MétaCan
Menu
Back to cohort
Record W2082921933 · doi:10.1115/pvp2009-77423

Influence of Viscosity, Density and Surface Tension on Two-Phase Damping

2009· article· en· W2082921933 on OpenAlexaff
C. Be ́guin, J. Wehbe, A. Ross, M. J. Pettigrew, Njuki Mureithi

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Vibration Analysis
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsPipingMechanicsMaterials scienceDissipationPhase (matter)VibrationTwo-phase flowViscosityFlow (mathematics)ThermodynamicsComposite materialPhysics

Abstract

fetched live from OpenAlex

Internal two-phase flow is common in piping systems. Such flow may induce vibration that can lead to premature fatigue or wear of pipes. In the nuclear industry in particular, failure of piping components is critical and must be avoided. Two-phase damping is considered part of the solution, since it constitutes a dominant component of the total damping in piping with internal flow. However, the energy dissipation mechanisms in two-phase flow are yet to be fully understood. The purpose of this paper is to explore the relationships between two-phase damping and fluid properties. Simple experiments were carried out in a clear vertical clamped-clamped tube to verify the effects of fluid properties on two-phase damping. Various fluids, such as air, alcohol, pure water, sugared water, glycerol, and perfluorocarbon, were combined to obtain different controlled mixtures and to determine the effect of surface tension, density and viscosity on two-phase damping. Two-phase damping ratios were obtained from free transverse vibration measurements on the tube. Two sets of experiments with stagnant and moving continuous phase were conducted. Based on dimensional analysis, we obtained a semi-empirical model for two-phase damping in bubbly and slug flow. The Void fraction and Bond number are shown to be major parameters of two-phase damping, which is described as a kinetic energy transfer from the tube to the continuous phase through added mass of the dispersed phase.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.249
Threshold uncertainty score0.216

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 head0.007
GPT teacher head0.249
Teacher spread0.242 · 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

Citations3
Published2009
Admission routes1
Has abstractyes

Explore more

Same topicFluid Dynamics and Vibration AnalysisFrench-language works237,207