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Record W4386236282 · doi:10.53375/icmame.2023.196

Simplified equations for natural frequencies of pipes on elastic foundation conveying gases

2023· article· en· W4386236282 on OpenAlexafffund
Saher Attia, Magdi Mohareb, M. Martens, Samer Adeeb

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicVibration and Dynamic Analysis
Canadian institutionsUniversity of AlbertaAlberta EnergyUniversity of Ottawa
FundersMitacs
KeywordsPipeline transportTurbomachineryBoundary value problemFinite element methodStructural engineeringVibrationDecoupling (probability)MechanicsDifferential equationNatural gasEngineeringPhysicsMathematical analysisMathematicsMechanical engineeringAcoustics

Abstract

fetched live from OpenAlex

Pipelines are commonly subjected to harmonic forces induced by reciprocating centrifugal pumps and compressors, unbalanced moments and forces in turbomachinery, pressure surge, and momentum changes due to valve operation. To guard resonance and fatigue failure, the natural frequencies of pipelines need to be accurately characterized. The present study introduces two sets of simplified equations to compute the natural frequencies of pipes conveying gases resting on uniform elastic foundation for different boundary conditions. While the first set is analytically derived by solving the differential equation, the second set is obtained based on a decoupling approach. A finite element code is built to verify the results computed using both sets of equations and excellent agreement is obtained.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.006
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.029
GPT teacher head0.263
Teacher spread0.235 · 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

Citations1
Published2023
Admission routes2
Has abstractyes

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