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Record W2042570287 · doi:10.1115/gt2002-30278

On Reducing Piping Vibration Levels: Attacking the Source

2002· article· en· W2042570287 on OpenAlexaff
Zheji Liu, D. Lee Hill, Roman W. Motriuk

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicHydraulic and Pneumatic Systems
Canadian institutionsTransCanada (Canada)
FundersSichuan University of Science and EngineeringAmerican Heart Association
KeywordsNoise (video)AcousticsVibrationHarmonicsCentrifugal compressorPipingGas compressorNoise reductionBody orificeEngineeringDiffuser (optics)Helmholtz resonatorComputer scienceMechanical engineeringElectrical engineeringPhysicsResonator

Abstract

fetched live from OpenAlex

Centrifugal compressors used in the pipeline market generate very strong noise, which is typically dominated by the blade passing frequency and its higher harmonics. The high level noise is not only very disturbing to the people living nearby the installation site but also causes expensive structural failures in the downstream piping. A novel design of Helmholtz array has been developed to address this type of noise problem. Computational studies show that the installation of the Helmholtz array acoustic liner on the compressor diffuser walls is very effective in reducing noise level of the compressor, especially the dominant blade passing frequency noise. The acoustic liner design has been built and tested at an installation site by the customer. The data clearly shows that the use of acoustic liners is indeed very effective in the reduction of both the noise and the vibration levels of the machine.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0040.001

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 head0.040
GPT teacher head0.217
Teacher spread0.177 · 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 designBench or experimental
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

Citations1
Published2002
Admission routes1
Has abstractyes

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