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Record W1154893782 · doi:10.1115/imece2014-38812

Experimental Validation of Numerical Simulations of a Closed-Loop Thermosyphon Operating With Slurries of a Microencapsulated Phase-Change Material

2014· article· en· W1154893782 on OpenAlexafffund
Alexandre Lamoureux, B. R. Baliga

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicHeat Transfer and Optimization
Canadian institutionsMcGill UniversityHatch (Canada)
FundersFonds Québécois de la Recherche sur la Nature et les TechnologiesNatural Sciences and Engineering Research Council of CanadaMcGill University
KeywordsMechanicsMaterials scienceSlurryThermosiphonHeat transferLaminar flowThermodynamicsNon-Newtonian fluidPhysicsComposite material

Abstract

fetched live from OpenAlex

Experimental validation and calibration of numerical simulations of a closed-loop thermosyphon operating under steady-state conditions with slurries of a microencapsulated phase-change material (MCPCM) suspended in distilled water are presented. The slurries exhibited a non-Newtonian, shear-thinning, power-law rheological behavior in the range of parameters considered; and the constants in the related model were calibrated using data from specially conducted experiments. The flows of these slurries in the problems of interest were laminar. Furthermore, the velocity and temperature differences between the dispersed and conveying phases of these slurries were negligibly small, so homogeneous models could be used for mathematical representations of the fluid flow and heat transfer phenomena. A hybrid numerical method was used in the simulations: detailed two-dimensional axisymmetric control-volume finite element (CVFEM) simulations of the heated and cooled sections of the thermosyphon were coupled with segmented quasi-one-dimensional finite volume (FVM) simulations of the other portions. The CVFEM and FVM used in this work are well-established. Thus, the verification of these methods is not addressed here. Rather, the details of the thermosyphon, effective properties of the MCPCM and slurries, overviews of the hybrid model and the aforementioned numerical methods, notes on the experimental calibration and validation, and some results are presented and discussed.

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 score0.159
Threshold uncertainty score0.315

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 head0.016
GPT teacher head0.250
Teacher spread0.234 · 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
Published2014
Admission routes2
Has abstractyes

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