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Record W2791092744 · doi:10.1109/tec.2018.2807618

Multimodal Design Optimization of V-Shaped Magnet IPM Synchronous Machines

2018· article· en· W2791092744 on OpenAlex
Buddhika De Silva Guruwatta Vidanalage, Mohammad Sedigh Toulabi, Shaahin Filizadeh

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIEEE Transactions on Energy Conversion · 2018
Typearticle
Languageen
FieldEngineering
TopicElectric Motor Design and Analysis
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsMaxima and minimaMagnetControl theory (sociology)Optimal designComputer scienceSynchronous motorGlobal optimizationControl engineeringAlgorithmEngineeringMathematicsMechanical engineeringElectrical engineeringArtificial intelligence

Abstract

fetched live from OpenAlex

An algorithm is developed for multimodal design optimization of a V-shaped magnet interior permanent magnet synchronous machine (IPMSM). This algorithm is capable of finding all the local and global minima of a given single- or multi-objective function. The paper demonstrates the algorithm to minimize the total active weight of an IPMSM while minimizing its losses considering mechanical, thermal, and magnetic constraints. Predicted operating characteristics of a preliminary, nonoptimized IPMSM are compared with the global and local optimal solutions obtained. The proposed algorithm provides the designer with a complete view of alternative optimal machine designs from which a suitable design may be selected. The close agreement between the simulation and experimental results for the globally optimized IPMSM design verifies the accuracy of the optimization approach.

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.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.976
Threshold uncertainty score1.000

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)0.0010.000

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 head0.008
GPT teacher head0.192
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