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Record W4367627015 · doi:10.3390/en16093825

Comparing the Performance of a Straight-Channel Heat Sink with Different Channel Heights: An Experimental and Numerical Study

2023· article· en· W4367627015 on OpenAlexafffund
Mahsa Hajialibabaei, M. Ziad Saghir, Yusuf Biçer

Bibliographic record

VenueEnergies · 2023
Typearticle
Languageen
FieldEngineering
TopicHeat Transfer and Optimization
Canadian institutionsToronto Metropolitan University
FundersNatural Sciences and Engineering Research Council of CanadaQatar National Research FundFonds National de la Recherche LuxembourgQatar Foundation
KeywordsHeat sinkNusselt numberMechanicsPressure dropThermalVolumetric flow rateThermodynamicsMaterials scienceReynolds numberPhysics

Abstract

fetched live from OpenAlex

Straight-channel heat sinks are widely used as a cooling method for electronic devices and lithium battery thermal management. This study aims to enhance the thermal performance of a straight-channel heat sink while decreasing the pressure drop. In this design, the height of inside channels is reduced to provide open space above the channel while the size of the heat sink remains constant. Both experimental investigation and numerical analysis were conducted to study the thermal efficiency of heat sinks with varying heights, at flow rates ranging from 6.94 to 19.56 m3/s and heat fluxes between 3.8 and 7.43 W/cm2. The comparison is carried out between channels with four different heights in terms of temperature distribution, local Nusselt number, velocity, and flow characteristic. The results indicate that lowering the height of the channel from 12.7 to 7 and 4 mm raises the highest temperature of the heat sink, whereas the change in height to 10 mm reduces the temperature. Furthermore, increasing the flow rate has a higher impact on improving the Nusselt number in channels with a height of 10 mm. When the height is decreased from 12.7 to 10 mm, the performance evaluation criterion is obtained higher than one for all flow rates.

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 score0.100
Threshold uncertainty score0.291

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 head0.020
GPT teacher head0.226
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 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

Citations11
Published2023
Admission routes2
Has abstractyes

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