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Record W2766335977 · doi:10.1115/ht2017-4801

Investigation of the Thermal Behaviour of Thin Phase Change Material Packages as a Solution to Temperature Control in Electronics

2017· article· en· W2766335977 on OpenAlexafffund
Benjamin Sponagle, Simon Maranda, Dominic Groulx

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicPhase Change Materials Research
Canadian institutionsDalhousie University
FundersNatural Sciences and Engineering Research Council of CanadaIntel Corporation
KeywordsMaterials sciencePhase-change materialElectronicsThermalTemperature controlElectronic packagingWork (physics)Temperature measurementPhase changeFinite element methodThin filmMechanical engineeringComposite materialElectrical engineeringThermodynamicsEngineeringStructural engineeringEngineering physicsNanotechnology

Abstract

fetched live from OpenAlex

This paper presents an experimental and numerical investigation of the thermal behavior of thin phase change material (PCM) packages as a solution to thermal management in portable electronic devices. The thin packages are made of encapsulated PCM in aluminized laminated film. The experimental setup is designed to include the most fundamental aspects of a portable electronic system while also being simple enough to be easily simulated using the finite element method; it is rectangular in nature. Two different types of PCM are used for the experimental work; the commercially available PT-37 and n-eicosane. It was determined that the use of a thin PCM thermal energy storage package significantly improved the temperature behavior of the experimental setup, by reducing the rate of temperature increase at the heater and the back cover. The time needed to reach a critical cover temperature of 45°C was increased by 12 to 18% while the time for the heater temperature to reach 70°C was increased by up to 66%. Numerical simulations of the system were in good agreement with the experimental data.

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 score0.025
Threshold uncertainty score0.293

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 head0.042
GPT teacher head0.304
Teacher spread0.262 · 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
Published2017
Admission routes2
Has abstractyes

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