{"id":"W2325579538","doi":"10.2514/6.2013-2909","title":"Adjoint-Based Aerodynamic Design Optimization Using the Drag Decomposition Method","year":2013,"lang":"en","type":"article","venue":"31st AIAA Applied Aerodynamics Conference","topic":"Computational Fluid Dynamics and Aerodynamics","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Aerodynamics; Drag; Decomposition; Aerodynamic drag; Computer science; Aerospace engineering; Multidisciplinary design optimization; Mathematical optimization; Marine engineering; Mathematics; Engineering; Chemistry","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004879619,0.0005789372,0.0004447601,0.0002243678,0.0003995551,0.0004876093,0.0006535032,0.0002414192,0.0001988933],"category_scores_gemma":[0.00002372174,0.0005243037,0.000145291,0.0006352153,0.0001353821,0.0002922186,0.0001075943,0.0005094956,0.00008520637],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004230895,"about_ca_system_score_gemma":0.0001749339,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008671015,"about_ca_topic_score_gemma":0.00008534957,"domain_scores_codex":[0.9974216,0.0001462018,0.0007041944,0.0005882221,0.0004787201,0.0006610766],"domain_scores_gemma":[0.9981906,0.0004591212,0.0001675785,0.0006546521,0.0003472847,0.0001807766],"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":[0.00001297559,0.00004020532,0.00002567207,0.00003746458,0.00005215145,0.000001269703,0.00007106057,0.9551915,0.02157401,0.0186851,0.00005029731,0.00425831],"study_design_scores_gemma":[0.0005043562,0.00002907489,0.0009500934,0.00004377755,0.00006530427,0.00001169194,0.00007072883,0.9934568,0.000188883,0.004042815,0.00001464864,0.0006218338],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06948522,0.00003296107,0.9267957,0.0002042082,0.0004184166,0.00119934,0.00003772874,0.000521963,0.001304496],"genre_scores_gemma":[0.679244,0.00002219855,0.3199501,0.0001653454,0.00006173184,0.0001590611,0.0002775858,0.00009757485,0.00002233531],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6097589,"threshold_uncertainty_score":0.9997209,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01656755202058006,"score_gpt":0.2422874839720134,"score_spread":0.2257199319514334,"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."}}