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Record W2150999543 · doi:10.1002/cjce.22237

Performances of helical baffle heat exchangers with different baffle assembly configurations

2015· article· en· W2150999543 on OpenAlexvenueno aff
Cong Dong, Yaping Chen, Jiafeng Wu

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2015
Typearticle
Languageen
FieldEngineering
TopicHeat Transfer and Optimization
Canadian institutionsnot available
Fundersnot available
KeywordsBaffleHeat exchangerMechanicsMaterials scienceNusselt numberHeat transferTube (container)GeometryReynolds numberPhysicsMathematicsThermodynamicsComposite materialTurbulence

Abstract

fetched live from OpenAlex

The flow and heat transfer characteristics of helical baffle heat exchangers with diverse inclined angles and baffles, but similar baffle pitch and tube layout, were numerically simulated, three using non‐continuous trisection baffles, two using non‐continuous quadrant baffles, and one using a continuous helical baffle. The results show that, under the same operating conditions, the 20°TCO (trisection circumferential overlap baffles with 20° inclined angle) structure can significantly enhance shell side heat transfer with strong Dean vortex “secondary flow“ and restrained V‐notch leakage, because the shapes of trisection baffles are very suitable to equilateral triangle tube layout and there is a row of tubes to dampen the leakage flow in each circumferential overlapped area of adjacent baffles. The shell side Nusselt Number Nu o and comprehensive index ( Nu o / Eu z,o 1/3 ) of 20°TCO structure are 18.31 %, 25.82 %, 5.93 %, 6.36 %, and 15.04 %, and 15.43 %, 18.47 %, 5.30 %, 3.91 %, and 11.10 % higher than those of the 20°TEE (trisection end‐to‐end baffles with 20° inclined angle), 36.2°TMO (trisection middle overlap baffles with 36.2° inclined angle), 18°QCO (quadrant circumferential overlap baffles with 18° inclined angle), 18°QEE (quadrant end‐to‐end baffles with 18° inclined angle), and 18.4°CH (continuous helical baffle with 18.4° helical angle) structures, respectively.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.470
Threshold uncertainty score0.302

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)0.0000.000

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 head0.187
Teacher spread0.173 · 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.

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".

Quick stats

Citations7
Published2015
Admission routes1
Has abstractyes

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