Drag Coefficient and Two-Phase Friction Multiplier on Tube Bundles Subjected to Two-Phase Cross-Flow
Bibliographic record
Abstract
An approximate analytical model, to predict the drag coefficient on a cylinder and the two-phase Euler number for upward two-phase cross-flow through horizontal bundles, has been developed. To verify the model, two sets of experiments were performed with an air–water mixture for a range of pitch mass fluxes and void fractions. The experiments were undertaken using a rotated triangular (RT) array of cylinders having a pitch-to-diameter ratio of 1.5 and cylinder diameter 38 mm. The void fraction model proposed by Feenstra et al. was used to estimate the void fraction of the flow within the tube bundle. An important variable for drag coefficient estimation is the two-phase friction multiplier. A new drag coefficient model has been developed, based on the single-phase flow Euler number formulation proposed by Zukauskas et al. and the two-phase friction multiplier in duct flow formulated by various researchers. The present model is developed considering the Euler number formulation by Zukauskas et al. as well as existing two-phase friction multiplier models. It is found that Marchaterre's model for two-phase friction multiplier is applicable to air–water mixtures. The analytical results agree reasonably well with experimental drag coefficients and Euler numbers in air–water mixtures for a sufficiently wide range of pitch mass fluxes and qualities. This model will allow researchers to provide analytical estimates of the drag coefficient, which is related to two-phase damping.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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".