{"id":"W2319489757","doi":"10.2514/6.2007-1274","title":"Time-Varying Objective Functions for Optimum Shape Design via Control Theory","year":2007,"lang":"en","type":"article","venue":"45th AIAA Aerospace Sciences Meeting and Exhibit","topic":"Computational Fluid Dynamics and Aerodynamics","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Mach number; Rotor (electric); Inflow; Solver; Control theory (sociology); Variation (astronomy); Boundary value problem; Frequency domain; Boundary (topology); Aerodynamics; Aeroelasticity; Computer science; Helicopter rotor; Computational fluid dynamics; Domain (mathematical analysis); Flow (mathematics); Mathematics; Control (management); Engineering; Mathematical optimization; Aerospace engineering; Mechanics; Mathematical analysis; Mechanical engineering; Physics; Geometry","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":[0.001572121,0.001108307,0.0007756219,0.0005662628,0.0003522477,0.0008835647,0.0005414916,0.0008925067,0.002546212],"category_scores_gemma":[0.002564449,0.0004169152,0.0005201086,0.0003717334,0.0009173165,0.0006503651,0.0007115046,0.001046079,0.0003370601],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007517282,"about_ca_system_score_gemma":0.0008779789,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001857023,"about_ca_topic_score_gemma":0.001519931,"domain_scores_codex":[0.9996459,0.0001313523,0.00001164816,0.00004877081,0.0001339091,0.00002825358],"domain_scores_gemma":[0.9993042,0.0004525834,0.00007222994,0.00002132012,0.0001301542,0.00001953867],"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":[0.00002354966,0.00003245043,0.00007904394,0.00007246475,0.00001312091,0.00002635349,0.00005152742,0.9188246,0.002336732,0.05546804,0.0005512857,0.02252094],"study_design_scores_gemma":[0.00000555189,0.00001450628,0.00001379724,0.00000522708,0.000001720392,0.000003060284,0.000002868422,0.9934846,0.0002316295,0.00575074,0.0004836191,0.000002842906],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001963081,0.0001053684,0.9960678,0.0000572903,0.00001447357,0.00001945464,0.000005280324,0.00004117031,0.00172622],"genre_scores_gemma":[0.4784191,0.0006894003,0.5107327,0.0002190534,0.0001215122,0.000666059,0.00009218867,0.0002343634,0.008825547],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002546212,"threshold_uncertainty_score":0.008517981,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009011134393908181,"score_gpt":0.2245412031488613,"score_spread":0.2155300687549531,"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."}}