{"id":"W7066697324","doi":"","title":"Implementation and validation of the spalart-allmaras turbulance model","year":2018,"lang":"en","type":"dissertation","venue":"eScholarship@McGill (McGill)","topic":"Computational Fluid Dynamics and Aerodynamics","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; Fonds Québécois de la Recherche sur la Nature et les Technologies","keywords":"Turbulence; Benchmark (surveying); Computational fluid dynamics; K-omega turbulence model; Aerodynamics; K-epsilon turbulence model; Turbulence modeling; Field (mathematics)","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":[0.00107275,0.0005892083,0.0006831497,0.0005228442,0.0009680312,0.001231558,0.002460218,0.001094419,0.004554161],"category_scores_gemma":[0.002127851,0.0002997435,0.0006798909,0.0006015357,0.0006827107,0.0009958165,0.001605188,0.001481583,0.001368242],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009587297,"about_ca_system_score_gemma":0.003011984,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02547733,"about_ca_topic_score_gemma":0.01541204,"domain_scores_codex":[0.9992259,0.0001032074,0.00004565722,0.00007484653,0.0004320545,0.0001183696],"domain_scores_gemma":[0.9990701,0.0002035919,0.00005708822,0.0002064022,0.0003639716,0.00009887515],"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":[0.0001639173,0.0003855263,0.004869419,0.00008957266,0.00002211818,0.0002480439,0.0001433461,0.9330416,0.009707829,0.01631485,0.006803387,0.02821043],"study_design_scores_gemma":[0.00003038279,0.00006271169,0.0004656356,0.000009383488,0.000003517785,0.00002539449,0.00002989515,0.9896184,0.004829697,0.0006906411,0.004218949,0.00001537602],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.6053363,0.0004814041,0.2911459,0.001279023,0.0004439458,0.0007063965,0.005905432,0.01005657,0.08464504],"genre_scores_gemma":[0.833138,0.0002568128,0.1502797,0.0001826391,0.00002510474,0.0004023136,0.00460018,0.0008485891,0.01026649],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.02547733,"threshold_uncertainty_score":0.05065805,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008653199971611113,"score_gpt":0.2311483895320714,"score_spread":0.2224951895604603,"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."}}