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Thermal Characterization of a Two-Phase Integrated Heat Sink with Different Heat Source Locations

2024· article· en· W4403390724 on OpenAlexaff
Roberta Perna, Mohamed Hasan, Ahmed Elkholy, Jason Durfee, Roger Kempers

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicHeat Transfer and Optimization
Canadian institutionsMagna International (Canada)York University
Fundersnot available
KeywordsHeat sinkThermalThermal resistanceMaterials scienceEnvironmental scienceThermodynamicsPhysics

Abstract

fetched live from OpenAlex

The objective of this research is to thermally characterize a heat sink with integrated two-phase heat transport which was fabricated using laser powder bed fusion to demonstrate the potential of employing additive manufacturing (AM) design flexibility for the integration of two-phase heat spreading for variety of applications. The vertically finned heat sink base and fins are entirely hollow and is designed to spread heat throughout the heat sink using the internal two-phase heat flow. The heat sink base is interchangeable and has a porous wick region which is also additively manufactured. The wick structure provides capillary pumping of the working fluid to the heat source, which can be placed at any location on the heat sink base. In the present analysis, the thermal performance of the heat sink is examined in terms of thermal resistance for different locations of the evaporator heat source. Previous research suggests that shifting the heat source away from the center location can result in a slight improvement in thermal performance. The results showed that lower spreading resistance offered by the two-phase integrated heat sink in the bottom mode can reduce the temperature of the heating section by up to 77-87 °C compared to the middle mode using 9 ml, 12 ml and 15 ml of acetone as a working fluid.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.581
Threshold uncertainty score0.343

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 head0.008
GPT teacher head0.221
Teacher spread0.213 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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