{"id":"W2551229631","doi":"10.1007/s00521-016-2683-z","title":"Design and economic optimization of shell-and-tube heat exchanger using cohort intelligence algorithm","year":2016,"lang":"en","type":"article","venue":"Neural Computing and Applications","topic":"Heat Transfer and Optimization","field":"Engineering","cited_by":76,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Baffle; Shell and tube heat exchanger; Robustness (evolution); Heat exchanger; Computer science; Tube (container); Mechanical engineering; Minification; Mathematical optimization; Engineering; Mathematics; Chemistry","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001197551,0.000642526,0.001544401,0.0007399427,0.0007488353,0.001270105,0.001130037,0.001889409,0.003742715],"category_scores_gemma":[0.001718991,0.0007734756,0.001038422,0.0006762185,0.0008425873,0.00082329,0.001083294,0.0009378229,0.0003107832],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001026262,"about_ca_system_score_gemma":0.001665278,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005346457,"about_ca_topic_score_gemma":0.004121319,"domain_scores_codex":[0.9997311,0.000103292,0.0000106575,0.00004750318,0.00006047135,0.00004698362],"domain_scores_gemma":[0.9995065,0.0002986656,0.0000398665,0.00002184173,0.00009454205,0.0000386199],"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":[0.00002615102,0.00002625893,0.0001749458,0.00002514,0.00001057596,0.00002021133,0.000008493661,0.9935256,0.0005088075,0.002349534,0.000210587,0.00311366],"study_design_scores_gemma":[0.000004110401,0.000009859278,0.00003792685,0.000001033921,0.000001739974,0.000001485486,0.000002248959,0.9995559,0.00006938076,0.0002563389,0.00005883195,0.000001177125],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1064516,0.0005748526,0.8733907,0.000498783,0.0001263944,0.0001935326,0.0001206954,0.0002261675,0.01841735],"genre_scores_gemma":[0.9071088,0.0002910626,0.08361651,0.00008656599,0.00004302674,0.0004355421,0.000121815,0.00009084331,0.00820593],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005346457,"threshold_uncertainty_score":0.01252067,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02119377135015751,"score_gpt":0.245045383523189,"score_spread":0.2238516121730315,"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."}}