{"id":"W2519702094","doi":"10.1109/tia.2016.2558162","title":"A Novel Grain-Oriented Lamination Rotor Core Assembly for a Synchronous Reluctance Traction Motor With a Reduced Torque Ripple Algorithm","year":2016,"lang":"en","type":"article","venue":"IEEE Transactions on Industry Applications","topic":"Electric Motor Design and Analysis","field":"Engineering","cited_by":89,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada; Hydro-Québec; Concordia University","keywords":"Torque ripple; Torque density; Direct torque control; Torque; Rotor (electric); Switched reluctance motor; Cogging torque; Reluctance motor; Stall torque; Traction motor; Magnetic reluctance; Synchronous motor; Engineering; Automotive engineering; Mechanical engineering; Computer science; Materials science; Control theory (sociology); Magnet; Electrical engineering; Physics; Induction motor","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001184064,0.0002668829,0.0002293672,0.0002612229,0.0003101002,0.00003283555,0.0001574266,0.0003731356,0.00005347998],"category_scores_gemma":[0.000006930861,0.000225175,0.000128359,0.0008319474,0.00005416992,0.0002529882,4.769064e-7,0.0004142364,0.00002218519],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004589986,"about_ca_system_score_gemma":0.00008838339,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002601414,"about_ca_topic_score_gemma":0.00003438949,"domain_scores_codex":[0.9986659,0.00001616508,0.000332748,0.0004200041,0.0002277411,0.0003374102],"domain_scores_gemma":[0.9989563,0.0001726,0.0001015161,0.0004206906,0.0001995445,0.000149309],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00006716988,0.0004514614,0.000003984189,0.00004042515,0.0002381801,9.941951e-7,0.00007438203,0.003367902,0.6202708,0.0003219055,0.0004850551,0.3746777],"study_design_scores_gemma":[0.005890741,0.001166323,0.0004495647,0.0003804775,0.0008589597,0.0001630188,0.0002060028,0.4080724,0.5434601,0.0003099721,0.03726679,0.001775639],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01013685,0.00001791303,0.9865968,0.0002719809,0.0001406686,0.001712094,0.0004857043,0.0004930833,0.0001449261],"genre_scores_gemma":[0.9204969,0.0000529575,0.06049956,0.00004903441,0.0003173678,0.01516292,0.00004633467,0.0001202066,0.003254715],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9260972,"threshold_uncertainty_score":0.918237,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01367445519275742,"score_gpt":0.2367074243861069,"score_spread":0.2230329691933495,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}