{"id":"W2047155601","doi":"10.1115/gt2013-95005","title":"Investigation of Efficient CFD Methods for the Prediction of Blade Damping","year":2013,"lang":"en","type":"article","venue":"","topic":"Turbomachinery Performance and Optimization","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ansys (Canada)","funders":"","keywords":"Computational fluid dynamics; Aerodynamics; Blade (archaeology); Blade element momentum theory; Transonic; Structural engineering; Axial compressor; Rotor (electric); Blade element theory; Mach number; Computer science; Aeroelasticity; Engineering; Mechanical engineering; Gas compressor; Mechanics; Turbine blade; Aerospace engineering; Turbine; Physics","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":[],"consensus_categories":[],"category_scores_codex":[0.0001357128,0.00004071809,0.00005862944,0.00003749036,0.00002160716,0.000005071047,0.00004384752,0.00002723855,0.0000307447],"category_scores_gemma":[0.00001892935,0.00002671247,0.00002379865,0.00009793619,0.00001828003,0.00008166698,0.000005941633,0.00002759413,0.000001169647],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000008761591,"about_ca_system_score_gemma":0.000004207314,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001953463,"about_ca_topic_score_gemma":9.346901e-7,"domain_scores_codex":[0.9996983,0.000008968283,0.0001579007,0.00003820321,0.00004193751,0.00005465867],"domain_scores_gemma":[0.9997407,0.00008418722,0.00003103985,0.00008609028,0.00004663077,0.00001138712],"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":[0.00000112679,0.00000173513,0.0007384677,0.00005847246,0.00001238847,8.023099e-10,0.0002508569,0.9271368,0.06138376,0.0001836245,0.0002677978,0.009964914],"study_design_scores_gemma":[0.00008122453,0.00001388601,0.01012618,0.000009751249,0.00001049267,1.493802e-7,0.00003676119,0.8472282,0.1423236,0.00006767338,0.00008135816,0.00002075067],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.245904,0.00006961732,0.7532074,0.00003680608,0.0001319822,0.0002338156,0.000002278086,0.00004546957,0.0003686239],"genre_scores_gemma":[0.9141679,0.00001924885,0.08568305,0.00001333942,0.00002837975,0.00003965408,0.000009896914,0.00000724943,0.00003123461],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.668264,"threshold_uncertainty_score":0.1089303,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02010507001247708,"score_gpt":0.2542268306116149,"score_spread":0.2341217605991378,"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."}}