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Record W2806290846 · doi:10.11159/ffhmt18.190

Effect of Vibration on the Melting of Phase Change Material inside a Cylindrical Enclosure

2018· article· en· W2806290846 on OpenAlexaffvenue
Mohammadhossein Hajiyan, Manar Al-Jethelah, Yazeed Alomai, Muath Alomair, Syeda Humaira Tasnim, Shohel Mahmud

Bibliographic record

VenueProceedings of the ... International Conference on Fluid Flow, Heat and Mass Transfer · 2018
Typearticle
Languageen
FieldEngineering
TopicEngineering Applied Research
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsEnclosureVibrationPhase-change materialMaterials sciencePhase changePhase (matter)Composite materialAcousticsEngineeringPhysicsElectrical engineeringEngineering physics

Abstract

fetched live from OpenAlex

The present study reports a novel numerical work on the melting of phase change material (PCM) inside a cylindrical enclosure under the application of vibration. No such work is available in the existing literature. To simulate the melting of PCM inside a vertical cylindrical enclosure, the governing equations, namely, the conservation of mass, momentum, and energy equations are solved. In addition to that enthalpy-porosity method is used to model phase change of PCM. COMSOL Multiphysics software is used to simulate the melting behaviour of a PCM during vibration conditions. The objective of the present work is to observe the melting behaviour under the application of vibration. Melting behaviour of PCM inside a vertical cylindrical enclosure has been simulated first without vibration for comparison purpose. It has been observed that vibration affects the melting behaviour significantly. This study is applicable to battery thermal management for the hybrid vehicles where PCM can be used for controlling the temperature of the battery.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
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 head0.027
GPT teacher head0.269
Teacher spread0.242 · 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 designSimulation or modeling
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

Citations4
Published2018
Admission routes2
Has abstractyes

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