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Record W2403656793 · doi:10.1109/apec.2016.7468156

Thermal analysis of a magnetic packaged power module

2016· article· en· W2403656793 on OpenAlexaff
Laili Wang, Doug Malcolm, Wenbo Liu, Yan‐Fei Liu

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicSilicon Carbide Semiconductor Technologies
Canadian institutionsQueen's University
Fundersnot available
KeywordsThermal resistanceInductorPower modulePower (physics)ThermalPower densityElectrical engineeringElectronic componentThermal conductivityMaterials scienceConvertersComputer scienceElectronic engineeringMechanical engineeringEngineeringPhysics

Abstract

fetched live from OpenAlex

Power density of converters have been dramatically increased through the innovations of packaging and integration technologies. Meanwhile, it also imposes more challenges on the thermal performances. An integrated power module packaged with magnetic component is proposed to improve both electrical and thermal performances. This paper presents the thermal analysis of the proposed power module. The magnetic component acts as both the filter inductor in the converter and the package of the power module. Benefiting from this package technology, the inductor can be designed with a bigger winding of lower resistance, thus generating less heat. The magnetic material has better thermal conductivity than plastic material used in conventional plastic packaged power modules; therefore, the power module has better thermal performance. Simulation is executed to show thermal effect of winding configurations. A thermal evaluation board is built to compare thermal performances of the proposed power module and two other commercial products. The proposed power module has 11°C lower than the other part with the same size.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.071
Threshold uncertainty score0.998

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 head0.009
GPT teacher head0.199
Teacher spread0.191 · 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 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

Citations12
Published2016
Admission routes1
Has abstractyes

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