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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
\nhave severe repercussions in a variety of industrial systems.
\nAcoustic resonance can cause high levels of vibrations leading
\nto damage or premature failure of critical components. Although
\nacoustic resonance affects a broad spectrum of industrial
\nequipment, piping systems will be of focus in this work. Both
\npassive and active damping techniques were previously
\ninvestigated. However, there is a need to investigate the
\npracticality of such devices when implemented in industrial
\nsystems. Herschel-Quincke (HQ) tubes have been selected for
\nexperimental study throughout this work. The experimental
\nsetup consists of an open-air loop pipeline system which is
\ncapable of exciting a standing wave with a fundamental
\nfrequency of 30 Hz and a target dominant fifth mode of 150 Hz.
\nTransmission loss measurements were performed by means of
\nthe two source-location method. Insertion loss measurements
\nwere performed with a straight pipe used as the baseline. The
\ncurrent work has shown that Herschel-Quincke devices have
\npotential for practical implementation into resonant piping
\nsystems 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 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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.647
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.007
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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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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