{"id":"W4383554993","doi":"10.20944/preprints202307.0409.v1","title":"An Analysis of CFD-DEM with Coarse Graining for Turbulent Particle-Laden Jet Flows","year":2023,"lang":"en","type":"preprint","venue":"Preprints.org","topic":"Particle Dynamics in Fluid Flows","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Granularity; Mechanics; Computational fluid dynamics; Turbulence; Stokes number; Reynolds number; Jet (fluid); Physics; CFD-DEM; Scaling; Nozzle; Flow (mathematics); Particle (ecology); Statistical physics; Mathematics; Computer science; Thermodynamics; Geometry; Geology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001095772,0.000795548,0.0006396985,0.0007396332,0.0006697346,0.0009362967,0.0007192858,0.001094549,0.001335541],"category_scores_gemma":[0.003617281,0.0004002967,0.0006353593,0.000618475,0.0006423154,0.0007909355,0.0005532383,0.0007823373,0.0001594982],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009923015,"about_ca_system_score_gemma":0.001031771,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01327815,"about_ca_topic_score_gemma":0.007913204,"domain_scores_codex":[0.9997078,0.00005961078,0.00002513302,0.00004519006,0.0001145533,0.00004777552],"domain_scores_gemma":[0.9981602,0.001093099,0.0001201734,0.0002618037,0.0002957591,0.00006891715],"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":[0.0000916279,0.00009365026,0.004076079,0.00008740685,0.00002469207,0.00008529922,0.00006671713,0.978526,0.007290486,0.002711091,0.0002604266,0.006686506],"study_design_scores_gemma":[0.000005695993,0.00001108648,0.0006895933,0.000005117223,0.000002338833,0.000007110417,0.00001013785,0.9965527,0.002008382,0.0003858742,0.0003167862,0.000005191036],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.795227,0.0005021729,0.1894392,0.0004461831,0.0001431159,0.0001912944,0.001104701,0.00189481,0.01105146],"genre_scores_gemma":[0.9465207,0.0001241079,0.05147544,0.00006691206,0.00001546105,0.00008668267,0.0005918186,0.0002040797,0.0009147804],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01327815,"threshold_uncertainty_score":0.02640176,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1299835835973317,"score_gpt":0.3464047042664069,"score_spread":0.2164211206690752,"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."}}