as the baseline. The current work has shown that Herschel-Quincke devices have potential for practical implementation into resonant piping systems in industry.

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)0.000
metaresearch head score (Gemma)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

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)0.0040.000

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 head0.007
GPT teacher head0.176
Teacher spread0.169 · 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

Citations2
Published2018
Admission routes2
Has abstractyes

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