Assessment of Selected Two-Phase Friction Multiplier Correlations for Steam-Water Flows in Vertical Heated and Unheated Tubes
Bibliographic record
Abstract
In this study, around 10 000 experimental two-phase (2ϕ) flow pressure drop (ΔP) data for steam-water in uniformly heated and adiabatic vertical tubes have been compiled from the literature. The compiled data have been used to assess the prediction accuracy of two-phase friction multiplier (ϕlo2) correlations selected from the literature and the homogeneous-flow model for steam-water flow in vertical tubes. The predictions of the ϕlo2 correlations have also been compared to the predictions of the homogeneous-flow model. The assessments are based on comparisons of the average and the RMS errors. Additionally, ΔP components (due to gravitation, acceleration and friction) have been examined for a variety of flow conditions in vertical heated and unheated tubes. The results of the assessments show that the predictions of Muller-Steinhagen and Heck (1986) correlation yield the minimum RMS error for flow in heated tubes and the predictions of the Chisholm (1973) correlation yield the minimum RMS error for flow in adiabatic tubes when these predictions are compared with both the data base and the predictions of the homogeneous-flow model. It is concluded from the research and a review of the literature that the current state of the art is such that none of the investigated prediction methods is very good because of their large RMS errors. More work is needed to expand the ΔP data base especially for adiabatic two-phase flow. Also, a systematic experimental study should be performed on the effect of heating on the 2ϕ-flow ΔP.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| 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".