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Record W2123570326 · doi:10.1109/itherm.2010.5501368

Study of the transient behavior of microfluidic heat sinks

2010· article· en· W2123570326 on OpenAlexafffund
Alireza Motieifar, Cyrus Shafai, H.M. Soliman

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicHeat Transfer and Optimization
Canadian institutionsUniversity of Manitoba
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsHeat sinkHeat spreaderMaterials scienceHeat transferThermal reservoirThermal resistanceMechanicsThermodynamicsSink (geography)Transient (computer programming)Physics

Abstract

fetched live from OpenAlex

In this work the transient behavior of a microfluidic heat sink is studied. Transient response of a heat sink is important for situations where heat inputs are dynamic, especially for applications such as microprocessors that have surfaces which generate heat non-uniformly. Heat generation profile on their surface changes with time and sometimes this change is very rapid. It is the transient characteristics and behavior of the heat sink that determines the surface temperature profile in response to the dynamic heat input. Transient simulation of microfluidic heat sinks confirms that their characteristic approximately follows a simple first order RC equivalent circuit model. Simulations show that the steady state surface temperature of the copper heat sink in transient condition is lower than the surface temperature of the silicon and nickel heat sinks. Therefore, the copper heat sink is superior for long duration transient conditions. Also shown is that a larger heat sink thermal capacitance provides a delayed temperature rise on the surface of the heat sink during the transient. This indicates that using a material with higher heat conductance is not necessarily good if it possess a small heat capacitance. This is illustrated by the silicon heat sink performing poorer than the nickel heat sink, in terms of temperature rise during transient situation, even though it has a higher heat conductance.

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 score0.120
Threshold uncertainty score0.128

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 head0.010
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.

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 routes2
Has abstractyes

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