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Record W4399390020 · doi:10.1016/j.jsv.2024.118574

Dynamics of a cantilevered pipe conveying fluid and partially subjected to a confined counter-current external axial flow of a different fluid

2024· article· en· W4399390020 on OpenAlexaff
Mahdi Chehreghani, Arun K. Misra, Michael P. Paı̈doussis

Bibliographic record

VenueJournal of Sound and Vibration · 2024
Typearticle
Languageen
FieldEngineering
TopicVibration and Dynamic Analysis
Canadian institutionsMcGill University
Fundersnot available
KeywordsCurrent (fluid)Fluid dynamicsMechanicsCantileverFlow (mathematics)Materials scienceEngineeringStructural engineeringPhysicsElectrical engineering

Abstract

fetched live from OpenAlex

A linear analytical model has been developed for prediction of fluid-elastic instabilities of brine-strings during product retrieval in salt-mined caverns. These caverns are utilized for storage and subsequent retrieval of hydrocarbons, hydrogen gas or compressed air in Compressed Air Energy Storage (CAES) plants. The retrieval operation involves pumping brine downwards through a long cantilevered pipe (“brine-string”) into the cavern, causing the lighter-than-brine gaseous or liquid “product” stored in the cavern to flow upwards and out of the cavern through a shorter annular passage formed by a concentric casing surrounding the upper portion of the pipe. The presence in the cavern of two different fluids with a variable interface level is taken into account. Employing a Newtonian derivation of the equation of motion solutions were obtained via the Galerkin modal decomposition technique, demonstrating that the brine-string may develop buckling or flutter at high-enough flow rates, depending on the system parameters. Extensive computations investigated the influence of system parameters on the dynamics. It is shown that simplifying the system by considering a single fluid in the cavern, and so for the flows within and around the brine-string, leads to unrealistically high critical flow velocity predictions.

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: Empirical
Teacher disagreement score0.956
Threshold uncertainty score0.397

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 head0.009
GPT teacher head0.230
Teacher spread0.221 · 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

Citations6
Published2024
Admission routes1
Has abstractyes

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