Experimental and Numerical Investigation of Gas-Yield Power-Law Fluids in a Horizontal Pipe
Bibliographic record
Abstract
Abstract Two-phase flow of gas/yield power-law (YPL) fluids in pipes can be found in a wide range of practical and industrial applications. To improve the understanding of the effects of rheological parameters of non-Newtonian liquids in a two-phase model, experimental and Computational Fluid Dynamics (CFD) investigations of gas/yield power-law fluids in a horizontal pipe were carried out. Two Xanthan gum (XG) solutions at concentrations of (0.05% and 0.10% by weight) were used as the working liquids. The experiments were conducted in a flow loop in a 65-m open-cycle system. The horizontal test section had a diameter of 3 inches (76.2 mm). The transient calculations were conducted using a Volume of Fluid (VOF) model in ANSYS Fluent version 17.2. Slug flow characteristics were recorded and observed by a high-speed digital camera in different operating conditions. The slug velocity and slug frequency were investigated experimentally and numerically, and a comparison of results with empirical relationships found in the literature was performed. We observed that the rheological properties of non-Newtonian phase influence the flow behavior in two-phase flow with increasing XG concentrations. The results of the empirical correlation to measure the slug frequency of a gas/non-Newtonian with considered the rheology of the shear-thinning behaver gave acceptable agreement with numerical measurements at low polymer concentration. The effect of liquid superficial velocity on slug translational velocity at low gas superficial velocity was relatively high.
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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 | 0.001 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".