MétaCan
Menu
Back to cohort
Record W2990427069 · doi:10.1109/ecce.2019.8912257

A High Power Density Thermal Management Approach Using Multi-PCB Distributed Cooling (MPDC) Structure

2019· article· en· W2990427069 on OpenAlexaff
Wenbo Liu, Andrew Yurek, Yang Chen, Bo Sheng, Xiang Zhou, Yan‐Fei Liu

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicSilicon Carbide Semiconductor Technologies
Canadian institutionsQueen's University
Fundersnot available
KeywordsComputer coolingPower densityThermalPrinted circuit boardPower (physics)Mechanical engineeringWater coolingActive coolingFinite element methodMaterials scienceThermal management of electronic devices and systemsThermal analysisPassive coolingAir coolingElectronic engineeringNuclear engineeringEngineeringElectrical engineeringStructural engineeringThermodynamicsPhysics

Abstract

fetched live from OpenAlex

This paper presents a new thermomechanical PCB design that uses a multi-PCB cooling (MPDC) structure to achieve higher power density while maintaining thermal performance. The new MPDC structure focusses on three design principles: multiple vertically stacked PCB's for more efficient use of space, component sorting based on losses for improved cooling, and integrated liquid cooling for maximum thermal dissipation. Liquid cooling method is utilized to implement the thermal management problem with very high power density. Finite element analysis (FEA) based thermal analysis was conducted on the 1.3kW converter with two-PCB integrated liquid cooling model. Same thermal estimation as single PCB structure was verified. An experimental prototype with one PCB and cooling setup with liquid and air cooling was built. A 1.3 kW LLC power converter was developed, 50% less temperature rise on critical devices and 0.6% better efficiency are achieved. Thereafter, the proposed MPDC structure was investigated based the single PCB design. The two-layer MPDC prototype repeats the same efficiency and thermal performance while achieving 31% improvement in power density.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.497
Threshold uncertainty score0.967

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 head0.013
GPT teacher head0.205
Teacher spread0.192 · 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 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

Citations4
Published2019
Admission routes1
Has abstractyes

Explore more

Same topicSilicon Carbide Semiconductor TechnologiesFrench-language works237,207