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Record W7116205684 · doi:10.82417/3ypq-xh23

Numerical analysis of buoyancy-driven immiscible exchange flows in axially rotating pipes

2025· other· en· W7116205684 on OpenAlexfundno aff

Bibliographic record

VenueEspace ÉTS (ETS) · 2025
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaAlliance de recherche numérique du CanadaUniversité Laval
KeywordsTurbulenceAxial symmetryKinetic energyRotation (mathematics)Turbulence kinetic energyAngular velocityFront (military)Numerical analysisFunction (biology)

Abstract

fetched live from OpenAlex

This numerical investigation examines buoyancy-driven immiscible exchange flows in near-horizontal rotating pipes using OpenFOAM simulations, with direct applications to primary cementing of oil and gas wells. The study analyzes a density-unstable configuration where a higher-density fluid (water solutions) overlies a lower-density fluid (silicone oil), focusing on three critical parameters: the pipe's rotational angular velocities, density differences between fluids, and inclination angles. Analysis reveals that the displacing front velocity is an increasing function of density differences and a decreasing function of inclination angles and rotations. Higher density differences, faster rotations, and steeper inclinations could all enhance the Kelvin-Helmholtz instability. Higher pipe rotation dampens axial velocity fluctuations while enhancing radial direction velocity fluctuations, whereas both increased density differences and decreased inclination angles intensify axial velocity fluctuations, with cross-sectional components remaining relatively unchanged across all conditions. Additionally, turbulent kinetic energy is enhanced by faster rotations, higher density differences, and steeper inclination angles. These findings characterize the complex interplay between rotation, density difference, and inclinations in immiscible exchange flows, and provide some insights into practical cementing operations through velocity fluctuation and turbulent kinetic energy analysis.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.012

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 communication0.0010.000
Open science0.0000.000
Research integrity0.0010.000
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 head0.012
GPT teacher head0.273
Teacher spread0.261 · 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
Published2025
Admission routes1
Has abstractyes

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