MétaCan
Menu
Back to cohort
Record W2075620017 · doi:10.2298/tsci141229030q

Enhanced heat transfer characteristics of conjugated air jet impingement on a finned heat sink

2015· article· en· W2075620017 on OpenAlexaff
Shuxia Qiu, Peng Xu, Liping Geng, Arun S. Mujumdar, Zhouting Jiang, Jinghua Yang

Bibliographic record

VenueThermal Science · 2015
Typearticle
Languageen
FieldEngineering
TopicHeat Transfer Mechanisms
Canadian institutionsWestern University
FundersNational Natural Science Foundation of China
KeywordsHeat sinkNusselt numberHeat transferMaterials scienceMechanicsFinJet (fluid)Reynolds numberHeat transfer enhancementThermodynamicsDimensionless quantityHeat transfer coefficientPhysicsComposite materialTurbulence

Abstract

fetched live from OpenAlex

Air jet impingement is one of the effective cooling techniques employed in micro-electronic industry. To enhance the heat transfer performance, a cooling system with air jet impingement on a finned heat sink is evaluated via the computational fluid dynamics method. A two-dimensional confined slot air impinging on a finned flat plate is modeled. The numerical model is validated by comparison of the computed Nusselt number distribution on the impingement target with published experimental results. The flow characteristics and heat transfer performance of jet impingement on both of smooth and finned heat sinks are compared. It is observed that jet impingement over finned target plate improves the cooling performance significantly. A dimensionless heat transfer enhancement factor is introduced to quantify the effect of jet flow Reynolds number on the finned surface. The effect of rectangular fin dimensions on impingement heat transfer rate is discussed in order to optimize the cooling system. Also, the computed flow and thermal fields of the air impingement system are examined to explore the physical mechanisms for heat transfer enhancement.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.022
GPT teacher head0.234
Teacher spread0.212 · 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 source (direct Gemma or distilled Codex), 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

Citations20
Published2015
Admission routes1
Has abstractyes

Explore more

Same venueThermal ScienceSame topicHeat Transfer MechanismsFrench-language works237,207