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Record W4381619433 · doi:10.11159/ffhmt23.180

A Study on the Heat Transfer Characteristics of Semi- Flooded Type Evaporator Fin Tube for Adsorption Chiller

2023· article· en· W4381619433 on OpenAlexvenueno aff
S.K. Noh, Van Cong Le, Huynh Tan Loc, Van Hau Duong, Chan Woo Park

Bibliographic record

VenueProceedings of the ... International Conference on Fluid Flow, Heat and Mass Transfer · 2023
Typearticle
Languageen
FieldEngineering
TopicAdsorption and Cooling Systems
Canadian institutionsnot available
FundersKorea Institute of Energy Technology Evaluation and PlanningMinistry of SMEs and StartupsNational Research Foundation of KoreaMinistry of Trade, Industry and EnergyNational Research FoundationKorea Industrial Technology Association
KeywordsEvaporatorChillerFinAdsorptionTube (container)Heat transferMaterials scienceThermodynamicsNuclear engineeringEnvironmental scienceChemistryHeat exchangerEngineeringPhysicsComposite material

Abstract

fetched live from OpenAlex

The purpose of this study is to compare the heat transfer performance of an adsorption chiller's evaporator with and without a hydrophilic coating applied to a fin-tube heat exchanger.The evaporator used in this study is a semi-flooded type, which utilizes the capillary phenomenon to induce efficient refrigerant evaporation without requiring a refrigerant circulation pump.The hydrophilic coating is prepared by using a coating solution of aluminum oxide and a water-soluble hardening agent, which is then applied using the dip-coating method.The researchers investigated the effect of the weight ratio of aluminum oxide powder as a coating variable and found that a coating solution with a weight ratio of 31 wt% resulted in excellent coating surfaces in almost all ranges.The study revealed that the heat transfer rate and overall heat transfer coefficient of the fin-tube coated with this coating method increased by up to 1.6 times compared to the uncoated case under the operating conditions of the adsorption-type evaporator.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.515
Threshold uncertainty score0.538

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 head0.038
GPT teacher head0.250
Teacher spread0.211 · 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
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of the ... International Conference on Fluid Flow, Heat and Mass TransferSame topicAdsorption and Cooling SystemsFrench-language works237,207