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Record W2966050223 · doi:10.1109/isie.2019.8781365

Performance Evaluation of LLC-SRC and LCC-T Resonant Tanks in Low-Voltage High-Current applications

2019· article· en· W2966050223 on OpenAlexaff
Venkata R. Vakacharla, Akshay Kumar Rathore

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced DC-DC Converters
Canadian institutionsConcordia University
Fundersnot available
KeywordsConvertersCapacitorResonant converterElectrical engineeringVoltageCurrent (fluid)Ćuk converterEngineeringForward converterElectronic engineeringGridMaterials sciencePhysicsBoost converterMathematics

Abstract

fetched live from OpenAlex

Current-fed, isolated, dc/dc converters with high gain generating capability are needed for integration of fuel cell/ PV panel to DC micro grid. This manuscript proposes LLC-SRC resonant tank assisted soft-switched current-fed isolated DC-DC converter followed by its performance evaluation and comparison with LCC-T resonant converter counterpart. A LCC-T tank assisted resonant converter offers compact size as compared to quasi resonant converters. But this converter compromises efficiency at lighter loads due to longer charging times of parallel resonant capacitor. To eliminate this drawback, LLC-SRC resonant tank assisted soft-switched current-fed isolated dc/dc converter is proposed, designed and simulated. Proposed theory is verified with simulation 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 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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.970
Threshold uncertainty score0.328

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 head0.009
GPT teacher head0.236
Teacher spread0.227 · 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 designOther design
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

Citations7
Published2019
Admission routes1
Has abstractyes

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