Flow Regime, Slug Frequency and Wavelet Analysis of Air/Newtonian and Air/non-Newtonian Two-Phase Flow
Bibliographic record
Abstract
This study focused on gas/Newtonian and gas/non-Newtonian two-phase horizontal fluid flow behavior by analyzing their flow regime identification and flow structural analysis on a horizontal flow loop apparatus. This involved the recognition of two-phase flow regimes for this flow loop and validation with existing flow maps in the literature. In addition, the study included flow pattern identification via wavelet analysis for gas/Newtonian and gas/non-Newtonian two-phase fluid flow in a horizontal flow loop apparatus. Furthermore, the study was extended to the detailed examination of slug frequency in the presence of air/Newtonian and air/non-Newtonian fluid flow, and the predicted slug frequency model was applied to the studied systems. The obtained results suggest that the flow regime maps and slug frequency analysis have a significant impact. The obtained pressure sensor results indicate that the experimental setup could not provide high-frequency and high-resolution data; nevertheless, wavelet decomposition and wavelet norm entropy were calculated. It offered recognizable flow characteristics for bubble, bubble-elongated bubble, and slug flow patterns. Therefore, this study can provide deep insight into intricate multiphase flow patterns, and the wavelet could potentially be applied for flow analysis in oil and gas pipelines.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".