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Record W2559843301 · doi:10.1109/epec.2016.7771734

Switching frequency selection for aerospace power converter system considering the design of output LC filter inductor optimizing weight and power loss

2016· article· en· W2559843301 on OpenAlexaff
Tharmini Thavaratnam, Chushan Li, Dewei Xu

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced DC-DC Converters
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsInductorAerospaceHeat sinkPower (physics)Electronic engineeringSwitching frequencyFilter (signal processing)Buck converterComputer scienceElectrical engineeringEngineeringPhysics

Abstract

fetched live from OpenAlex

Switching frequency is one of the main deciding factors in development of high power converter systems used in industrial applications. This paper aims at verifying the switching frequencies range between 9kHz and 20kHz by selecting and designing the inductor of the output LC filter for each switching frequency within that range while optimizing the weight and power loss of the overall power converter system used in aerospace industry. In this paper, the power loss of the inverter semiconductors is also studied for each switching frequency by considering the weight of the heat sink needed to cool the system using air as the coolant. An example design of 50kW, 540VDC, 0.8pf motor drive, 3 phase 2 level 400Hz system is used to analyze the results obtained using the proposed methods. The optimization of the overall power converter system weight is finalized by a trade-off between the heat sinking and filtering requirements.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.014
GPT teacher head0.203
Teacher spread0.189 · 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 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

Citations5
Published2016
Admission routes1
Has abstractyes

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