{"id":"W4312008235","doi":"10.1016/j.jcp.2022.111833","title":"Large eddy simulation on unstructured grids using explicit differential filtering: A case study of Taylor-Green vortex","year":2022,"lang":"en","type":"article","venue":"Journal of Computational Physics","topic":"Fluid Dynamics and Turbulent Flows","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; National Institutes of Health; National Institute on Deafness and Other Communication Disorders; Alliance de recherche numérique du Canada","keywords":"Large eddy simulation; Reynolds number; Unstructured grid; Turbulence; Filter (signal processing); Laminar flow; Vortex; Grid; Applied mathematics; Mechanics; Mathematics; Physics; Computer science; Geometry","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004982627,0.000414036,0.0006001899,0.0002987182,0.0007463924,0.0009508296,0.0005108979,0.001433499,0.0006982896],"category_scores_gemma":[0.001585607,0.0002303988,0.0004580203,0.0003541391,0.0005908284,0.0007246959,0.0004981419,0.0004633642,0.00008142796],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003798514,"about_ca_system_score_gemma":0.0005359253,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00822765,"about_ca_topic_score_gemma":0.00588637,"domain_scores_codex":[0.9998568,0.00005739958,0.00000888176,0.00001878113,0.00003258012,0.00002554381],"domain_scores_gemma":[0.9988488,0.0008433413,0.00005600917,0.00006875987,0.000114818,0.00006832284],"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":[0.0002009609,0.0003043521,0.003592765,0.00004013318,0.00002164441,0.0006856896,0.0001156602,0.9772192,0.006854713,0.003364901,0.0003663052,0.007233598],"study_design_scores_gemma":[0.00001677259,0.00003708621,0.0003120863,0.000001646521,0.000003440614,0.00002365791,0.00002251678,0.9981043,0.001066113,0.0003337189,0.00007398785,0.000004662331],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9279382,0.0001364198,0.06648553,0.0002391741,0.00004400618,0.00005265202,0.00006568229,0.0002014587,0.004836838],"genre_scores_gemma":[0.9884316,0.0000297876,0.01063386,0.00001126844,0.000005201556,0.00001022709,0.00003105282,0.00001782595,0.0008291578],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00822765,"threshold_uncertainty_score":0.01635951,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01957474941220853,"score_gpt":0.2592510163459697,"score_spread":0.2396762669337612,"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."}}