MétaCan
Menu
Back to cohort
Record W2000208604 · doi:10.1115/ipc2010-31671

Experimental Studies of Small Air Bubble Motion in Turbulent Pipe Flow

2010· article· en· W2000208604 on OpenAlexaff
O. V. Zhukovskaya, Ronald J. Hugo

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Mixing
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsBubbleTurbulenceMechanicsReynolds numberParticle image velocimetryPipe flowParticle tracking velocimetryPhysicsFlow (mathematics)Two-phase flowOptics

Abstract

fetched live from OpenAlex

The main objective of this research is to collect statistical information concerning momentum phase coupling between the continuous phase and a single air bubble in turbulent flow in a horizontal pipe, and to develop data that can be used for the verification of numerical modeling efforts. In comparison with vertical pipe bubble flow, horizontal bubble flow has received less attention, especially from the experimental side. Thus, an experimental investigation of bubble behaviour in a horizontal square pipe was performed. Tracking of a single bubble released in water flow in a 56.8 mm × 56.8 mm square pipe was performed to provide a basis for characterizing the behaviour of the single bubble in turbulent pipe flow. A Shack Hartman Wavefront Sensor and a High Speed Video Camera were used to collect images at various points downstream from the bubble injection point, providing information on bubble size, velocity, and spatial location as a function of Reynolds number. Velocity profile information of the continuous phase was collected using Particle Image Velocimetry (PIV) in order to perform a complete characterization of the flow. The data collected using PIV coupled with the analysis of the three-dimensional trajectory of a single bubble provides information about parameters such as a gas slippage velocity with the fluid phase and bubble distribution as a function of both Reynolds number and mean velocity profile.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.556
Threshold uncertainty score0.231

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 head0.013
GPT teacher head0.229
Teacher spread0.216 · 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 designBench or experimental
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
Published2010
Admission routes1
Has abstractyes

Explore more

Same topicFluid Dynamics and MixingFrench-language works237,207