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Compact 3D-Printed Jet-Impingement Nozzle for Top-Side-Cooled GaN Power Devices

2022· article· en· W4286569380 on OpenAlexaff
Mohammad Shawkat Zaman, Ramgopal Varma Ramaraju, Seyed Amir Assadi, S. Chandra, Olivier Trescases

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicHeat Transfer and Optimization
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsNozzleMaterials scienceCoolantJet (fluid)Thermal conductivityComputer coolingMechanical engineeringThermalBoron nitrideOptoelectronicsThermal resistanceConvertersPower (physics)Thermal management of electronic devices and systemsComposite materialMechanicsEngineeringPhysicsThermodynamics

Abstract

fetched live from OpenAlex

The stringent performance requirements for power-electronic converters in electric vehicles necessitate advanced cooling techniques and device structures. This paper presents a thermal-management solution for automotive power-electronic converters that leverages top-side-cooled GaN devices and liquid-based jet-impingement cooling for enhanced thermal performance. Stereolithographic 3D printing allows the nozzle structure to be customized according to PCB geometry, minimizing volume overhead. Deposited boron nitride is used to create a thermal interface material (TIM) with high thermal conductivity and electrical isolation to separate the GaN devices from the coolant, minimizing the impact of the TIM. Experimental results using 1 L/min fluid flow show average thermal resistances as low as 1ºC/W with a heat flux of approximately 250W/cm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> , demonstrating the effectiveness of the proposed solution.

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 categoriesInsufficient payload (model declined to judge)
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 score0.815
Threshold uncertainty score0.999

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)0.0020.000

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 head0.014
GPT teacher head0.239
Teacher spread0.225 · 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.

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

Citations3
Published2022
Admission routes1
Has abstractyes

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