{"id":"W2943453974","doi":"10.1007/s10494-019-00019-x","title":"Subgrid Reaction-Diffusion Closure for Large Eddy Simulations Using the Linear-Eddy Model","year":2019,"lang":"en","type":"article","venue":"Flow Turbulence and Combustion","topic":"Combustion and flame dynamics","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Vetenskapsrådet; Chalmers Tekniska Högskola; Small Business Innovative Research and Small Business Technology Transfer; Canada Excellence Research Chairs, Government of Canada","keywords":"Large eddy simulation; Combustion; Turbulence; Mechanics; Closure (psychology); Eddy diffusion; Computational fluid dynamics; Diffusion flame; Flame structure; Diffusion; Thermodynamics; Turbulence modeling; Turbulent diffusion; Statistical physics; Range (aeronautics); Premixed flame; Direct numerical simulation; Physics; Chemistry; Aerospace engineering; Reynolds number; Engineering; Physical chemistry; Combustor","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001379036,0.0001436392,0.0001285274,0.00006134088,0.0002344935,0.00004182073,0.00008778537,0.0001155777,0.00002195133],"category_scores_gemma":[0.00001875906,0.0001203259,0.00006144326,0.0001473281,0.00001565043,0.0002116466,0.00002781121,0.0001933946,0.00001212377],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004996193,"about_ca_system_score_gemma":0.00001770622,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006231301,"about_ca_topic_score_gemma":0.00002119785,"domain_scores_codex":[0.9992851,0.00001335551,0.0001868678,0.0001771786,0.0001312827,0.0002062132],"domain_scores_gemma":[0.9995274,0.00006894202,0.00003773196,0.0002313656,0.0000881641,0.00004641648],"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":[0.0000196208,0.00002319133,0.0007149855,0.00004862403,0.000009992805,1.60026e-7,0.0001875489,0.9938706,0.002965868,0.0007129864,0.0001738814,0.001272582],"study_design_scores_gemma":[0.0005008901,0.00002400631,0.0007251754,0.00004759476,0.00003624381,0.000004517614,0.00009953597,0.996801,0.00009282562,0.0005059658,0.001001731,0.0001604952],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6837563,0.0001077103,0.3149961,0.00008257728,0.0004995205,0.0003376026,0.00006073105,0.0001208118,0.00003872422],"genre_scores_gemma":[0.9957004,0.0001182321,0.003474305,0.0000863997,0.000138892,0.00001207723,0.0001325456,0.00002598641,0.0003111442],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3119442,"threshold_uncertainty_score":0.4906749,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01306562154184417,"score_gpt":0.2385680427134363,"score_spread":0.2255024211715921,"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."}}