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Record W4225162688 · doi:10.3390/en15093249

Advances in Thermal Management Technologies of Electrical Machines

2022· article· en· W4225162688 on OpenAlexaff
Qingsong Wang, Yu Wu, Shuangxia Niu, Xing Zhao

Bibliographic record

VenueEnergies · 2022
Typearticle
Languageen
FieldEngineering
TopicElectric Motor Design and Analysis
Canadian institutionsÉcole de Technologie Supérieure
Fundersnot available
KeywordsThermal management of electronic devices and systemsWater coolingArmature (electrical engineering)Mechanical engineeringThermalTorqueAutomotive engineeringComputer scienceElectrical engineeringEngineeringElectromagnetic coilPhysics

Abstract

fetched live from OpenAlex

Given the fact that the operation of electrical machines generates various loss components that finally become heat, developing advanced thermal management technologies is essential to control temperature increases and to guarantee safe operations. Meanwhile, the armature winding can stand larger currents when the machines are equipped with advanced cooling systems, which directly improves torque/power densities. This paper aims to provide a systematic review of the latest developments of advanced thermal management technologies of electrical machines. According to different heat dissipation mechanisms, the cooling systems studied in this paper are categorized into five major types: enclosed housing cooling, enhanced conductive cooling, embedded heat pipe cooling, direct oil cooling, and enhanced rotor cooling. The advantages and disadvantages of these cooling systems are researched and compared comprehensively. This study contributes to the revelation of insights on the thermal management of electrical machines and offers good guidance for the thermal management of electrical machines.

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)0.001
metaresearch head score (Gemma)0.002
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: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.003
GPT teacher head0.188
Teacher spread0.185 · 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
GenreReview

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

Citations22
Published2022
Admission routes1
Has abstractyes

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