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Record W2765210979 · doi:10.1115/pvp2017-65933

Towards Understanding Two-Phase Flow Induced Vibration of Piping Structure With Flow Restricting Orifices

2017· article· en· W2765210979 on OpenAlexaff
Olufemi E. Bamidele, Wael H. Ahmed, Marwan Hassan

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicFlow Measurement and Analysis
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsPipingSlug flowMechanicsBody orificeTwo-phase flowFlow coefficientStratified flowPipe flowParticle image velocimetryVibrationFlow (mathematics)Materials scienceVortexFlow measurementOrifice plateVolumetric flow rateTurbulencePhysicsAcousticsEngineeringMechanical engineeringThermodynamics

Abstract

fetched live from OpenAlex

The current work studies air-water flow through a ½-inch flow restricting orifice installed in a 1-inch pipe. Investigation of two phase flow downstream the orifice and its effects on vibration of the piping structure have been carried out. Several flow regimes from bubbly to stratified-wavy flow have been analyzed to evaluate the effects of flow pattern, phase redistribution, bubble frequency, and liquid flow rate on the vibration of the structure. The liquid velocity fields have been obtained using Particle Image Velocimetry (PIV) along with post processing algorithm for phase discrimination. Proximity sensors have been used to capture the pipe response in two orthogonal directions. Also, a capacitance sensor was used to measure the two-phase void fraction. The results show that the magnitude and nature of vibrations of the piping structure is largely affected by the frequency and size of the bubbles upstream, vortex creation by pressure fluctuation downstream, liquid flow rate, and the flow pattern upstream. Slug flow and stratified flow patterns induced significant vibrations in the examined structure. The location of the transition region of slug flow on flow pattern maps, play important role in the dynamic response of the structure to the flow.

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 score0.611
Threshold uncertainty score0.472

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 head0.064
GPT teacher head0.277
Teacher spread0.213 · 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
Published2017
Admission routes1
Has abstractyes

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