{"id":"W4210346844","doi":"10.1109/tte.2022.3147976","title":"Overview of Current Thermal Management of Automotive Power Electronics for Traction Purposes and Future Directions","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Transportation Electrification","topic":"Silicon Carbide Semiconductor Technologies","field":"Engineering","cited_by":99,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Thermal management of electronic devices and systems; Electronics; Power electronics; Automotive engineering; Water cooling; Heat sink; Reliability (semiconductor); Passive cooling; Automotive industry; Traction (geology); Active cooling; Power density; Electronic component; Computer cooling; Power module; Mechanical engineering; Capacitor; Electrical engineering; Power (physics); Thermal; Engineering; Aerospace engineering; Voltage; Physics","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":[0.0001240169,0.0001481634,0.0001829447,0.0002920314,0.0001334791,0.000005303946,0.00009907413,0.00006882286,0.00004531289],"category_scores_gemma":[9.106257e-7,0.0001747468,0.0001116836,0.0005634568,0.0000381279,0.0001480127,4.339124e-8,0.000276045,3.643767e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001459682,"about_ca_system_score_gemma":0.00002516433,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003558139,"about_ca_topic_score_gemma":0.00001019899,"domain_scores_codex":[0.9990277,0.00002883419,0.0003793715,0.0002084863,0.0001971454,0.0001584633],"domain_scores_gemma":[0.9995046,0.00005295893,0.0001308372,0.0001869551,0.0001023214,0.00002235938],"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":[0.0001133633,0.0002878766,0.00001454261,0.000377831,0.0002228698,2.27119e-7,0.0009435329,0.02540061,0.727918,0.003483562,0.00002535171,0.2412122],"study_design_scores_gemma":[0.0006195507,0.000316733,0.004893951,0.00002641726,0.000224589,0.000002244814,0.001011666,0.001887666,0.9869875,0.0003037986,0.003528808,0.0001970354],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9004276,0.009166714,0.0874083,0.0001161706,0.0007820555,0.001194701,0.0004021105,0.0004485121,0.00005386637],"genre_scores_gemma":[0.9906879,0.008366055,0.0002079206,0.000005198266,0.00001096054,0.0006182589,0.00005957478,0.000030893,0.00001322012],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2590695,"threshold_uncertainty_score":0.7125967,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02182956339981318,"score_gpt":0.2645034760570021,"score_spread":0.242673912657189,"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."}}