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Record W3130253391 · doi:10.1109/tpel.2021.3059558

Jet Impingement Cooling in Power Electronics for Electrified Automotive Transportation: Current Status and Future Trends

2021· article· en· W3130253391 on OpenAlexaff
Samantha Jones-Jackson, Romina Rodriguez, Ali Emadi

Bibliographic record

VenueIEEE Transactions on Power Electronics · 2021
Typearticle
Languageen
FieldEngineering
TopicHeat Transfer Mechanisms
Canadian institutionsMcMaster University
Fundersnot available
KeywordsPower electronicsHeat sinkElectronicsElectronics coolingMechanical engineeringReliability (semiconductor)Computer coolingJet (fluid)Automotive engineeringAutomotive industryHeat transferElectrical engineeringEngineeringPower (physics)Thermal management of electronic devices and systemsAerospace engineeringMechanicsPhysicsVoltage

Abstract

fetched live from OpenAlex

Effective thermal management of power electronics in electric vehicles is essential for reliability and increased power density. Currently, traditional cooling technologies such as cold plates and heat sinks have been utilized by the automotive industry. As the next generation of power electronics implements wide-bandgap devices, however, increased heat fluxes will require more advanced cooling strategies. Recently, jet impingement has gained attention as an advanced cooling technique for power electronics due to its proven thermal performance in high-heat-flux applications. This article aims to review the state-of-the-art jet impingement designs applied for power electronics cooling, as well as review future jet impingement technology. Important factors for widespread implementation such as heat transfer, pressure drop, and reliability are discussed, along with the current technical gaps and challenges for jet impingement research in electrified transportation.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0020.003
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.242
Teacher spread0.234 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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

Citations116
Published2021
Admission routes1
Has abstractyes

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