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Record W4316193757 · doi:10.1063/5.0135925

Buoyant fluid injections at high viscosity contrasts in an inclined closed-end pipe

2023· article· en· W4316193757 on OpenAlexafffund
Soheil Akbari, Seyed Mohammad Taghavi

Bibliographic record

VenuePhysics of Fluids · 2023
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Turbulent Flows
Canadian institutionsUniversité Laval
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsCanada Foundation for InnovationPetroleum Technology Alliance Canada
KeywordsPhysicsMechanicsDimensionless quantityReynolds numberParticle image velocimetryViscosityBuoyancyFlow visualizationNewtonian fluidFluid dynamicsFlow (mathematics)OpticsThermodynamicsTurbulence

Abstract

fetched live from OpenAlex

This paper studies the buoyant miscible injection of a high-viscosity fluid in a pipe filled with a low-viscosity fluid. The injection is carried out via an eccentric inner pipe inside an inclined closed-end outer pipe. A heavy fluid is injected into a light fluid at a constant density difference. Although the density difference is small, the buoyancy force, quantified via the Archimedes number (Ar), remains large. Our research relies on non-intrusive experimental methods, via a mix of high-speed camera imaging, ultrasound Doppler velocimetry, planar laser induced fluorescence, and particle image velocimetry techniques, accompanied by complementary numerical simulations. The effects of the viscosity ratio (M), the Reynolds number (Re) and the inclination angle (β) are analyzed on the injection/placement flow dynamics. Accordingly, a detailed description of the flow is presented, in terms of the concentration and velocity fields, the average front velocity of the heavy fluid (V¯f), the mixing index, and the flow regimes. The findings reveal that V¯f is mainly governed by an inertial-buoyant balance, allowing us to develop a correlation for V¯f vs Ar, M, Re and β. The results also show that a heavy fluid front separation occurs when M is small, β is large (i.e., near-vertical inclinations), and Re is large. This observation permits us to classify the flows into separation and non-separation regimes, in a dimensionless group plane based on a combination of the aforementioned dimensionless numbers.

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 score0.694
Threshold uncertainty score0.901

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.011
GPT teacher head0.230
Teacher spread0.219 · 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

Citations21
Published2023
Admission routes2
Has abstractyes

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