{"id":"W2329128548","doi":"10.2514/6.2012-5447","title":"Two-Level Free-Form Deformation for High-Fidelity Aerodynamic Shape Optimization","year":2012,"lang":"en","type":"article","venue":"12th AIAA Aviation Technology, Integration, and Operations (ATIO) Conference and 14th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference","topic":"Advanced Numerical Analysis Techniques","field":"Engineering","cited_by":33,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Fonds Québécois de la Recherche sur la Nature et les Technologies; Natural Sciences and Engineering Research Council of Canada; McGill University","keywords":"Free-form deformation; Aerodynamics; Deformation (meteorology); Morphing; Free form; Computer science; Shape optimization; Structural engineering; Materials science; Mechanics; Physics; Engineering; Computer graphics (images); Composite material; Finite element method","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005339314,0.000544902,0.0007009038,0.001248102,0.001077023,0.0004238826,0.0002817358,0.0004256959,0.0002672688],"category_scores_gemma":[0.000338623,0.0005070058,0.0001126333,0.001794394,0.000317741,0.00291226,0.0001446557,0.000336262,0.00000330926],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001297139,"about_ca_system_score_gemma":0.00007988702,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001580752,"about_ca_topic_score_gemma":0.001160466,"domain_scores_codex":[0.9973909,0.0000900683,0.001115836,0.0006469819,0.0002808339,0.0004753584],"domain_scores_gemma":[0.9974388,0.0001258139,0.0003014423,0.000529143,0.001378004,0.0002267668],"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":[0.00004768392,0.0002123552,0.01113152,0.0001142064,0.0005093102,3.557143e-7,0.001873386,0.8658326,0.002411584,0.07555598,0.00005833577,0.04225274],"study_design_scores_gemma":[0.0007856341,0.0001209748,0.00752007,0.00005853643,0.0006110275,0.000005338723,0.001404096,0.9831057,0.002030767,0.003742538,0.00002352959,0.0005917968],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07004891,0.0002972238,0.9266799,0.0009585946,0.00009014035,0.001031787,0.0002391507,0.0005316072,0.0001226855],"genre_scores_gemma":[0.696008,0.00142876,0.2997469,0.00004574356,0.00005456052,0.0005180513,0.002085053,0.00002830296,0.00008467466],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.626933,"threshold_uncertainty_score":0.9997382,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01641920261148782,"score_gpt":0.2696865129696796,"score_spread":0.2532673103581918,"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."}}