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Record W2778067412 · doi:10.1115/1.4038844

Effects of Surface Roughness and Bend Geometry on Mass Transfer in an S-Shaped Back to Back Bend at Reynolds Number of 200,000

2017· article· en· W2778067412 on OpenAlexaff
D. Wang, D. Ewing, C.Y. Ching

Bibliographic record

VenueJournal of Heat Transfer · 2017
Typearticle
Languageen
FieldEngineering
TopicHeat Transfer Mechanisms
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMass transferReynolds numberSurface roughnessGeometrySurface finishMaterials scienceMechanicsOpticsComposite materialTurbulencePhysicsMathematics

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.048
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.014
GPT teacher head0.253
Teacher spread0.239 · 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 teacher head, not a consensus.

Study designBench or experimental
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".

Quick stats

Citations1
Published2017
Admission routes1
Has abstractyes

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