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Record W1975369089 · doi:10.2514/1.33189

Numerical Investigation of a Radial Microchannel Heat Exchanger with Varying Cross-Sectional Channels

2008· article· en· W1975369089 on OpenAlexaff
R. Muwanga, Ibrahim Hassan, Mohamed Ghorab

Bibliographic record

VenueJournal of Thermophysics and Heat Transfer · 2008
Typearticle
Languageen
FieldEngineering
TopicHeat Transfer and Optimization
Canadian institutionsConcordia University
Fundersnot available
KeywordsMicrochannelMechanicsMaterials scienceHeat transferThermal conductionHeat sinkHeat exchangerComputational fluid dynamicsInletThermal resistanceMicro heat exchangerThermodynamicsHeat transfer coefficientMechanical engineeringPhysicsComposite materialEngineering

Abstract

fetched live from OpenAlex

This paper investigates the heat transfer performance of a radial microchannel heat exchanger with varying cross-sectional-area channels. The thermal performance of axially varying cross-sectional-area channels is compared with uniform cross-sectional-area channels. The first model is a one-dimensional thermal-resistance based model, and the second model is a three-dimensional conjugate computational fluid dynamics analysis using FLUENT software. The heat sink has a footprint area of 3.5 cm 2 and the fluid flows radially inward. The inlet aspect ratio is varied from 0.4 to 1.0, and the outlet aspect ratio is fixed at 0.5. Inclusion of axial conduction effects are found to be imperative for accurate modeling of a radial configuration using the one-dimensional thermal-resistance model. The analysis shows that when constrained by a fixed channel-outlet area, increasing the channel-inlet area will improve the thermal performance. At low pumping powers, the present scheme is found to have thermal performance that is equivalent to or better than the performances with other experimentally and numerically investigated microchannel heat sink designs.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.379
Threshold uncertainty score0.497

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 head0.021
GPT teacher head0.217
Teacher spread0.196 · 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 designBench or experimental
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

Citations16
Published2008
Admission routes1
Has abstractyes

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