Determination of Flow Structure in a Tube Array by Particle Image Velocimetry
Bibliographic record
Abstract
Particle Image Velocimetry (PIV) presents a possible approach to measuring two-phase flow parameters over a large area, leading to a snap shot of flow behavior in complex geometries such as tube bundles. Tests have been conducted in a 2m long wavy wall channel simulating the open lane within a rotated triangular array. The results show that liquid phase PIV measurements must be limited to very low void fractions. On the other hand, much information can be gained from the gas phase data. The details of the flow structure within the array are revealed. Current measurements, performed in the bubbly flow regime, show that even in this regime, the flow structure is significantly non-uniform and complex. Bubble diameters have been found to be strongly dependent on flow velocity; the effect of turbulence shear at high flow rates breaking up the bubbles to smaller sizes. The PIV measurements yield the complete averaged velocity vector field in the measurement region. The velocity profiles across the measurement section are also obtained. The non-uniform profiles show the challenges associated with attempts to estimate average void fractions and slip ratios in the array.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".