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Record W4384519405 · doi:10.1109/tte.2023.3296284

Multilevel Inverters for Electric Aircraft Applications: Current Status and Future Trends

2023· article· en· W4384519405 on OpenAlexaff
Di Wang, Samuel Hemming, Yuhang Yang, Amirreza Poorfakhraei, Linke Zhou, Chang Liu, Ali Emadi

Bibliographic record

VenueIEEE Transactions on Transportation Electrification · 2023
Typearticle
Languageen
FieldEngineering
TopicMultilevel Inverters and Converters
Canadian institutionsMcMaster University
Fundersnot available
KeywordsAviationElectrificationReliability (semiconductor)EngineeringVoltagePower (physics)Automotive engineeringElectrical engineeringAerospace engineering

Abstract

fetched live from OpenAlex

Aviation electrification has been in the spotlight over the last decade, expected to lead to a more sustainable future. As the power demand and the voltage level of onboard electric power systems dramatically increase, multilevel inverters have attracted the attention of the aviation industry for their superior performance. This paper reviews multilevel inverters for electric aircraft applications. The functions of multilevel inverters in different subsystems of electric aircraft are summarized. The advantages of multilevel inverters compared with their two-level counterparts in electric aircraft, in terms of efficiency, power density, reliability, costs, and power quality, are evaluated through literature review and case studies. Technical innovations with respect to the design, control, modulation, and prototyping of multilevel inverters for aviation applications are also introduced. Finally, future trends of multilevel inverters for electric aircraft applications are discussed.

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 score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.016
GPT teacher head0.247
Teacher spread0.230 · 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

Citations53
Published2023
Admission routes1
Has abstractyes

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