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Mechanical Design Considerations of a Light-Weight Rotor for Brushless Double Fed Machines

2024· article· en· W4403279694 on OpenAlexaff
Malihe Heidary, Salman Abdi, Hamidreza Mosaddegh Hesar, Xiaodong Liang, Ehsan Abdi, Richard McMahon

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicHydraulic and Pneumatic Systems
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsRotor (electric)DC motorComputer scienceAutomotive engineeringControl theory (sociology)Mechanical engineeringEngineeringElectrical engineeringArtificial intelligence

Abstract

fetched live from OpenAlex

This paper presents an optimised mechanical design aimed at reducing the rotor mass of a Brushless Doubly-Fed Machine (BDFM). The focus is on the rotor back iron, identifying specific areas where iron materials can be removed without compromising the magnetic performance of the rotor. Mechanical stress and vibration analyses are conducted using the Finite Element (FE) method in Ansys software to evaluate the feasibility of the proposed design. The study is performed on a prototype D400 250-kW BDFM, showing that the rotor mass can be reduced by approximately 24% while maintaining acceptable electromagnetic and mechanical performance under rated operating conditions.

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: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.983
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
Bibliometrics0.0000.000
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 head0.036
GPT teacher head0.262
Teacher spread0.225 · 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
GenreMethods

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 routes1
Has abstractyes

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