{"id":"W2637749702","doi":"10.2514/6.2017-3052","title":"Investigating the impact of using CFD generated unsteady Mach number dynamic stall data for numerical rotor analysis of helicopter forward flight","year":2017,"lang":"en","type":"article","venue":"35th AIAA Applied Aerodynamics Conference","topic":"Aerospace and Aviation Technology","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada; National Aeronautics and Space Administration","keywords":"Stall (fluid mechanics); Mach number; Computational fluid dynamics; Aerospace engineering; Computer science; Aerodynamics; Aeronautics; Rotor (electric); Mechanics; Engineering; Physics; Mechanical engineering","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":[],"consensus_categories":[],"category_scores_codex":[0.0002440558,0.0002930679,0.0006573623,0.0001275891,0.0002111642,0.00009371306,0.001290996,0.000215023,0.00004956068],"category_scores_gemma":[0.0001107116,0.000231114,0.0001628992,0.0003802514,0.0002524849,0.0001790413,0.0003203669,0.0002445127,0.000004003478],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001077408,"about_ca_system_score_gemma":0.0001101506,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002307359,"about_ca_topic_score_gemma":0.0002229272,"domain_scores_codex":[0.9984555,0.0000229514,0.0005434769,0.0004017043,0.0002003153,0.0003760589],"domain_scores_gemma":[0.9974144,0.0001035648,0.0005003164,0.001748664,0.0001524615,0.00008057836],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004393219,0.00008737569,0.01900277,0.0001804205,0.00243188,0.000001188638,0.0004013067,0.3415726,0.6176885,0.01232258,0.00009885888,0.006168514],"study_design_scores_gemma":[0.0003718441,0.00002036715,0.007165731,0.0000211918,0.0003890514,0.000001215346,0.00007510179,0.9859716,0.005255232,0.0004764608,0.0000116793,0.0002405371],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7387797,0.00001289801,0.2594476,0.00004006239,0.00005322915,0.0004553323,0.0007811995,0.00008096354,0.0003490922],"genre_scores_gemma":[0.9897122,0.00002755349,0.009529032,0.0000147138,0.00002148343,0.00004730405,0.0005838544,0.00004464452,0.00001915262],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.644399,"threshold_uncertainty_score":0.9424554,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04157824078128461,"score_gpt":0.3241883315404269,"score_spread":0.2826100907591423,"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."}}