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Record W4392927091 · doi:10.32920/25417267.v1

Studies in Ground Heat Transfer and Thermal Energy Storage Using Phase Change Material and Nanoparticles

2024· preprint· en· W4392927091 on OpenAlexaff
Reza Daneshazarian

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicPhase Change Materials Research
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsPhase-change materialOctadecaneThermal energy storageHeat transferMaterials scienceThermodynamicsEnclosureThermalEnergy storageHeat transfer enhancementLatent heatNanoparticleComposite materialChemical engineeringChemistryNanotechnologyHeat transfer coefficientEngineeringElectrical engineeringPhysics

Abstract

fetched live from OpenAlex

The thesis begins with a literature review of phase change material (PCM) and heat transfer intensification methods. The challenges of using these methods are discussed, and the potential of adding nanoparticles as one of the heat transfer enhancement methods is explored thoroughly and presented. Also, the potential of implementing thermally enhanced latent heat thermal energy storage (TES) in a ground source heat pump (GSHP) system is presented. A detailed study has been carried out on developing an enhanced numerical approach to investigate the melting performance of nanoPCM (exfoliated graphene nanoplatelets (xGnP)octadecane) filled in a vertical cylindrical enclosure at different weight concentrations. The results showed that by adding 0.5wt% of xGnP in the base PCM (octadecane), the melting rate decreases by 9.7% and the heat storage rate increases by 12.6%.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

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 communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.195
GPT teacher head0.369
Teacher spread0.173 · 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 source (direct Gemma or distilled Codex), 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
Published2024
Admission routes1
Has abstractyes

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