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Numerical Investigation of Different Jet Impingement Configurations for Thermally Unbalanced Power Modules in Aerospace Traction Inverters

2024· article· en· W4400945730 on OpenAlexaff
Mohamed Hefny, Sam Hemming, Linke Zhou, A.K. Allam, Di Wang, Giorgio Pietrini, Piranavan Suntharalingam, Mikhail Goykhman, Ali Emadi

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicVibration and Dynamic Analysis
Canadian institutionsMcMaster University
Fundersnot available
KeywordsAerospaceTraction (geology)Jet (fluid)Power (physics)Aerospace engineeringMechanical engineeringComputer scienceAutomotive engineeringEngineeringPhysics

Abstract

fetched live from OpenAlex

Thermal management of power electronic inverters becomes a critical necessity for high density aerospace applications. Proper thermal management techniques can push the rated power of such power inverters. Jet impingement is one of the optimistic techniques that possesses superior heat transfer characteristics, which can be an advanced assist for power inverters cooling innovation. This paper proposes different jet impingement configurations to provide a thermal management solution for thermally unbalanced power modules used in 1 MVA, 3-level ANPC inverter, by performing computational fluid dynamics simulations to analyze the thermal and hydraulic characteristics. The optimal design target is to decrease the temperature difference between the unbalanced switches to enhance the overall reliability and lifetime of the power module while simultaneously reducing the pressure drop. With the optimized design among all the proposals, the minimum temperature difference is 24 °C between the power module switches, while the pressure drop reached 12 kPa for the optimal jet configuration.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.726
Threshold uncertainty score0.272

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 head0.010
GPT teacher head0.226
Teacher spread0.216 · 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

Citations2
Published2024
Admission routes1
Has abstractyes

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