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THERMAL MODELING OF TABLETS: TEMPERATURE MANAGEMENT USING PHASE CHANGE MATERIALS

2015· article· en· W2288567557 on OpenAlexaff
Benjamin Sponagle, Dominic Groulx

Bibliographic record

VenueProceeding of Proceedings of CHT-15. 6th International Symposium on ADVANCES IN COMPUTATIONAL HEAT TRANSFER , May 25-29, 2015, Rutgers University, New Brunswick, NJ, USA · 2015
Typearticle
Languageen
FieldEngineering
TopicPhase Change Materials Research
Canadian institutionsDalhousie University
Fundersnot available
KeywordsThermal energy storagePhase-change materialMaterials scienceLatent heatThermalTransient (computer programming)Phase changeMelting temperatureThermodynamicsComposite materialNuclear engineeringComputer scienceEngineering

Abstract

fetched live from OpenAlex

A numerical model representative of a modern tablet computer was developed and used to analyze the use of integrated latent heat thermal storage. Thermal storage using phase change materials (PCMs) with melting temperatures of: 31.8°C, 43.3°C and 55.8°C were simulated. The effect of the thermal storage unit on the transient temperature profile of the system on a chip (SOC) and the back cover were analyzed. It was found that the SOC temperature can be significantly reduced by the integration of the latent heat thermal storage while still having a smaller but positive effect on the temperature of the back cover. The thermal storage with a melting temperature of 31.8°C was superior to the higher melting temperatures tested, it stored more heat and produced overall lower temperatures in the tablet computer.

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)0.000
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.304
Teacher spread0.259 · 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
Published2015
Admission routes1
Has abstractyes

Explore more

Same venueProceeding of Proceedings of CHT-15. 6th International Symposium on ADVANCES IN COMPUTATIONAL HEAT TRANSFER , May 25-29, 2015, Rutgers University, New Brunswick, NJ, USASame topicPhase Change Materials ResearchFrench-language works237,207