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Record W2990656044 · doi:10.1002/cjce.23680

Influence of outlet orientation and variable tube length on high temperature zones of U‐tube trisection helical baffle electric heaters

2019· article· en· W2990656044 on OpenAlexvenueno aff
Huaduo Gu, Yaping Chen, Jiafeng Wu, Ning Song, Shifan Yang

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2019
Typearticle
Languageen
FieldEngineering
TopicHeat Transfer and Boiling Studies
Canadian institutionsnot available
FundersNational Natural Science Foundation of China
KeywordsBaffleBundleTube (container)MechanicsMaterials scienceNozzleTangentPosition (finance)GeometryHeat transferMathematicsThermodynamicsPhysicsComposite material

Abstract

fetched live from OpenAlex

Abstract Numerical simulation was performed to optimize temperature distribution especially at high temperature zones at the rear part of U‐tube bundle electric heaters with unique one‐plus‐two U‐tube layouts. The influences of variable tube lengths and different outlet nozzle orientations on flow and thermal features on axial separation trisection helical baffle schemes in contrast to segmental schemes were investigated. The results show that the variable tube length schemes can effectively ameliorate high temperature zones and obtain more uniform temperature fields. The mean heat transfer coefficient, h.t.c., and tube wall temperature, T w , of the variable tube length scheme HV15(10)°‐180° are respectively 0.7% higher and 3.1 K lower than those of the normal scheme H15(10)°‐180°. The optimal relative position of the outlet and tube bundle for helical schemes is that the outlet axis is not only tangent to the last straight edge of the baffle but is also on the side near it, while the optimum outlet angle for the segmental scheme is 90° within the research scope. Compared with segmental scheme S200‐90°, the averaged heat transfer coefficients, h.t.c.s, and comprehensive indexes, h · Δ p −1/3 , of the helical baffle schemes HV15(10)°‐180°/HV15(10)°‐Ax respectively rise by 14.6/11.8% and 16.1/13.8%, while the average T w declines by 40.7/33.3 K.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.345
Threshold uncertainty score0.334

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 head0.003
GPT teacher head0.167
Teacher spread0.164 · 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 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

Citations9
Published2019
Admission routes1
Has abstractyes

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