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Numerical Evaluation of Heat Transfer and Entropy Generation of Helical Tubes with Various Cross-sections under Constant Heat Flux Condition

2018· article· en· W2899712736 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueMATEC Web of Conferences · 2018
Typearticle
Languageen
FieldEnergy
TopicSolar Thermal and Photovoltaic Systems
Canadian institutionsMcGill University
Fundersnot available
KeywordsHeat transferHeat fluxPressure dropThermodynamicsMechanicsCurvatureEntropy (arrow of time)Computational fluid dynamicsMaterials sciencePhysicsGeometryMathematics

Abstract

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The presence of curvature-induced secondary flow in helical pipe which create complex transport phenomena and higher transfer rate has attracted significant attention from both academic and industry. Flow behavior and transport processes in helical tube have been intensively investigated. Nevertheless, most studies were focused on the performance based on first law of thermodynamics with limited studies concerning the performance based on second law of thermodynamics. The objective of this study is to investigate the heat transfer performance of helical tube according to both first and second law. The heat transfer rate and entropy generation of helical tubes with various cross-sections, i.e. circular, ellipse and square, subjected to constant wall heat flux conditions are numerically evaluated by utilizing computational fluid dynamics (CFD) approach. Their performances are compared to those of straight tube with identical cross-section. The results indicate that helical tube provides higher heat transfer at the cost of higher pressure. Moreover, it was found that entropy generation in helical tubes is considerably lower as compared to that in straight tube. Among the studied cross-sections, square has the highest heat transfer albeit having the highest pressure drop and entropy generation for both straight and helical tubes.

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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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
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 score0.044
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

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 head0.047
GPT teacher head0.294
Teacher spread0.247 · 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