{"id":"W4308124840","doi":"10.3390/designs6060102","title":"Optimization and Design of a Flexible Droop Nose Leading Edge Morphing Wing Based on a Novel Black Widow Optimization (B.W.O.) Algorithm—Part II","year":2022,"lang":"en","type":"article","venue":"Designs","topic":"Aeroelasticity and Vibration Control","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure; Université du Québec à Montréal","funders":"Canada Research Chairs","keywords":"Airfoil; Stall (fluid mechanics); Leading edge; Wing; Morphing; Drag; Lift coefficient; Trailing edge; Fitness function; Angle of attack; Engineering; Mathematics; Aerodynamics; Control theory (sociology); Algorithm; Genetic algorithm; Structural engineering; Computer science; Mathematical optimization; Aerospace engineering; Mechanics; Reynolds number; Physics; 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":[0.0002121253,0.0004202448,0.0004021073,0.0002786238,0.0002111919,0.0004218975,0.0004042124,0.0005594272,0.0009187572],"category_scores_gemma":[0.0003087999,0.0002219547,0.0003977519,0.0001616734,0.0002530497,0.0002120948,0.0003454092,0.0002502975,0.0001378364],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002056734,"about_ca_system_score_gemma":0.0006232199,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002811383,"about_ca_topic_score_gemma":0.002321861,"domain_scores_codex":[0.999927,0.00001426135,0.000003294886,0.00001707874,0.00002247411,0.00001577871],"domain_scores_gemma":[0.9999363,0.00002126686,0.00001239754,0.000004594923,0.00001827149,0.000007211998],"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":[0.0001036723,0.00005149928,0.001213919,0.00006929463,0.00002940335,0.00007950832,0.0000379312,0.9338595,0.01961601,0.001766133,0.0003386665,0.04283435],"study_design_scores_gemma":[0.00001178832,0.00005742002,0.0002553368,0.000002996804,0.000004773804,0.00001044756,0.000007696917,0.9978205,0.001488036,0.00009593079,0.0002421834,0.000002952347],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2564694,0.0002757564,0.7372425,0.0001254028,0.00004592281,0.0001854965,0.00006663721,0.0002827036,0.00530629],"genre_scores_gemma":[0.7891743,0.0001177394,0.2081238,0.0000559037,0.00001013195,0.0002440369,0.00009450926,0.00004005871,0.002139523],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002811383,"threshold_uncertainty_score":0.005590022,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03743736356590247,"score_gpt":0.2259073551347978,"score_spread":0.1884699915688954,"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."}}