MétaCan
Menu
Back to cohort
Record W4319781558 · doi:10.11159/jffhmt.2023.001

Phase Change Materials for Better Thermal Performance of Central Processing Units (CPUs)

2023· article· en· W4319781558 on OpenAlexvenueno aff
S. Aal Khlaifin, A. Al-Janabi, N. Al-Rawahi, N. Al-Azri

Bibliographic record

VenueJournal of Fluid Flow Heat and Mass Transfer · 2023
Typearticle
Languageen
FieldEngineering
TopicHeat Transfer and Optimization
Canadian institutionsnot available
Fundersnot available
KeywordsComputer sciencePhase (matter)ThermalParallel computingChemistryThermodynamicsPhysics

Abstract

fetched live from OpenAlex

Nowadays, the small size of electronic components causes an increase in the heat flux that is dissipated at their surfaces, consequently, efficient cooling systems are required to improve reliability, prevent premature failure as well as keep components within permissible operating temperature limits.Most electronic devices use forced air-cooling which nowadays is somehow not adequate or even enough to keep the system cool.This study is aimed at investigating a passive cooling technique using the phase change material (PCM) type (RT35HC) to absorb the generated heat at the CPU of a personal computer and keep its surface temperature within an acceptable range.To do so, a set of 20 experiments were conducted where four different amounts of the PCM (20, 40, 60, and 80g) were subjected to five levels of dissipated heat (10, 20, 30, 40, and 50W).The results showed using RT35HC regardless of its amount enhanced the thermal performance of the CPU and provided a longer operating period while keeping the CPU surface temperate constant for low levels of dissipated heat (10-20W).On the other hand, increasing the PCM amount could help in providing a better thermal performance of the CPU that dissipated a higher amount of heat (30-50W) compared to thermal performance without using the PCM.The optimum amount of the PCM that can be used for passive cooling of the CPU was found to be 60g.

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 score0.245
Threshold uncertainty score0.415

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 head0.027
GPT teacher head0.239
Teacher spread0.212 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Fluid Flow Heat and Mass TransferSame topicHeat Transfer and OptimizationFrench-language works237,207