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Record W4404108052 · doi:10.1002/cjce.25536

Experimental study on heat transfer and resistance of round tube with tube sheet under ultrasonic action

2024· article· en· W4404108052 on OpenAlexvenueno aff
Xiaohan Lv, Chulin Yu, Wenqing Wang, Yuxi Yang, Haiqing Zhang

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2024
Typearticle
Languageen
FieldEngineering
TopicHeat Transfer and Boiling Studies
Canadian institutionsnot available
Fundersnot available
KeywordsTube (container)Ultrasonic sensorMaterials scienceHeat transferAction (physics)MechanicsAcousticsComposite materialPhysics

Abstract

fetched live from OpenAlex

Abstract Energy is vital to the survival and development of human beings. In the era of ‘carbon neutrality’, all walks of life around the world are upgrading their technological capabilities to reduce carbon emissions. As a new type of active strengthening technology to improve the comprehensive performance of heat exchangers, ultrasonic technology has attracted the attention of more and more scholars. From the actual installation situation, a set of ultrasonic enhanced heat transfer test device with tube sheet round tube was built. After experimental research and data analysis, the influence of ultrasonic sound intensity, frequency, and position on heat transfer, flow resistance, and comprehensive performance of heat exchange tube under different working conditions and without ultrasonic wave is studied. The results show that the ultrasonic enhanced heat transfer and drag reduction capacity increases with the decrease of ultrasonic frequency, and under the action of ultrasonic waves of different frequencies, the critical sound intensity of its enhanced heat transfer and drag reduction performance is different. The drag reduction performance of the ultrasonic heat exchange tube is increased by 18.41%, the heat transfer performance is increased by 36.53%, and the overall performance is increased by 20.57%.

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 score0.072
Threshold uncertainty score0.381

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 head0.015
GPT teacher head0.224
Teacher spread0.208 · 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

Citations1
Published2024
Admission routes1
Has abstractyes

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