A Simple Two-Phase Frictional Multiplier Calculation Method
Bibliographic record
Abstract
In this paper, a simple method for calculating two-phase frictional multiplier for total flow assumed liquid in the pipe φlo2 is presented. The homogeneous model is used to calculate the fluid properties (density and viscosity). The Churchill model is used to define the Fanning friction factor to take into account the effect of the mass flux on φlo2. Effect of stream pressure on φlo2 is also investigated. It is found that φlo2 decreases with increasing the stream pressure at a given mass quality and reaches 1 at the critical pressure. On the other hand, it is found that φlo2 increases with increasing the mass flux at a given mass quality. Comparison with other existing correlations for calculating φlo2 such as the Wallis correlation based on the homogeneous model without mass effect on φlo2, the Martinelli-Nelson correlation, the Chisholm correlation, and the Friedel correlation is presented. When the mass flux value becomes low, the effect of mass flux on φlo2 becomes small and present correlation approaches the Wallis correlation. Both the present correlation and the Wallis correlation approach the maximum two-phase frictional multiplier in a smooth consistent manner while the other correlations show a peaking effect at high mass qualities. The Friedel correlation shows better agreement with the present correlation than both the Martinelli-Nelson correlation and the Chisholm correlation. Comparison with results from other experimental test facilities for calculating φlo2 is also presented. Comparison with other experimental data shows better agreement with the present correlation than the Martinelli-Nelson correlation.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".