{"id":"W4387307465","doi":"10.1002/cjce.25108","title":"Heat transfer enhancement of serpentine channels with twisted tape insert by computational fluid dynamics and artificial intelligence","year":2023,"lang":"en","type":"article","venue":"The Canadian Journal of Chemical Engineering","topic":"Heat Transfer Mechanisms","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computational fluid dynamics; Nusselt number; Reynolds number; Heat transfer; Artificial neural network; Insert (composites); Heat exchanger; Mechanics; Mechanical engineering; Materials science; Computer science; Artificial intelligence; Thermodynamics; Physics; Engineering; Turbulence","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001988529,0.0001414374,0.0002139509,0.0001440876,0.0000288078,0.00002744273,0.0001605889,0.00005854423,0.00002868555],"category_scores_gemma":[0.00001525384,0.0001174565,0.00004037573,0.0003269763,0.00004999062,0.00006883257,0.00000457847,0.0002406248,0.00000190512],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001298498,"about_ca_system_score_gemma":0.00009309451,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001189164,"about_ca_topic_score_gemma":0.0001599235,"domain_scores_codex":[0.9990925,0.000007516776,0.0003544639,0.00007776015,0.0001976256,0.0002701046],"domain_scores_gemma":[0.9995149,0.00006419673,0.000005414917,0.00007589214,0.00007291626,0.0002666683],"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":[0.00001510448,0.000005619273,0.000004046218,0.0001039607,0.00008419372,0.00002074656,0.0005704802,0.5264359,0.470127,0.0015151,0.00004030203,0.001077639],"study_design_scores_gemma":[0.00008234509,0.00003933934,0.000006161291,0.0000891135,0.00001633552,0.00004230266,0.00002267471,0.518241,0.4811929,0.0001513112,0.00002417461,0.00009230436],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7989691,0.0001135577,0.2002497,0.0003580735,0.0001631814,0.00008053936,0.00002380809,0.00002592998,0.00001614441],"genre_scores_gemma":[0.9994453,0.000008138488,0.0004273613,0.0000193693,0.00004536465,0.000003546592,0.00001580794,0.00003176277,0.000003329319],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2004762,"threshold_uncertainty_score":0.4789735,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01039381653527706,"score_gpt":0.1885511641167673,"score_spread":0.1781573475814902,"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."}}