{"id":"W2902317652","doi":"10.23919/icems.2018.8548975","title":"Design Optimization and Performance Prediction of Synchronous Reluctance Motors","year":2018,"lang":"en","type":"article","venue":"","topic":"Electric Motor Design and Analysis","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"","keywords":"Magnetic reluctance; Torque ripple; Torque; Rotor (electric); Reluctance motor; Switched reluctance motor; Control theory (sociology); Torque density; Power (physics); Direct torque control; Finite element method; Computer science; Engineering; Automotive engineering; Mechanical engineering; Magnet; Induction motor; Structural engineering; Electrical engineering; Physics; Voltage","routes":{"ca_aff":true,"ca_fund":false,"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":[0.00007252015,0.00005409187,0.0000738388,0.0000631709,0.00002896953,0.000006791852,0.00003161473,0.00003343994,0.00008106805],"category_scores_gemma":[0.000005225798,0.00004990363,0.00001025497,0.0001859092,0.00002600848,0.0001108576,0.000003211742,0.00002758344,0.0000030703],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001891261,"about_ca_system_score_gemma":0.000005033638,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003032643,"about_ca_topic_score_gemma":3.21439e-7,"domain_scores_codex":[0.9996729,0.000008328575,0.0001094432,0.00007057594,0.00005966368,0.00007905785],"domain_scores_gemma":[0.9998368,0.00001202776,0.00001642502,0.00007639793,0.00003599446,0.00002238052],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003024774,0.00002816615,0.006245427,0.0001536847,0.0001356207,6.515705e-7,0.0004189905,0.86021,0.04305456,0.0001134838,0.001409173,0.08819997],"study_design_scores_gemma":[0.00007715258,0.0001318101,0.000911548,0.00001015927,0.0000144141,0.000001802272,0.000002176458,0.9762568,0.02252333,0.000007331314,0.00002120525,0.00004226117],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09220695,0.0001423765,0.9057423,0.00000268278,0.00003968123,0.00006463387,5.987186e-7,0.0001100255,0.001690672],"genre_scores_gemma":[0.9311527,0.0004646266,0.06817588,0.000004625422,0.00004593705,0.000004460084,0.000001427996,0.000008639264,0.0001416739],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8389457,"threshold_uncertainty_score":0.2035011,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007731929907218093,"score_gpt":0.1734149927460682,"score_spread":0.1656830628388501,"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."}}