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Record W3011452604

Passive cooling system: An integrated solution to the application in power electronics

2019· dissertation· en· W3011452604 on OpenAlexfundno aff
Zhongchen Zhang

Bibliographic record

VenueSummit (Simon Fraser University) · 2019
Typedissertation
Languageen
FieldEngineering
TopicHeat Transfer and Optimization
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaSimon Fraser UniversityBritish Columbia Innovation Council
KeywordsElectronicsPower electronicsEngineeringElectrical engineeringPower (physics)Water coolingSystems engineeringMechanical engineeringPhysicsVoltageThermodynamics
DOInot available

Abstract

fetched live from OpenAlex

Passive cooling systems are commonly used in power electronic industries to dissipate the tremendous excess heat generated in semiconductors devices to maintain the efficiency, reduce the thermal stress, and prevent the thermal runaway along with component failures. This research, which has been collaborated with our industrial partner, Delta-Q Technologies, aims to enhance the overall heat rejection capacity of a commercially-available naturally cooled battery charger heat sink by focusing on the fundamental heat transfer mechanisms of thermal radiation and natural convection at the same time. In this study, the effect of anodization in various types of aluminum alloy (die-cast A380, 6061) and its thermal impact was investigated. The thermal emissivity of anodized samples was measured with Fourier Transform Infrared Reflectometer (FTIR) spectroscopy. A customized test chamber was built in our lab to carry out the steady-state thermal tests. A conjugated numerical heat transfer model was developed in Ansys Fluent in case of both natural convection and thermal radiation. Various novel fin geometries for Naturally Cooled Heat Sinks (NCHx) were also designed, prototyped, tested, and compared in terms of different surface conditions and operational orientations. A sensitivity analysis of geometrical parameters in one of the most promising fin geometries, inclined interrupted fins, was performed and analyzed. The results reveal an up to 27% overall enhancement with regard to the current IC650 design (benchmark case).

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.439
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.005
GPT teacher head0.193
Teacher spread0.188 · 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

Citations0
Published2019
Admission routes1
Has abstractyes

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