{"id":"W2804874262","doi":"10.1063/1.5026862","title":"Topology optimization of reduced rare-earth permanent magnet arrays with finite coercivity","year":2018,"lang":"en","type":"article","venue":"Journal of Applied Physics","topic":"Electric Motor Design and Analysis","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"Natural Sciences and Engineering Research Council of Canada; BASF; U.S. Department of Energy","keywords":"Magnet; Neodymium magnet; Demagnetizing field; Halbach array; Coercivity; Materials science; Mechanical engineering; Electropermanent magnet; Topology optimization; Topology (electrical circuits); Finite element method; Electrical engineering; Condensed matter physics; Permanent magnet synchronous generator; Physics; Engineering; Structural engineering; Magnetic field","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002766568,0.0003674133,0.0003674627,0.0003115266,0.0001704123,0.0005640574,0.0003520513,0.000466844,0.001996447],"category_scores_gemma":[0.000742579,0.0002548334,0.0002791343,0.0001802697,0.0003252646,0.0004215717,0.0002611898,0.0002305586,0.0002546277],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000452851,"about_ca_system_score_gemma":0.0003253645,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005948869,"about_ca_topic_score_gemma":0.001066354,"domain_scores_codex":[0.9999206,0.00002400381,0.000002604187,0.00001409708,0.00002377462,0.00001489307],"domain_scores_gemma":[0.9997316,0.0001348767,0.00005583692,0.00001743051,0.00004112534,0.00001905137],"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":[0.00007192349,0.00002996659,0.0003214187,0.00007233587,0.00001269526,0.0001067162,0.00003045153,0.9723336,0.01066724,0.005702401,0.0004258068,0.01022545],"study_design_scores_gemma":[0.00001475563,0.00009033966,0.0001596795,0.000004581167,0.000005917722,0.0000332911,0.00002148675,0.9949474,0.002265185,0.00174314,0.0007101388,0.000004038277],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5677811,0.0007726922,0.3916643,0.0005563437,0.00007242649,0.00008248034,0.0001666729,0.0004308494,0.03847309],"genre_scores_gemma":[0.946175,0.00013123,0.04984274,0.00003313537,0.000009210939,0.00005520526,0.00005977988,0.00005302511,0.003640637],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001996447,"threshold_uncertainty_score":0.00667882,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008201320444738579,"score_gpt":0.2016250735909189,"score_spread":0.1934237531461803,"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."}}