Two Phase Flow: Accounting for the Presence of Liquids in Gas Pipeline Design
Bibliographic record
Abstract
Multiphase flow of gas and low loads of liquids occurs frequently in natural gas gathering and transmission pipelines for both onshore and offshore operations. Literature and experimental investigations indicate that dispersed droplet and stratified flow patterns are obtained when gas and small quantities of liquids flow concurrently in a pipe. Very few correlations exist for the prediction of holdup and pressure drop for these systems and fewer still give satisfactory results. Experimental studies for air-oil and airwater systems flowing through small diameter plastic and steel horizontal pipes ranging in size from 1-inch to 3-inches were performed. The experiments were carried out at the multiphase flow laboratories of Imperial College in London and the University of Calgary in Canada. Data from actual operating gas pipeline systems transporting small amounts of hydrocarbon liquids were also evaluated. Based on the experimental results and the operating data, two approaches for modeling these systems are proposed: 1) A homogeneous approach for very low liquid loads (holdups up to 0.005), typical in gas transmission systems. A friction factor correlation based on the holdup has been developed for this flow regime. 2) A mechanistic stratified two-phase approach for higher liquid loads (holdups greater than 0.005) usually found in gas gathering systems with consideration given to: a) The reduction in the available flow area and extent of wetting of the pipe perimeter by the liquid film. The gas/liquid interface was observed to be either flat or curved. b) The interfacial friction factor between the liquid film and the gas. A new correlation based on the liquid and gas Reynolds numbers as well as the film thickens and hold up has been developed. This correlation has been successfully tested against both experimental and actual pipeline operating data.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| 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 teacher head, 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".