MétaCan
Menu
Back to cohort
Record W1997166501 · doi:10.1115/imece2010-40649

Three-Dimensional Multiphysics Analyses of a CPU Integrated Thermal Heat Sink

2010· article· en· W1997166501 on OpenAlexaff
Maher Al-Dojayli, Ellen Chan, Sunny Leung, Hani E. Naguib, F.P. Dawson, Vincent Adinkrah, Laszlo Lakatos‐Hayward

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicHeat Transfer and Optimization
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMultiphysicsHeat sinkHeat transferFinMechanicsComputational fluid dynamicsMaterials scienceThermal resistanceThermalMechanical engineeringNatural convectionConvectionFinite element methodStructural engineeringEngineeringThermodynamicsPhysics

Abstract

fetched live from OpenAlex

Recent advances in electronic packaging have led to small, lightweight and highly efficient heat sink designs. Some of these attempts are aiming to integrate the heat sink with the packaging wall structure. In this paper, a three-dimensional multiphysics numerical model is developed for the integrated heat sink to carry out CFD and thermal analyses, stress analyses due to thermal expansion and modal analyses. Finite volumes were used to model the conjugate heat transfer (CHT) for the coupled fluid-structure fields representing the air and heat sink fin walls and base, respectively. In this analysis, both natural and forced convection analyses were considered. The predicted temperature distribution was then used to calculate the mechanical stresses due to thermal expansion, using finite elements. Lastly, modal analyses were conducted to calculate the natural frequencies of the model. The effect of varying the source heat generation rate, air flow speed, and some geometry features such as number of fins and fin’s height on the performance and structural integrity of the assembly have been studied.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.449
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 head0.019
GPT teacher head0.250
Teacher spread0.231 · 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
Published2010
Admission routes1
Has abstractyes

Explore more

Same topicHeat Transfer and OptimizationFrench-language works237,207