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Record W2158926857 · doi:10.1109/apec.2011.5744735

Thermal management for multi-phase current mode buck converters

2011· article· en· W2158926857 on OpenAlexafffund
Pearl Cao, Wai Tung Ng, Olivier Trescases

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced DC-DC Converters
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsConvertersBuck converterPhase (matter)Three-phasePower (physics)Electronic engineeringElectrical engineeringCurrent (fluid)ThermalComputer scienceControl theory (sociology)EngineeringVoltagePhysics

Abstract

fetched live from OpenAlex

This paper deals with thermal management and power sharing in multi-phase buck converters. In conventional designs, each phase is regulated to share the load current equally. However, due to variations in PCB layout, parasitic resistances, airflow, and transistor on-resistance (R <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">on</sub> ), it is common to observe significant temperature variations between the converter phases, leading to reliability issues and suboptimal device utilization. Several past research efforts have demonstrated closed-loop analog and digital techniques to achieve a uniform temperature in all phases. In this work, a thermal management unit (TMU) with four independent linear controllers is proposed to regulate the phase temperatures in a four-phase mixed-signal peak-current mode converter. The digital TMU is designed based on a lumped thermal model of the system and dynamically adjusts the peak current in each phase to match the moving average temperature. Experimental results from a digitally controlled 12 V to 1 V, 50 A, 250 kHz four-phase peak current mode buck converter demonstrate the effectiveness of the proposed thermal management technique in the presence of uneven air flow and dynamic load currents.

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: Methods · Consensus signal: none
Teacher disagreement score0.993
Threshold uncertainty score0.635

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 head0.058
GPT teacher head0.303
Teacher spread0.244 · 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
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

Citations8
Published2011
Admission routes2
Has abstractyes

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