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Record W2143758006 · doi:10.1109/ccece.2003.1226355

Design and characterization of a silicon base micro heat exchanger

2004· article· en· W2143758006 on OpenAlexafffund
S. Sadri-Lonbani, Mojtaba Kahrizi, Ion Stiharu

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicHeat Transfer and Optimization
Canadian institutionsConcordia University
FundersConcordia UniversityCMC Microsystems
KeywordsMicroelectronicsPlate fin heat exchangerMicro heat exchangerHeat exchangerMaterials scienceHeat transferMechanical engineeringHeat spreaderElectronic componentMicroelectromechanical systemsFinite element methodPlate heat exchangerConvective heat transferElectronicsConvectionMechanicsElectrical engineeringEngineeringStructural engineeringOptoelectronicsPhysics

Abstract

fetched live from OpenAlex

The need for greater computational power and speed in electronic devices has led to increase the circuit density. The large density is associated with larger amounts of heat generation. Most of the time, the free convection is unable to relief the heat from the components. Forced convection in conjunction with enhanced shape of the dissipation radiators are used to accelerate the heat dissipation from the electronic components. Using the field of integrated micro-sensors and micro-actuators, called MEMS, provides us an important link between microelectronics and nonelectronics applications, and a powerful enabling tool to realize such heat exchanger devices. In this work, the fundamental issues related to design of micro heat exchangers is presented. Based on these findings, a micro-heat exchanger made of non-uniform array of micro pins on silicon is designed. Finite element modeling (FEM) is used to simulate the size and shape of pins based on two different models of convection. It was found that square shape pins in compare with other shapes like circular and rectangular pins with the same area, exhibit the higher heat transfer capability with less pressure deference through the device.

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: none
Teacher disagreement score0.572
Threshold uncertainty score0.192

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 head0.013
GPT teacher head0.194
Teacher spread0.181 · 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

Citations1
Published2004
Admission routes2
Has abstractyes

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