{"id":"W2571369867","doi":"10.1016/j.cja.2016.12.013","title":"Optimization and design of an aircraft’s morphing wing-tip demonstrator for drag reduction at low speed, Part I – Aerodynamic optimization using genetic, bee colony and gradient descent algorithms","year":2017,"lang":"en","type":"article","venue":"Chinese Journal of Aeronautics","topic":"Aeroelasticity and Vibration Control","field":"Engineering","cited_by":56,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada; École de Technologie Supérieure","funders":"Natural Sciences and Engineering Research Council of Canada; Consortium de Recherche et d’innovation en Aérospatiale au Québec","keywords":"Morphing; Aerodynamics; Wing; Genetic algorithm; Reduction (mathematics); Convergence (economics); Drag; Descent (aeronautics); Wind tunnel; Engineering; Computer science; Aerospace engineering; Control theory (sociology); Mathematical optimization; Mathematics; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0002744939,0.0004383325,0.0003236947,0.0002687238,0.0002360453,0.0003566624,0.0003639004,0.0005783574,0.0006576041],"category_scores_gemma":[0.0003102604,0.0002062885,0.0003051685,0.0001352046,0.000256858,0.000216367,0.0002362872,0.0002751571,0.000142796],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002156474,"about_ca_system_score_gemma":0.0003669529,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009276054,"about_ca_topic_score_gemma":0.0009334365,"domain_scores_codex":[0.999917,0.00001741576,0.00000345662,0.00001692795,0.0000320438,0.00001310633],"domain_scores_gemma":[0.9998971,0.00003724428,0.00001988061,0.00001170901,0.0000249324,0.000009151987],"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":[0.00006406123,0.00007121822,0.001055541,0.0001420164,0.000031451,0.0001639245,0.00005731764,0.8573338,0.09655637,0.001623639,0.0003278482,0.04257273],"study_design_scores_gemma":[0.00001371383,0.0001842338,0.0007380384,0.000004305036,0.00001089206,0.00005179509,0.00001513295,0.988384,0.009653544,0.0001852766,0.0007530516,0.000005915972],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3264345,0.0003457854,0.6664445,0.0001455647,0.00003221399,0.0001341952,0.00004748164,0.0004028136,0.006012919],"genre_scores_gemma":[0.8548493,0.0001347737,0.1430955,0.00002224433,0.000006548728,0.0001204363,0.00004998517,0.00003667247,0.001684476],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0009276054,"threshold_uncertainty_score":0.002199948,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01932425727556609,"score_gpt":0.2442012395904227,"score_spread":0.2248769823148566,"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."}}