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Record W2973716647 · doi:10.3390/mi10090620

Analysis and Optimization of a Microchannel Heat Sink with V-Ribs Using Nanofluids for Micro Solar Cells

2019· article· en· W2973716647 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueMicromachines · 2019
Typearticle
Languageen
FieldEngineering
TopicHeat Transfer and Optimization
Canadian institutionsLaurentian University
FundersNational Natural Science Foundation of China
KeywordsNanofluidHeat sinkHeat transferNusselt numberCoolantMicrochannelMechanicsMaterials scienceReynolds numberThermalThermodynamicsPhysics

Abstract

fetched live from OpenAlex

It is crucial to control the temperature of solar cells for enhancing efficiency with the increasing power intensity of multiple photovoltaic systems. In order to improve the heat transfer efficiency, a microchannel heat sink (MCHS) with V-ribs using a water-based nanofluid as a coolant for micro solar cells was designed. Numerical simulations were carried out to investigate the flows and heat transfers in the MCHS when the Reynolds number ranges from 200 to 1000. The numerical results showed that the periodically arranged V-ribs can interrupt the thermal boundary, induce chaotic convection, increase heat transfer area, and subsequently improve the heat transfer performance of a MCHS. In addition, the preferential values of the geometric parameters of V-ribs and the physical parameters of the nanofluid were obtained on the basis of the Nusselt numbers at identical pump power. For MCHS with V-ribs on both the top and bottom wall, preferential values of V-rib are rib width d / W = 1 , flare angle α = 75 ° , rib height h r / H = 0.3 , and ratio of two slant sides b / a = 0.75 , respectively. This can provide sound foundations for the design of a MCHS in micro solar cells.

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.

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: none
Teacher disagreement score0.388
Threshold uncertainty score0.606

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 head0.005
GPT teacher head0.195
Teacher spread0.190 · 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