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Record W3157550313 · doi:10.3390/app11093983

Experimental Evaluation of a Latent Heat Storage Module with a Heat Spreader for Thermal Management of a Tablet Computer

2021· article· en· W3157550313 on OpenAlexaff
Benjamin Sponagle, Dominic Groulx, Mary Anne White

Bibliographic record

VenueApplied Sciences · 2021
Typearticle
Languageen
FieldEngineering
TopicHeat Transfer and Optimization
Canadian institutionsDalhousie University
Fundersnot available
KeywordsPhase-change materialMaterials scienceThermal energy storageHeat spreaderThermalLatent heatTransient (computer programming)Work (physics)Mechanical engineeringComposite materialHeat sinkComputer scienceThermodynamicsEngineering

Abstract

fetched live from OpenAlex

The objective of this work was to experimentally determine the feasibility of using a phase change material (PCM)-based temperature control module, in conjunction with a heat spreader and thermal interface material, to improve the thermal management of a tablet computer. An experimental apparatus was designed to be representative of a tablet computer. This mock tablet was used to perform a series of transient heating and cooling experiments to compare the impact of the PCM module on the thermal response of the system. The PCM module consisted of n-eicosane encapsulated with heat-sealable laminated film forming a 2 mm thick sheet of encapsulated PCM. A full comparison, including the use of a heat spreader and a thermal interface material (TIM), was conducted at heat generation rates of 4.5 and 7 W. The temperature control module was able to reduce the mean and peak temperatures of the internal components and at a heat generation rate of 7 W it extended its operating time by 30% before it reached a critical threshold temperature.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.012

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 studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.030
GPT teacher head0.253
Teacher spread0.223 · 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

Citations8
Published2021
Admission routes1
Has abstractyes

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