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EFFECTS OF FIN SPACING AND FIN HEIGHT OF CAPILLARY-ASSISTED TUBES ON THE PERFORMANCE OF A LOW OPERATING PRESSURE EVAPORATOR FOR AN ADSORPTION COOLING SYSTEM

2015· article· en· W2375081452 on OpenAlexaff
Poovanna Cheppudira Thimmaiah, Amir Sharafian, Wendell Huttema, Majid Bahrami

Bibliographic record

VenueHeat Pipe Science and Technology An International Journal · 2015
Typearticle
Languageen
FieldEngineering
TopicAdsorption and Cooling Systems
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsEvaporatorFinRefrigerantVapor-compression refrigerationMaterials scienceEvaporationRefrigerationCapillary actionTube (container)Heat transferThermodynamicsMechanicsCoefficient of performanceCooling capacityHeat transfer coefficientWater coolingEvaporative coolerInletComposite materialHeat exchangerMechanical engineeringEngineeringPhysics

Abstract

fetched live from OpenAlex

Adsorption cooling systems (ACS) are a viable alternative to vapor compression refrigeration cycles where low-grade waste heat is abundant. When using water as a refrigerant in ACS, the operating pressure is quite low (< 5 kPa) and the performance of the system is severely affected when using conventional evaporators. This problem can be addressed by using capillary-assisted evaporators. In this study, a new capillary-assisted evaporator test bed is designed and built, and three enhanced tubes with different fin geometries (fin spacing and fin height) and a plain tube are tested under different chilled water inlet temperatures. The results show that enhanced tubes provide 1.65−2.23 times higher total evaporation heat transfer rate compared to the plain tube. Under equal inner and outer heat transfer surface areas, the results also show that the enhanced tube with parallel continuous fins and higher fin height (Turbo Chil-26 FPI) has 13% higher evaporation heat transfer coefficient than that of a tube with lower fin height (GEWA-KS-40 FPI).

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)0.001
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 score0.471
Threshold uncertainty score0.222

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.013
GPT teacher head0.244
Teacher spread0.231 · 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

Citations0
Published2015
Admission routes1
Has abstractyes

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Same venueHeat Pipe Science and Technology An International JournalSame topicAdsorption and Cooling SystemsFrench-language works237,207