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Record W4307248033 · doi:10.1016/j.egyr.2022.10.149

Thermal performance of various adiabatic section lengths of closed-loop pulsating heat pipe designed for energy recovery applications

2022· article· en· W4307248033 on OpenAlexfundno aff
Niti Kammuang-lue, Phrut Sakulchangsatjatai, Pradit Terdtoon

Bibliographic record

VenueEnergy Reports · 2022
Typearticle
Languageen
FieldEngineering
TopicHeat Transfer and Boiling Studies
Canadian institutionsnot available
FundersDepartment of Mechanical Engineering, University of AlbertaChiang Mai University
KeywordsCondenser (optics)Adiabatic processEvaporatorHeat fluxMechanicsMaterials scienceThermodynamicsWorking fluidHeat pipeMicro-loop heat pipeHeat transferPhysicsHeat exchangerOptics

Abstract

fetched live from OpenAlex

This experimental study aims to emphasize on thermal performance and temperature variation of the closed-loop pulsating heat pipe (CLPHP) affected by various adiabatic section lengths. A capillary copper tube has been bent to have 10 and 20 meandering turns to form the CLPHPs with 1.78 mm internal diameter. The evaporator section length was 150 mm, which was the same as of the condenser section length. The adiabatic section length was varied from 75 to 150, 300, and 450 mm. Ethanol, R123, and water was selected to be working fluid with the volumetric filling ratio of 50%. Hot water was pumped through the heating jacket for supplying the heat input to the evaporator section. Heat flux selected as the thermal performance in this study was measured by means of the calorific method across the cooling jacket enveloping the condenser section. It could be concluded that effect of the adiabatic section length has two different trends that are: (i) When the adiabatic section length increases, the heat flux continuously increases until the evaporator section length exceeds a certain value, the heat flux then decreases. And (ii) when the adiabatic section length increases, the heat flux continuously increases. The different effects between both trends are depended on the working fluid’s flow velocity, surface tension, density, and viscosity.

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 score0.665
Threshold uncertainty score0.547

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 head0.007
GPT teacher head0.195
Teacher spread0.188 · 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
Published2022
Admission routes1
Has abstractyes

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