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Record W2039555633 · doi:10.2202/1542-6580.1726

Multiphase Flow Measurement Techniques for Slurry Transport

2011· article· en· W2039555633 on OpenAlexaff
Katherine Albion, Lauren Briens, Cedric Briens, Franco Berruti

Bibliographic record

VenueInternational Journal of Chemical Reactor Engineering · 2011
Typearticle
Languageen
FieldEngineering
TopicFlow Measurement and Analysis
Canadian institutionsWestern University
Fundersnot available
KeywordsSlurryFlow measurementMaterials scienceMultiphase flowAcousticsFlow (mathematics)CapacitanceMass flowPressure measurementNuclear engineeringMechanicsMechanical engineeringEngineeringChemistryPhysicsComposite material

Abstract

fetched live from OpenAlex

Multiphase flow measurement techniques are required to monitor slurry transport in industrial processes. Monitoring methods are used to ensure that the slurry is transported under specific conditions, and as an indicator of potential problems. Intrusive and non-intrusive sensors are used to measure solids concentration, mass flowrates, velocities and flow patterns. Sensing techniques are based on pressure, electrical, sound, imaging and nuclear properties. In this literature review, measurement techniques examined for horizontal pipelines include pressure measurements, the Coriolis mass flowmeter, acoustic sensors, capacitance, conductivity and microwave probes, electrical resistance tomography, laser Doppler imaging and the Pulsed Neutron Activation Technique. The principles of operation are described along with experimental results and a critique of the sensors and technology.

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)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.005
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0010.001
Scholarly communication0.0010.003
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.002

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 head0.032
GPT teacher head0.222
Teacher spread0.190 · 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 designBench or experimental
Domainnot available
GenreMethods

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

Citations18
Published2011
Admission routes1
Has abstractyes

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