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Damping of Tubes With Internal Two-Phase Flow

2006· article· en· W1998464355 on OpenAlexaff
Alexandre Gravelle, Annie Ross, M. J. Pettigrew, Njuki Mureithi

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Vibration Analysis
Canadian institutionsPolytechnique MontréalCAE (Canada)
Fundersnot available
KeywordsMechanicsPipingDissipationTwo-phase flowMagnetic dampingVoid (composites)Materials scienceFlow (mathematics)PorosityTurbulenceThermodynamicsPhysicsVibrationComposite materialAcoustics

Abstract

fetched live from OpenAlex

Two-phase internal flow is present in many piping system components. Although two-phase damping is known to be a significant constituent of the total damping, the energy dissipation mechanisms that govern two-phase damping are not well understood. In this paper, damping of vertical clamped-clamped tubes subjected to two-phase air-water internal flow is investigated. Experimental data is reported, showing no dependence of two-phase damping on tube natural frequency, and a strong dependence on void fraction, flow velocity and flow regime. Two-phase damping increases with void fraction, reaches a maximum, and decreases beyond that point. The maximum damping ratio is roughly 3% for all flow velocities. It is reached at around 50% void fraction for high velocities, and 25% void fraction for low velocities. Data points plotted on two-phase flow pattern maps indicate that damping is greater in a bubbly flow regime than it is in a slug or churn regime. The maximum two-phase damping is reached at the highest void fraction before the transition to a slug or churn flow regime. It appears that two-phase damping may depend on the interface surface area between phases.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.003
GPT teacher head0.210
Teacher spread0.206 · 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

Citations0
Published2006
Admission routes1
Has abstractyes

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