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Record W2804047343

MASS TRANSFER ON SOLUBLE WALLS WITH DEVELOPING ROUGHNESS IN PIPES AND BENDS

2016· dissertation· en· W2804047343 on OpenAlexfundno aff
Dong Wang

Bibliographic record

VenueMacSphere (McMaster University) · 2016
Typedissertation
Languageen
FieldEnvironmental Science
TopicUrban and spatial planning
Canadian institutionsnot available
FundersMcMaster University
KeywordsMass transferSurface finishMechanical engineeringEngineeringMaterials sciencePetroleum engineeringGeologyMechanicsPhysics
DOInot available

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.010
GPT teacher head0.182
Teacher spread0.172 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

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Citations0
Published2016
Admission routes1
Has abstractyes

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