{"id":"W2394839694","doi":"10.1139/tcsme-2011-0005","title":"MODELING AND MULTI-OBJECTIVE OPTIMIZATION OF FORWARD-CURVED BLADE CENTRIFUGAL FANS USING CFD AND NEURAL NETWORKS","year":2011,"lang":"en","type":"article","venue":"Transactions of the Canadian Society for Mechanical Engineering","topic":"Turbomachinery Performance and Optimization","field":"Engineering","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Blade (archaeology); Artificial neural network; Computational fluid dynamics; Head (geology); Genetic algorithm; Multi-objective optimization; Set (abstract data type); Structural engineering; Computer science; Engineering; Mathematics; Mathematical optimization; Artificial intelligence; Geology; Aerospace engineering","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":[0.00008498869,0.000119946,0.0001538299,0.00004881344,0.000120996,0.000009286965,0.0000736924,0.0001264027,0.00000461303],"category_scores_gemma":[0.000007075912,0.0001151881,0.0001393961,0.0001550031,0.00002340639,0.0001671645,0.000004572221,0.0001518132,1.017844e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009018066,"about_ca_system_score_gemma":0.00002480026,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002058559,"about_ca_topic_score_gemma":0.002756078,"domain_scores_codex":[0.999435,0.00000476298,0.0001976127,0.0001026906,0.00005616136,0.0002037691],"domain_scores_gemma":[0.9997156,0.00001647878,0.00002416034,0.00009727577,0.0000425525,0.0001039944],"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":[0.000004523189,0.000004693659,0.00001391184,0.000065869,0.00007080221,3.697171e-8,0.0004565427,0.9984496,0.0005774572,0.00004969673,5.619943e-7,0.0003062853],"study_design_scores_gemma":[0.0003440804,0.00001856793,0.00002776467,0.00003106389,0.00009464489,0.000003455905,0.00009186481,0.9975217,0.001746852,0.000007389936,0.000001203707,0.0001114219],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05377139,0.0001531867,0.9455724,0.000007783295,0.0001748894,0.0002403598,0.00003905076,0.00003746612,0.00000342687],"genre_scores_gemma":[0.8978368,0.00007282552,0.1020228,0.00000859471,0.00001563969,0.000009398684,0.000003917761,0.00002782114,0.000002240206],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8440654,"threshold_uncertainty_score":0.4697234,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01660283465404112,"score_gpt":0.1924734471139746,"score_spread":0.1758706124599335,"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."}}