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Record W2063174538 · doi:10.1115/icone18-29690

Transmission Type Ultrasonic Imaging of Horizontal Two-Phase Flow

2010· article· en· W2063174538 on OpenAlexaff
Jen‐Shih Chang

Bibliographic record

Venue18th International Conference on Nuclear Engineering: Volume 4, Parts A and B · 2010
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Mixing
Canadian institutionsMcMaster University
Fundersnot available
KeywordsUltrasonic sensorAcousticsPorosityMaterials scienceBubbleTwo-phase flowFlow measurementSlug flowTransducerStratified flowVoid (composites)Ultrasonic flow meterStratified flowsCavitationFlow (mathematics)MechanicsPhysicsTurbulenceComposite material

Abstract

fetched live from OpenAlex

Ultrasonic technique was widely applied for the measurement of two-phase flow, however, the majority techniques deal primary with pulse echo technique under higher operating sound frequency in a MHz order since higher accuracy can be obtain for the dynamic gas-liquid interface locations. On the other hand the transmission type ultrasonic methods was also developed for bubbly flow diagnostics for the time and cross sectional averaged void fraction measurement or bubble two-phase flow parameter determination by pulse echo techniques with higher operating sound frequency in a MHz order. In this work, the transmission type ultrasonic imaging of horizontal two-phase flow was experimentally investigated. Ultrasonic transducer used is 150 kHz and time averaged void fraction results for stratified flow was compared with capacitance void measurement and ultrasonic pulsed echo techniques. The results show that the transmission intensity for the stratified flow pattern decreases with increasing void fraction until 55% of void then increases with increasing void fraction till 100%. An application to plug, slug and annular flow regime also investigated in detail.

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 score0.678

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)0.0010.000

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 head0.009
GPT teacher head0.231
Teacher spread0.223 · 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
Published2010
Admission routes1
Has abstractyes

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