{"id":"W1991057133","doi":"10.1017/jfm.2015.29","title":"A grid-independent length scale for large-eddy simulations","year":2015,"lang":"en","type":"article","venue":"Journal of Fluid Mechanics","topic":"Fluid Dynamics and Turbulent Flows","field":"Engineering","cited_by":68,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada; Queen's University; Canada Research Chairs","keywords":"Turbulence; Large eddy simulation; Length scale; Homogeneous isotropic turbulence; Scale (ratio); Grid; Flow (mathematics); Turbulence kinetic energy; Kolmogorov microscales; Reynolds number; Isotropy; Computer science; Statistical physics; Mechanics; Physics; Direct numerical simulation; Geometry; Mathematics; K-omega turbulence model; Optics","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":[0.0005026433,0.0001374194,0.0002441685,0.0001472142,0.000047897,0.00004205081,0.0001899485,0.0001011076,0.00002006855],"category_scores_gemma":[0.00008543947,0.0001256343,0.0001643392,0.0001158851,0.00000245138,0.0001920529,0.00003695006,0.0001926529,0.00001018088],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000144703,"about_ca_system_score_gemma":0.00007578918,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":9.533794e-7,"about_ca_topic_score_gemma":0.00001635389,"domain_scores_codex":[0.998884,0.00001396525,0.0004365346,0.00008467682,0.0003251891,0.0002556359],"domain_scores_gemma":[0.9991599,0.00005134979,0.00008386569,0.0001614482,0.0003228023,0.0002205855],"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":[0.00004605695,0.0001048529,0.00001954208,0.00003693054,0.00009525815,0.00001326229,0.0003007158,0.9690192,0.007687891,0.009190952,0.01270973,0.0007756497],"study_design_scores_gemma":[0.00141253,0.0001845892,0.000006811335,0.00002735364,0.00005835864,0.00004813926,0.00006367195,0.9705,0.001053479,0.004547131,0.0219605,0.0001374553],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1817963,0.001118343,0.810881,0.0002196515,0.005134345,0.0002962062,0.0001581471,0.00008636453,0.0003095697],"genre_scores_gemma":[0.9902716,0.000113116,0.008763155,0.00006476753,0.0006328852,0.000003743978,0.00001161598,0.00004702626,0.00009207387],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8084753,"threshold_uncertainty_score":0.5123219,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01651183762501254,"score_gpt":0.2374065160285941,"score_spread":0.2208946784035816,"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."}}