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Record W4382199346 · doi:10.1139/tcsme-2022-0180

Numerical investigation of microchannel heat sink with novel ogive shape ribs

2023· article· en· W4382199346 on OpenAlexvenueno aff
Haseeb Ali, Fayyaz Alam, Kareem Akhtar, Azed Abbas, Nasir Shah, Faraz Ahmad, Sahar Noor

Bibliographic record

VenueTransactions of the Canadian Society for Mechanical Engineering · 2023
Typearticle
Languageen
FieldEngineering
TopicHeat Transfer and Optimization
Canadian institutionsnot available
Fundersnot available
KeywordsNusselt numberHeat sinkThermalMechanicsDissipationHeat transferEngineeringPhysicsMechanical engineeringThermodynamicsReynolds number

Abstract

fetched live from OpenAlex

In this study, 3D numerical conjugate heat transfer modeling is used to investigate the thermal and hydraulic characteristics of the microchannel heat sink (MCHS) with different configurations of novel ogive shape ribs on channel walls. It was found that new proposed MCHS configurations with ogive ribs have a high Nusselt number as compared to the smooth MCHS because ogive ribs enhance the heat dissipation between channel walls and fluid by continuously interrupting the thermal boundary layer development. MCHS configuration with ogive ribs mounted on bottom wall improves the Nusselt number of smooth MCHS by 1.13–1.87 times, while MCHS with ogive ribs mounted on both side walls and MCHS with ogive ribs mounted on bottom channel wall improve Nusselt number by 1.12–1.70 and 1.08–1.59 times, respectively, at Re 100–1000. In terms of thermal enhancement factor criterion, the MCHS with ogive ribs on side walls shows superior performance at Re 100–300 and has the highest thermal enhancement factor. While MCHS with ogive ribs on bottom wall outperformed other configurations in terms of thermal enhancement factor at Reynold number > 300. A maximum thermal enhancement factor of 1.42 is reported for MCHS with bottom wall ribs ogive at Re = 1000.

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: none
Teacher disagreement score0.955
Threshold uncertainty score0.477

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.187
Teacher spread0.174 · 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

Citations9
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueTransactions of the Canadian Society for Mechanical EngineeringSame topicHeat Transfer and OptimizationFrench-language works237,207