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Record W3209975534 · doi:10.32920/ryerson.14645109.v1

Optimal switching frequency for aerospace power converter systems

2021· preprint· en· W3209975534 on OpenAlexaff
Tharmini Thavaratnam

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicAdvanced DC-DC Converters
Canadian institutionsToronto Metropolitan UniversityUniversity of Waterloo
Fundersnot available
KeywordsAerospaceSwitching frequencyInsulated-gate bipolar transistorInductorFilter (signal processing)Power (physics)Electronic engineeringCapacitorEngineeringComputer scienceElectrical engineeringVoltage

Abstract

fetched live from OpenAlex

Switching frequency is one of the main deciding factors in development of a power converter system for aerospace applications. The filter is one of the major components that significantly contribute to the overall system weight and efficiency. The design of the filter, including inductor and capacitor is determined by the switching frequency. On the other hand, the device switching loss is proportional to the loss, which in turn changes the design of thermal system. Thus selection of optimal switching frequency is essential for power converter systems used in aerospace industry. The objective of this study is to select the optimal switching frequency when the overall converter weight is minimal. In this study, switching frequencies ranging from 9kHz to 20kHz is selected based on current IGBT technology. Proven design algorithm for passive filter is adopted in the optimization. Partial verification for power efficiency is conducted via simulation and experiment results.

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)0.001
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: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
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 head0.011
GPT teacher head0.228
Teacher spread0.217 · 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
GenreMethods

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

Citations0
Published2021
Admission routes1
Has abstractyes

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