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Record W2093852290 · doi:10.1115/ihtc14-22974

Computational Investigation of Laminar Mixed Convection in a Vertical Pipe With Slurries of a Microencapsulated Phase-Change Material in Distilled Water

2010· article· en· W2093852290 on OpenAlexaff
David A. Scott, Alexandre Lamoureux, B. R. Baliga

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicPhase Change Materials Research
Canadian institutionsMcGill University
Fundersnot available
KeywordsLaminar flowDistilled waterSlurryMaterials scienceFinite volume methodHeat fluxMechanicsThermodynamicsConvectionPhase-change materialCombined forced and natural convectionHeat transferNatural convectionPhase changeComposite materialPhysics

Abstract

fetched live from OpenAlex

A computational investigation of steady, laminar, mixed convection in a vertical pipe, with essentially uniform heat flux imposed on its outer surface and slurries of a microencapsulated phase-change material (MCPCM) particles suspended in distilled water as the working fluid flowing upwards, is presented. The MCPCM particles considered here have a core of solid-liquid PCM contained in a thin solid shell. The mean effective diameter of these particles is 2.5μm; the melting of the core PCM takes place primarily in the temperature range 26°C to 30°C; and the latent heat of fusion of this PCM is 129.5kJ/kg. The total length of the pipe is 2.2479m; and its inside and outside diameters are 0.01257m and 0.01588m, respectively. The main contributions of this paper are the following: i) a homogenous mathematical model is shown to be applicable to the aforementioned mixed convection phenomena; ii) correlations for the effective properties of the MCPCM slurries and procedures for their implementation are presented; iii) difficulties with the standard definition of bulk temperature when the specific heat of the fluid changes significantly with temperature are elaborated; iv) a modified bulk temperature that overcomes these difficulties is proposed; v) a finite volume method (FVM) for the solution of the proposed mathematical model is described briefly; and vi) the numerical results are presented, compared to complementary experimental data, and discussed. These comparisons show that proposed model and FVM allow cost-effective computer simulations of the problems of interest.

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 score0.053
Threshold uncertainty score0.336

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 head0.023
GPT teacher head0.257
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

Citations1
Published2010
Admission routes1
Has abstractyes

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