Effects of Surface Roughness and Bend Geometry on Mass Transfer in an S-Shaped Back to Back Bend at Reynolds Number of 200,000
Bibliographic record
Abstract
Experiments were performed to investigate the local development of roughness and its effect on mass transfer in an S-shaped bend at Reynolds number of 200,000. The tests were performed over four consecutive time periods using a 203-mm-diameter test section with a dissolving gypsum lining to water in a closed flow loop at a Schmidt number of 1200. The surface roughness and the mass transfer over the test periods were measured using X-ray computed tomography (CT) scans of the surface. Two regions of high mass transfer are found: along the intrados of the first and second bends. The surface roughness in these two regions, characterized by the height-to-spacing ratio, grows more rapidly than in the upstream pipe. There is an increase in the mass transfer with time, which corresponds well with the local increase in the height-to-spacing ratio of the roughness. The two regions of high mass transfer enhancement in the bend can be attributed to both a roughness effect and a flow effect due to the bend geometry. The geometry effect was determined by normalizing the local mass transfer with that in a straight pipe with equivalent surface roughness. The mass transfer enhancement due to the geometry effect was found to be relatively constant for the two high mass transfer regions, with a value of approximately 1.5.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".