Buoyant fluid injections at high viscosity contrasts in an inclined closed-end pipe
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".