{"id":"W1970968973","doi":"10.1109/asc-icsc.2008.4675410","title":"A framework for high-fidelity aerostructural optimization of aircraft configurations","year":2008,"lang":"en","type":"article","venue":"","topic":"Computational Fluid Dynamics and Aerodynamics","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Finite element method; Sensitivity (control systems); Computation; Adjoint equation; Multidisciplinary design optimization; Computer science; Block (permutation group theory); High fidelity; Computational fluid dynamics; Mathematical optimization; Fidelity; Code (set theory); Applied mathematics; Computational science; Algorithm; Mathematics; Engineering; Multidisciplinary approach; Electronic engineering; Aerospace engineering; Partial differential equation; Geometry; Mathematical analysis","routes":{"ca_aff":true,"ca_fund":false,"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":[0.001121361,0.001153854,0.00102034,0.0008553259,0.0009352976,0.001570233,0.002093571,0.001273461,0.004600615],"category_scores_gemma":[0.001893919,0.00072524,0.001167462,0.0005980799,0.001195512,0.001168373,0.00197497,0.001896505,0.0009426725],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001057176,"about_ca_system_score_gemma":0.001687231,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004783444,"about_ca_topic_score_gemma":0.004379278,"domain_scores_codex":[0.9994154,0.0001418453,0.00002033978,0.00005673756,0.0003199898,0.00004559443],"domain_scores_gemma":[0.9994904,0.0001977342,0.00005282738,0.0000976677,0.0001229325,0.00003844587],"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":[0.000008160891,0.00001942721,0.0001192276,0.0000571073,0.00001463758,0.00006131436,0.00003059352,0.8856357,0.002716545,0.09752823,0.0007222313,0.01308677],"study_design_scores_gemma":[0.000003980882,0.000009431813,0.00003530804,0.0000110034,0.000002593368,0.0000243399,0.000007199363,0.9742268,0.0004306612,0.02257173,0.00267029,0.000006804912],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0007615263,0.00005279702,0.996845,0.00004525837,0.000009661755,0.0000269291,0.00003612985,0.0001267371,0.002096048],"genre_scores_gemma":[0.1208516,0.0003384622,0.8743429,0.0000676723,0.0000558709,0.0004922763,0.0002459649,0.0003262068,0.003279023],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004783444,"threshold_uncertainty_score":0.01539057,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01197356583618353,"score_gpt":0.2298535321858263,"score_spread":0.2178799663496427,"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."}}