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Record W4236923416 · doi:10.5383/ijtee.12.02.010

Numerical Simulation of a Cylindrical Heat Pipe and Performance Study

2015· article· en· W4236923416 on OpenAlexvenueno aff
Mohammed Noorul Hussain, Isam Janajreh

Bibliographic record

VenueInternational Journal of Thermal and Environmental Engineering · 2015
Typearticle
Languageen
FieldEngineering
TopicHeat Transfer and Boiling Studies
Canadian institutionsnot available
Fundersnot available
KeywordsCondenser (optics)Thermal resistanceMaterials scienceMechanicsMicro-loop heat pipeEvaporatorHeat transferHeat pipeHeat sinkMultiphysicsLoop heat pipeThermodynamicsHeat fluxHeat exchangerFinite element methodPhysics

Abstract

fetched live from OpenAlex

Heat Pipes are passive devices used for transferring heat from a heat source to a heat sink with very small difference in temperature. They have three main sections namely the evaporator, adiabatic section and the condenser. In construction terms, they have three parts, the metal container, the porous wick region which lines the container from inside and the hollow flow chamber. Heat pipe performance is governed by several parameters such as the geometry, dimensions, working fluid, wick and container materials etc. In this study numerical simulation method was used to analyze the performance of the heat pipe. A numerical model based on Navier stokes equation, Energy equation and conjugate heat transfer was developed using COMSOL Multiphysics package. The top wall temperature profile obtained was validated with experimental results, and further the absolute thermal resistance was calculated. A sensitivity study was carried out to study the performance dependency of heat pipe on four parameters namely, porosity, condenser evaporator lengths, radius of heat pipe and the heat input, in terms of the absolute thermal resistance. The results showed that absolute thermal resistance varied directly with respect to porosity of the wick, and inversely in case of radius of the heat pipe. The absolute thermal resistance was maximum in case of equal condenser and evaporator lengths. Interestingly the absolute thermal resistance did not vary with the applied heat rate, demonstrating the practicality in using absolute thermal resistance as a performance characteristic parameter.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.141
Threshold uncertainty score0.249

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 head0.012
GPT teacher head0.213
Teacher spread0.202 · 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 designSimulation or modeling
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

Citations13
Published2015
Admission routes1
Has abstractyes

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