{"id":"W2559968736","doi":"10.1016/j.combustflame.2016.11.015","title":"A priori filtered chemical source term modeling for LES of high Karlovitz number premixed flames","year":2016,"lang":"en","type":"article","venue":"Combustion and Flame","topic":"Combustion and flame dynamics","field":"Engineering","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Air Force Office of Scientific Research; Fonds de recherche du Québec – Nature et technologies; U.S. Department of Energy; Office of Science; National Science Foundation","keywords":"Lewis number; Term (time); Turbulence; A priori and a posteriori; Estimator; Diffusion; Chemistry; Statistical physics; Probability density function; Thermodynamics; Mechanics; Mathematics; Physics; Combustion; Statistics; Physical chemistry","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"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":[0.0009945168,0.0007461018,0.0005726009,0.0004533499,0.0004391462,0.001088398,0.0009670384,0.0008406242,0.0005831745],"category_scores_gemma":[0.002213799,0.0002973775,0.0005276783,0.0002961922,0.0004809072,0.001248994,0.0003554874,0.0008563987,0.0001612451],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001163655,"about_ca_system_score_gemma":0.001287812,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01060652,"about_ca_topic_score_gemma":0.005073722,"domain_scores_codex":[0.9997628,0.00005825549,0.00001978489,0.00002726659,0.0001033192,0.00002848093],"domain_scores_gemma":[0.9990844,0.0004766273,0.0001066869,0.00009639312,0.0002048201,0.00003108145],"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":[0.000084428,0.00006410098,0.002337703,0.00004818308,0.00001703327,0.00006283089,0.00004982468,0.9695166,0.01641861,0.005897921,0.0001213585,0.005381384],"study_design_scores_gemma":[0.000004117487,0.00000878807,0.0001533932,0.000002350048,0.000001252329,0.000004046691,0.000002695924,0.9968476,0.002633477,0.0002722168,0.00006593938,0.000004065409],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3686559,0.0003110665,0.6258077,0.0001422253,0.00005649038,0.000107999,0.0004388804,0.001264534,0.003215223],"genre_scores_gemma":[0.9386396,0.0001904273,0.05956843,0.00003554413,0.00001522701,0.00009883309,0.0002801851,0.0001176862,0.001054074],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01060652,"threshold_uncertainty_score":0.02108955,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01220260212737608,"score_gpt":0.2129626344710535,"score_spread":0.2007600323436774,"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."}}