MétaCan
Menu
Back to cohort
Record W2050072477 · doi:10.1115/interpack2009-89076

Improving the Performance of an Impingement Heat Sink by Modifying the Fin Shapes

2009· article· en· W2050072477 on OpenAlexaff
Sravan Gondipalli, Bahgat Sammakia, Gamal Refai-Ahmed

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicHeat Transfer and Optimization
Canadian institutionsAdvanced Micro Devices (Canada)
Fundersnot available
KeywordsHeat sinkFinThermal resistanceAirflowMaterials scienceSink (geography)Pressure dropHeat transferPlate fin heat exchangerThermalHeat spreaderMechanicsMechanical engineeringThermodynamicsEngineeringPlate heat exchangerComposite materialPhysics

Abstract

fetched live from OpenAlex

As the power dissipated by advanced microelectronic devices continues to increase, the demand for reliability also increases. This increases the requirements on the thermal performance of every part of the system, including the heat sink. One of the objectives of this study is to examine the effect of the shape of the heat sink fins on the thermal performance of the system. The pressure gradient at the center of the heat sink, near the base, tends to be high. This significantly reduces the airflow at that location and, hence, decreases transport in that region. Different fin shapes and airflow rates have been studied with the objective of searching for an optimal heat sink design that would improve the thermal performance without increasing the pressure drop across the heat sink. Parallel plate fins have been investigated by removing fin material from the region near the center of the heat sink along the length and height of the fins. The study also examines the impact of uniform and non-uniform heat sources in the device upon the overall system thermal performance. Twenty one heat sink designs with different cuts were simulated and compared and an improved heat sink design was proposed by eliminating the fin material at the center of the heat sink, thereby enhancing its thermal performance.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.118
Threshold uncertainty score0.159

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 head0.009
GPT teacher head0.201
Teacher spread0.192 · 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 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

Citations1
Published2009
Admission routes1
Has abstractyes

Explore more

Same topicHeat Transfer and OptimizationFrench-language works237,207