MASS TRANSFER ON SOLUBLE WALLS WITH DEVELOPING ROUGHNESS IN PIPES AND BENDS
Bibliographic record
Abstract
Flow accelerated corrosion is a piping degradation mechanism that results in pipe wall thinning due to the dissolution of the magnetite oxide layer on carbon steel surfaces to the bulk flow. The rate limiting process of flow accelerated corrosion in piping system is the diffusion-controlled mass transfer. The surface roughness develops due to the mass transfer and can subsequently have a significant effect on the mass transfer. The naturally developing surface roughness in many dissolving surfaces, including carbon steel pipes, is a densely packed array of saucer shaped depression called scallops, which can have several length scales. Heretofore, the developing roughness on soluble walls has not been quantified, mainly due to the lack of a reliable measurement methodology. The overall objective of this research is to investigate the developing roughness and the corresponding mass transfer on soluble walls in different piping geometries. A wall dissolving method using gypsum test sections dissolving to water in a closed flow loop was used to mimic the mass transfer in carbon steel pipes due to a similar Schmidt number of 1200. A novel non-destructive measurement technique using X-ray CT scans was developed to measure the development of surface roughness and the corresponding mass transfer. The method was validated by performing experiments using straight pipe test sections and comparing against traditional measurements method using ultrasonic sensors, coordinate measurement machine and laser scans. The time evolution of surface roughness and the corresponding mass transfer were measured in pipe test sections at Reynolds number of 50,000, 100,000 and 200,000. The roughness scallops were observed to initiate locally and then develop until the surface is spatially saturated. The surface roughness was characterized by the RMS height, peak-to-valley height, integral length scale, density and spacing of the scallops. Two time periods of roughness development were identified: an initial period of slower growth in the roughness height followed by a relatively higher growth rate that corresponded to the period before and after the surface saturates with the scallops. The mass transfer enhancement due to the roughness in each of these time periods was also found to be different, with a higher increase in the first period followed by a slower increase once the streamwise spacing was approximately constant. Both the height and spacing of the roughness elements was found to affect the mass transfer enhancement. A new correlation is proposed for the mass transfer enhancement as a function of the height-to-spacing ratio of roughness, with a weak dependence on Reynolds number. The measurement methodology was extended to study the mass transfer and developing roughness in a complex S-shaped back to back bend at Reynolds number of 200,000. The mass transfer in bend geometry can be enhanced by both the local flow due to the geometry effect and the developing roughness. Two high mass transfer regions were identified: at the intrados of the first and second bends. The height-to-spacing ratio of the roughness was found to increase more rapidly in these high mass transfer regions. An additional one-time experiment was performed at a Reynolds number of 300,000. A higher surface roughness with smaller values of spacing-to-height ratio was found in the regions with high mass transfer.
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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 | 0.000 |
| 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".