MétaCan
Menu
Back to cohort
Record W2108122204 · doi:10.1109/itherm.2010.5501380

Fin-shape optimization of an impingement-parallel plate heat sink

2010· article· en· W2108122204 on OpenAlexaff
Sravan Gondipalli, Bahgat Sammakia, Susan Lu, Gamal Refai-Ahmed

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicHeat Transfer and Optimization
Canadian institutionsAdvanced Micro Devices (Canada)
Fundersnot available
KeywordsHeat sinkThermal resistancePressure dropFinMaterials scienceElectronics coolingMechanicsThermalAirflowSink (geography)Heat transferMechanical engineeringThermodynamicsEngineeringComposite 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 shape of the heat sink fins on the thermal performance of the system. The pressure gradient from the fan to the base of the heat sink, near the center, tends to be high. This significantly reduces the airflow at that location and, hence, decreases transport in that region. Parallel plate heat sinks have been investigated by removing fin material near the center along the length and height of the fins. The junction-to-ambient temperature difference and pressure drop are adopted as thermal performance characteristics. Five design variables related to the fin shape were considered and the most significant influential geometric parameters for minimizing the objective functions identified using the analysis of variance (ANOVA) approach. Different fin shapes have been studied with the objective of searching for a new optimal heat sink design by optimizing the variables that improve the thermal performance without increasing pressure drop across the heat sink.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.614
Threshold uncertainty score0.999

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)0.0010.000

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 head0.216
Teacher spread0.206 · 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.

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
Published2010
Admission routes1
Has abstractyes

Explore more

Same topicHeat Transfer and OptimizationFrench-language works237,207