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Record W2794248513 · doi:10.2514/1.t5341

Energy Storage by Melting Commercial Change Phase Materials in Hexagonal-Shaped Heat Exchangers

2018· article· en· W2794248513 on OpenAlexaff
Latifa Begum, Mainul Hasan, Georgios H. Vatistas

Bibliographic record

VenueJournal of Thermophysics and Heat Transfer · 2018
Typearticle
Languageen
FieldEngineering
TopicPhase Change Materials Research
Canadian institutionsMcGill UniversityConcordia University
Fundersnot available
KeywordsMaterials scienceTube (container)Phase-change materialHeat exchangerMechanicsInner coreNusselt numberPhase (matter)Thermal energy storageThermodynamicsComposite materialReynolds numberThermalPhysics

Abstract

fetched live from OpenAlex

A two-dimensional melting of a technical-grade phase-change material is modeled. The phase-change material is placed in the annular space formed by an outer regular hexagonal tube and an inner tube. A nonorthogonal boundary fitted coordinate method is implemented on a staggered grid arrangement. The mushy region of the impure phase-change material has been accommodated through the enthalpy-porosity scheme. A set of parametric studies is carried out. Selected results are presented as a function of time for the velocity and temperature fields, the cumulatively stored energy, the total melt fraction, and the average Nusselt number on the inner tube. The oblate-shaped inner tube results in the highest melting rate and stores the maximum energy among the studied units irrespective of the shape and vertical position of the inner tube. Among the three vertical positions of the inner circular tube, when the center of the inner tube is placed at one-third height from the base of the outer tube, it shows the greatest thermal efficiency, and the worst performance is obtained for the upper position of the inner tube at the two-thirds height of the outer tube. With the increase in the Rayleigh number (wall temperature), the amount of energy storage is enhanced.

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 score0.008
Threshold uncertainty score0.777

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 head0.045
GPT teacher head0.291
Teacher spread0.246 · 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

Citations7
Published2018
Admission routes1
Has abstractyes

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