{"id":"W2801915269","doi":"10.1139/tcsme-2011-0019","title":"ANALYSIS OF SYNTHETIC JET FLOW FIELD: APPLICATION OF URANS APPROACH","year":2011,"lang":"en","type":"article","venue":"Transactions of the Canadian Society for Mechanical Engineering","topic":"Fluid Dynamics and Turbulent Flows","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Turbulence; Reynolds stress; K-epsilon turbulence model; Reynolds stress equation model; Mechanics; K-omega turbulence model; Turbulence kinetic energy; Jet (fluid); Physics; Closure (psychology); Reynolds number; Synthetic jet; Reynolds-averaged Navier–Stokes equations; Flow (mathematics); Moment (physics); Field (mathematics); Statistical physics; Reynolds decomposition; Turbulence modeling; Nonlinear system; Classical mechanics; Mathematics; Computer science","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002773783,0.0003560866,0.0002612421,0.0004639866,0.0002124625,0.0004023235,0.0003210528,0.0002954877,0.000990256],"category_scores_gemma":[0.0008341351,0.00009834855,0.0002714322,0.0003108318,0.000221642,0.000262904,0.0001867475,0.0001981375,0.00009955035],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000241975,"about_ca_system_score_gemma":0.0002622876,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003542534,"about_ca_topic_score_gemma":0.001374923,"domain_scores_codex":[0.9998757,0.00003143902,0.00000747315,0.00001887758,0.00005196763,0.00001450631],"domain_scores_gemma":[0.9997196,0.0001110502,0.00002944079,0.00003028241,0.00009527933,0.00001440933],"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":[0.0001807328,0.0001242523,0.005270563,0.00007369158,0.00003294205,0.0004187709,0.0001702969,0.9339126,0.02844925,0.006847379,0.0005165191,0.02400308],"study_design_scores_gemma":[0.000004249373,0.00001742779,0.0005199695,0.000001594565,0.000001455671,0.00001752339,0.00001366325,0.9966858,0.002406956,0.0001824346,0.0001455037,0.000003367987],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8537328,0.0001284466,0.1394855,0.0001246134,0.00006261811,0.00006139712,0.0003448887,0.0009429994,0.005116688],"genre_scores_gemma":[0.974414,0.00005919262,0.02457078,0.0000113775,0.000007927561,0.00002293697,0.0002183848,0.00005868523,0.000636825],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003542534,"threshold_uncertainty_score":0.007043839,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008539027004920346,"score_gpt":0.1747531592586356,"score_spread":0.1662141322537153,"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."}}