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Record W2024921896 · doi:10.1115/ht2013-17810

Forced Convection Heat Transfer of a Phase Change Material (PCM) Nanoemulsion

2013· article· en· W2024921896 on OpenAlexafffund
Ryan Anderson, Masahiro Kawaji, Kenichi Togashi, Ravi Ramnanan-Singh

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicPhase Change Materials Research
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaEnergy Institute, City University of New YorkUniversity of TorontoCity University of New York
KeywordsMaterials sciencePhase-change materialForced convectionHeat transfer coefficientHeat transferThermal energy storageLatent heatThermodynamicsFinConvective heat transferThermalComposite material

Abstract

fetched live from OpenAlex

Phase Change Materials (PCM) are suitable for use in Thermal Energy Storage (TES) systems as they can store and release both sensible heat and latent heat during phase change. This investigation examines the thermophysical properties and heat transfer properties of a beeswax nanoemulsion during forced convection in a circular tube. First, the beeswax nanoemulsion was synthesized using surfactants and water, which possesses a relatively low viscosity to enhance pumpability, as well as a high beeswax percentage by mass for greater latent heat storage capacity. The test section was a circular stainless steel tube, 11.3 mm in diameter and heated uniformly using an Ohmic heating method. To determine the heat transfer coefficient, the inlet and exit nanoemulsion temperatures and tube wall temperatures were measured at several axial locations. The forced convection heat transfer coefficient results were first compared to water in order to verify the setup accuracy as well as the degree of success of the PCM in heat storage ability. The experimental results indicate suitable heat transfer coefficients for a stable beeswax nanoemulsion, making it a potential candidate for charging and discharging thermal energy in thermal storage applications.

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 categoriesInsufficient payload (model declined to judge)
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 score0.014
Threshold uncertainty score0.993

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)0.0080.000

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 head0.052
GPT teacher head0.287
Teacher spread0.235 · 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.

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

Citations10
Published2013
Admission routes2
Has abstractyes

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