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Record W2551149990 · doi:10.1115/ht2016-7076

A Comparison of Numerical Strategies for Optimal Liquid Cooled Heat Sink Design

2016· article· en· W2551149990 on OpenAlexaff
Ali C. Kheirabadi, Dominic Groulx

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicHeat Transfer and Optimization
Canadian institutionsDalhousie University
Fundersnot available
KeywordsMultiphysicsHeat sinkHeat transferMechanicsParametric statisticsLaminar flowMaterials scienceChannel (broadcasting)ThermodynamicsPressure dropFluid dynamicsComputational fluid dynamicsFinite element methodMathematicsComputer sciencePhysics

Abstract

fetched live from OpenAlex

This study compares two common numerical strategies for modeling flow and heat transfer through mini- and micro-channel heat sinks: the unit cell approach and a complete three dimensional unified approach. Conjugate heat transfer and laminar flow through a copper-water heat sink over a 2×2 cm2 heat source have been modelled using the finite element method within COMSOL Multiphysics 5.0; with the primary objective being to identify the channel width at which the two models yield similar temperature and pressure predictions. Parametric studies that varied channel widths showed that as these widths were reduced, and the total number of channels increased, temperature and pressure predictions from both models converged to similar values. Relative differences as low as 5.4 and 1.6 % were attained at a channel width of 0.25 mm for maximum wall temperatures and channel pressure drops, respectively. Based upon its computational efficiency and conservative over prediction of wall temperatures, the unit cell approach is recommended as a superior design tool for parametric design studies at channel widths of less than 0.5 mm.

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: Methods · Consensus signal: none
Teacher disagreement score0.929
Threshold uncertainty score0.235

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 head0.037
GPT teacher head0.285
Teacher spread0.248 · 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
GenreMethods

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

Citations2
Published2016
Admission routes1
Has abstractyes

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