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Record W2973128456 · doi:10.1115/1.4044700

Impact of Phase Change Material Transition Temperature on the Performance of Latent Heat Storage Thermal Control in Tablet Computers

2019· article· en· W2973128456 on OpenAlexaff
Benjamin Sponagle, Dominic Groulx, Mary Anne White

Bibliographic record

VenueJournal of Heat Transfer · 2019
Typearticle
Languageen
FieldEngineering
TopicPhase Change Materials Research
Canadian institutionsDalhousie University
Fundersnot available
KeywordsOverheating (electricity)Latent heatPhase-change materialThermal energy storageMaterials scienceDissipationThermalPhase transitionThermal management of electronic devices and systemsComputer data storageComputer sciencePhase changeMechanical engineeringProcess engineeringElectrical engineeringEngineering physicsEngineeringThermodynamicsComputer hardware

Abstract

fetched live from OpenAlex

Abstract The processing power of handheld electronic devices has increased rapidly over the last decade. Modern handheld devices are thin (<9 mm) and utilize passive temperature control strategies. The combination of these factors has resulted in temperature control becoming a major obstacle to continued development. This work investigates the use of latent heat storage modules to improve the temperature control of tablet computers. Such modules store energy during periods of high heat dissipation and release it later when the device is less active. A key design aspect for these systems is identification of the appropriate phase change materials (PCMs), and specifically the optimal transition temperature. A numerical model of a tablet computer was created. Simulations with latent heat storage modules with transition temperatures between 35 and 47 °C showed that PCMs with lower transition temperatures allowed the tablet computer to operate longer without overheating. PCMs with transition temperatures between 35 and 40 °C were found to be most suitable.

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 score0.047
Threshold uncertainty score0.398

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 head0.021
GPT teacher head0.261
Teacher spread0.240 · 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
Published2019
Admission routes1
Has abstractyes

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