{"id":"W4230961217","doi":"10.32920/ryerson.14652768","title":"Numerical simulation of liquid fuel prechamber ignition","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Combustion and flame dynamics","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"Mitacs","keywords":"Reynolds-averaged Navier–Stokes equations; Computational fluid dynamics; Mechanics; Autoignition temperature; Ignition system; Large eddy simulation; Turbulence; Fluent; Reciprocating motion; Combustion; Combustion chamber; Materials science; Simulation; Engineering; Physics; Mechanical engineering; Aerospace engineering; Chemistry","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002762914,0.0004316406,0.00097518,0.0004281586,0.0007872806,0.0007578278,0.00114475,0.001072019,0.003314151],"category_scores_gemma":[0.0009083599,0.0002956469,0.0003954781,0.0005710002,0.0005979391,0.0004915394,0.0005846006,0.0006468414,0.0003599761],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00116875,"about_ca_system_score_gemma":0.001749764,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01733403,"about_ca_topic_score_gemma":0.008403746,"domain_scores_codex":[0.9997594,0.00002730931,0.00000932579,0.00003560389,0.0001092413,0.00005912293],"domain_scores_gemma":[0.9996763,0.000136355,0.00003022095,0.00003463197,0.00008350559,0.00003887925],"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":[0.00008136463,0.00005610571,0.0008062632,0.00004219061,0.000007736141,0.0001825332,0.00007350711,0.9811822,0.009981086,0.003287898,0.0002871699,0.00401193],"study_design_scores_gemma":[0.00001167533,0.00003183496,0.0002587736,0.00000242291,0.000001847369,0.00001639752,0.00001178832,0.9961856,0.002773824,0.0002052815,0.0004961317,0.0000044043],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8471173,0.0003213891,0.1170292,0.0001936648,0.0001040489,0.0002344696,0.0009421501,0.001141642,0.0329162],"genre_scores_gemma":[0.9669151,0.0001423375,0.02425783,0.00003284095,0.000009093113,0.0001508756,0.0003321158,0.000061727,0.00809803],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01733403,"threshold_uncertainty_score":0.03446633,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01641045687588116,"score_gpt":0.2476399120369263,"score_spread":0.2312294551610451,"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."}}