MétaCan
Menu
Back to cohort
Record W4407578988 · doi:10.1115/1.4067927

Lateral and Transverse Stiffness Requirements for Supports of Pipeline Systems Conveying Fluids

2025· article· en· W4407578988 on OpenAlexafffund
Saher Attia, Magdi Mohareb, K. K. Botros, Samer Adeeb

Bibliographic record

VenueJournal of Pressure Vessel Technology · 2025
Typearticle
Languageen
FieldEngineering
TopicVibration and Dynamic Analysis
Canadian institutionsUniversity of AlbertaNova Chemicals (Canada)University of Ottawa
FundersMitacs
KeywordsTransverse planePipeline (software)StiffnessGeologyStructural engineeringEngineeringMechanicsMaterials scienceGeotechnical engineeringMechanical engineeringPhysics

Abstract

fetched live from OpenAlex

Abstract Elevated pipelines systems are commonly supported by flexible steel frames at regular intervals. During operation, these systems can be subjected to sources of harmonic excitations. In order to control the vibration response for such systems, designers may target a threshold minimum fundamental natural frequency for the system. Within this context, the study first shows that, within the ranges of straight pipelines geometries commonly used in the oil and gas industry, the fluid mass and stiffness of the intermediate flexible supports significantly impact the fundamental natural frequency of the system, while fluid velocity within the practical range of the oil and gas industry, temperature differential between the installation and operating temperatures, and pipe self-weight are less important or negligible. The study further extends the analysis to nonstraight piping systems with L-Shape and S-Shape configurations. In conclusion, design formulas are proposed to estimate the required lateral and transverse stiffnesses for the supporting systems to attain a specified fundamental natural frequency for straight and curved pipelines. Natural frequency comparisons with finite element solutions show the validity of the proposed design formulas.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.779
Threshold uncertainty score0.334

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 head0.008
GPT teacher head0.245
Teacher spread0.237 · 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

Citations0
Published2025
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Pressure Vessel TechnologySame topicVibration and Dynamic AnalysisFrench-language works237,207