MétaCan
Menu
Back to cohort
Record W4404062064 · doi:10.1063/5.0219982

Permanent magnets with embedded phase changing material for electric motor thermal management

2024· article· en· W4404062064 on OpenAlexafffund
Jean-Michel Lamarre, Maged Ibrahim, Roger Pelletier, Hossein Vatandoost, Fabrice Bernier

Bibliographic record

VenueJournal of Applied Physics · 2024
Typearticle
Languageen
FieldEngineering
TopicElectric Motor Design and Analysis
Canadian institutionsNational Research Council Canada
FundersOffice of Energy Research and Development
KeywordsMagnetThermal management of electronic devices and systemsElectric motorMaterials sciencePhase (matter)ThermalMechanical engineeringPhysicsEngineeringThermodynamics

Abstract

fetched live from OpenAlex

The magnetic performance of NdFeB permanent magnets rapidly decreases as their operation temperature increases. This limits the power output of electric motors as their internal temperature quickly increases with the power demand. This is particularly problematic for applications where high peak power is required for a short period of time, for example during automobile highway acceleration or during an airplane lift-off. With the advances in additive manufacturing, one can envision to fabricate more complex motor geometries and magnetic structures, without additional costs, allowing for enhanced functionalities such as better thermal management. In this context, this paper investigates the feasibility of using phase changing materials (PCMs) to mitigate the temperature rise in permanent magnets (PMs) fabricated by additive manufacturing. The potential of PCM and its relevance was validated by modeling the thermal response of an electric motor during a representative electric vehicle driving scenario. It was found that segmented magnets with embedded phase changing materials would allow to efficiently control temperature rise. To validate the simulation results, PM test pieces with and without embedded PCMs were fabricated using cold spray additive manufacturing and tested using a custom laser thermal cycling setup.

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 score0.785
Threshold uncertainty score0.506

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 head0.006
GPT teacher head0.214
Teacher spread0.208 · 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

Citations3
Published2024
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Applied PhysicsSame topicElectric Motor Design and AnalysisFrench-language works237,207