TWO-PHASE FRICTION FACTOR IN GAS-LIQUID PIPE FLOW
Bibliographic record
Abstract
An improved friction factor prediction model for two-phase gas-liquid pipe flow is proposed. The model is based on a previous no-slip formulation where a mixture Reynolds number was defined. In this study, the mixture Reynolds number is modified by introducing slip-ratio information through the inclusion of void-fraction and flow-pattern dependent models. An experimental database reconstituted from the available literature and new frictional pressure-drop data for air-water horizontal flow in an I.D. 0.0204m pipe are also presented. The full database considers several different flow conditions for horizontal two-phase flow of refrigerants and air-water mixtures. It was compared to predictions of models from the literature as well as the new proposed model. We found that the proposed and Müller-Steinhagen-and-Heck methods provide better agreement for the current experimental database. It is shown that the inclusion of void-fraction information on the previous mixture Reynolds definition improves the friction-factor prediction
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".