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Record W3120444275 · doi:10.23919/cjee.2020.000026

A novel intermediate bus converter topology for cutting edge data center applications

2020· article· en· W3120444275 on OpenAlexaff
Samuel Webb, Yan‐Fei Liu

Bibliographic record

VenueChinese Journal of Electrical Engineering · 2020
Typearticle
Languageen
FieldEngineering
TopicAdvanced DC-DC Converters
Canadian institutionsQueen's University
Fundersnot available
KeywordsConvertersCapacitorData centerNetwork topologyPower (physics)Topology (electrical circuits)Center (category theory)Electrical engineeringPower densityEnhanced Data Rates for GSM EvolutionReduction (mathematics)Key (lock)Computer scienceVoltageElectronic engineeringEngineeringPhysicsOperating systemTelecommunicationsMathematics

Abstract

fetched live from OpenAlex

As new technologies emerge data centers and servers have established themselves as one of the largest and fastest growing consumers of power. While switched capacitor converter topologies have some very attractive features, namely low reliance on magnetic components and high efficiency, several critical factors have prevented their adoption in high current data center applications. The family of converters proposed are novel intermediate bus converter that demonstrates the highest performance yet achieved for 48 V to 12 V conversion with up to 2.5 kW/in3power density, higher than 99% peak efficiency, and 97.2% full load efficiency for 12 V/70 A output. The reduction of voltage stress across the MOSFETs as well as extremely low reliance on magnetics are the key driving factors behind this high efficiency and power density, and are achieved without a sensitive resonant design or the usage of complex control technique.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.007
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0070.002

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 head0.253
Teacher spread0.237 · 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 designBench or experimental
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

Citations3
Published2020
Admission routes1
Has abstractyes

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