MétaCan
Menu
Back to cohort

Multi-physics Design Platform for a High Power Density Multi-phase IPM Traction Motor: Analysis and Simulation

2022· article· en· W4284884034 on OpenAlexaff
Ahmed Abdelrahman, Ashish Kumar Sahu, Nathan Emery, Dhafar Al-Ani, Berker Bilgin

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicElectric Motor Design and Analysis
Canadian institutionsMcMaster University
Fundersnot available
KeywordsNoise, vibration, and harshnessPower densityMagnetTraction (geology)Computational fluid dynamicsComputer scienceElectromagnetic coilTraction motorElectromagneticsVibrationMechanical engineeringPower (physics)Automotive engineeringPhysicsElectronic engineeringEngineeringElectrical engineeringAerospace engineeringAcoustics

Abstract

fetched live from OpenAlex

Design of a high-speed high-power density interior permanent magnet (IPM) motor can be challenging since it is constrained by various multi-physics couplings. Therefore, feasible solution that meets different multi-domain specifications is hardly attained through individual domain calculations only. This paper presents a parameterized two-way multi-physics couplings platform for an IPM motor with hairpin windings. It incorporates electromagnetic (EM) analysis, stress and fatigue analyses, thermal analysis with computational fluid dynamics (CFD), and noise-vibration-harshness (NVH) analysis. The temperature gradient is implemented for all couplings by using temperature/yield strength-dependent material properties. A case study for a 300 kW IPM motor with a maximum speed of 17,000 rpm is developed and its performance is optimized to meet different design requirements.

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 score0.721
Threshold uncertainty score0.725

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 head0.033
GPT teacher head0.270
Teacher spread0.238 · 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

Citations4
Published2022
Admission routes1
Has abstractyes

Explore more

Same topicElectric Motor Design and AnalysisFrench-language works237,207