{"id":"W4399723978","doi":"10.1615/tfec2024.nmf.049908","title":"COMPUTATIONAL ANALYSIS OF A COMBINED NOVEL PIN-FIN HEAT SINK AND GRAPHENE NANOPLATELETS BASED NANOFLUID FOR COOLING ENHANCEMENT OF IGBT MODULES IN ELECTRIC VEHICLES","year":2024,"lang":"en","type":"article","venue":"","topic":"Heat Transfer and Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Toronto Metropolitan University; University of Calgary","funders":"","keywords":"Nanofluid; Heat sink; Materials science; Fin; Insulated-gate bipolar transistor; Graphene; Thermal management of electronic devices and systems; Exfoliated graphite nano-platelets; Optoelectronics; Composite material; Mechanical engineering; Nanotechnology; Electrical engineering; Nanoparticle; Engineering; 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":[0.0001249969,0.00009846803,0.0002583444,0.0006290007,0.0000155832,0.00001354358,0.00003528868,0.00004645973,0.00001555243],"category_scores_gemma":[0.000007081976,0.00009681677,0.00007336616,0.001075357,0.00001403676,0.00005774714,0.00000310287,0.00003531511,1.407328e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002597863,"about_ca_system_score_gemma":0.00002402811,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004716846,"about_ca_topic_score_gemma":0.00004132208,"domain_scores_codex":[0.9993097,0.000008945683,0.0003321848,0.0001200936,0.0001056357,0.0001235102],"domain_scores_gemma":[0.9996606,0.0001964699,0.000005868183,0.00005489491,0.0000559094,0.00002629161],"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":[0.00003410227,0.0000485102,0.0001587716,0.0002266455,0.0002134106,2.970422e-7,0.0001317386,0.7213799,0.2760598,0.0009815181,0.000007483807,0.0007577942],"study_design_scores_gemma":[0.0005203746,0.00006357765,0.001464478,0.00006046909,0.000110949,1.736302e-7,0.000008639241,0.7667484,0.2308713,0.00007580286,0.000003883098,0.00007195897],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4839638,0.0004522576,0.5153657,0.00002098711,0.00001713675,0.0001146291,0.00002166557,0.00002732783,0.0000165036],"genre_scores_gemma":[0.9871972,0.00008955976,0.01253471,0.00001632396,0.00000368234,0.00001991174,0.0001223884,0.00001329941,0.000002962203],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5032333,"threshold_uncertainty_score":0.3948073,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00951970850011219,"score_gpt":0.223102570598873,"score_spread":0.2135828620987608,"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."}}