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Low Resistance Heat Paths Application to Electric Machines Rotor Cooling

2024· article· en· W4400945776 on OpenAlexafffund
Islam Zaher, Maaz Khalid, Mohamed Abdalmagid, Giorgio Pietrini, Mikhail Goykhman, Ali Emadi

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicTribology and Lubrication Engineering
Canadian institutionsMcMaster University
FundersMitacs
KeywordsRotor (electric)Computer scienceThermal resistanceAutomotive engineeringMaterials scienceElectrical engineeringMechanical engineeringMechanicsEngineeringPhysicsHeat transfer

Abstract

fetched live from OpenAlex

Thermal non-uniformities in electric machines manifested as local hotspots and high temperature gradients, pose significant risks to machine safety, leading to heightened thermal stresses and an increased likelihood of component failures. In this research, a newly developed 150 kW high-speed machine exhibited significant thermal imbalances, leading to hot spots near bearing seats. To mitigate these challenges, the paper proposes the use of integrated low-resistance heat paths in the steel rotor, replacing traditional void entities with lightweight, highly conductive materials such as aluminum or copper. Computational Fluid Dynamics (CFD) simulations demonstrate the effectiveness of an aluminum insert, resulting in a significant 47°C reduction in maximum shaft temperature and improved thermal uniformity. Structural analysis guides the optimization of fit parameters, defining a transition fit for maximum stress within yield strength. This comprehensive approach offers a strategic solution for enhancing rotor thermal management in high-speed 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 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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.968
Threshold uncertainty score0.403

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.003
GPT teacher head0.202
Teacher spread0.199 · 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 designSimulation or modeling
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

Citations0
Published2024
Admission routes2
Has abstractyes

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