{"id":"W2884178843","doi":"10.1016/j.proci.2018.07.026","title":"Large-Eddy Simulation of the lean-premixed PRECCINSTA burner with wall heat loss","year":2018,"lang":"en","type":"article","venue":"Proceedings of the Combustion Institute","topic":"Combustion and flame dynamics","field":"Engineering","cited_by":80,"is_retracted":false,"has_abstract":false,"ca_institutions":"Safran Electronics (Canada)","funders":"","keywords":"Combustor; Large eddy simulation; Adiabatic process; Mechanics; Materials science; Topology (electrical circuits); Polygon mesh; Mechanical engineering; Premixed flame; Computer science; Thermodynamics; Combustion; Chemistry; Engineering; Turbulence; Physics; Electrical engineering","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":[0.0003365531,0.0005955892,0.0008596854,0.0003346181,0.0009380159,0.001051415,0.001039259,0.001461222,0.003492514],"category_scores_gemma":[0.0009913046,0.0004821187,0.000579589,0.0004028121,0.0008178428,0.0007420867,0.0005095981,0.00109395,0.0002284472],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001416505,"about_ca_system_score_gemma":0.001426394,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03898923,"about_ca_topic_score_gemma":0.02969876,"domain_scores_codex":[0.9998792,0.0000215266,0.000005082678,0.0000189156,0.00002661056,0.00004862289],"domain_scores_gemma":[0.9995047,0.0002343261,0.0000382213,0.00003008702,0.00005860818,0.0001340268],"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":[0.0005645408,0.0003348531,0.004066997,0.00004235495,0.00004037362,0.0002189123,0.00005548478,0.9867961,0.004676083,0.000946548,0.0005439114,0.001713843],"study_design_scores_gemma":[0.00006557281,0.00007682317,0.002124103,0.000003224964,0.000006220776,0.000008879217,0.00003520423,0.9965324,0.0009037401,0.0001053672,0.0001301526,0.000008354174],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9881144,0.00009413279,0.002733388,0.000239406,0.00004968541,0.0000307294,0.000251904,0.0001461692,0.008340225],"genre_scores_gemma":[0.9972872,0.00002283016,0.0007814436,0.00003022919,0.000006239514,0.00001180878,0.0001416514,0.00002686984,0.001691732],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03898923,"threshold_uncertainty_score":0.07752454,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008902240199116828,"score_gpt":0.2141292925717375,"score_spread":0.2052270523726206,"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."}}