{"id":"W4229009734","doi":"10.1108/hff-08-2021-0524","title":"Estimates of turbulence modeling uncertainties in NACA65 cascade flow predictions by Bayesian model-scenario averaging","year":2022,"lang":"en","type":"article","venue":"International Journal of Numerical Methods for Heat &amp Fluid Flow","topic":"Turbomachinery Performance and Optimization","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Safran Electronics (Canada)","funders":"","keywords":"Reynolds-averaged Navier–Stokes equations; Weighting; Calibration; Turbulence; Cascade; Turbulence modeling; Surrogate model; Computer science; Bayesian probability; K-omega turbulence model; Flow (mathematics); Mathematics; K-epsilon turbulence model; Mathematical optimization; Statistics; Engineering; Meteorology; Physics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007058224,0.0001840735,0.000359185,0.0003822468,0.0001035512,0.00003542873,0.0004157003,0.0000615052,0.0000648736],"category_scores_gemma":[0.0001729631,0.0001817194,0.0001838235,0.0002471468,0.00002726439,0.0004104654,0.00006777836,0.0004508878,3.747903e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003902093,"about_ca_system_score_gemma":0.00007130588,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004221327,"about_ca_topic_score_gemma":0.000002110575,"domain_scores_codex":[0.9982831,0.000086825,0.000804993,0.0001557996,0.000432651,0.0002365552],"domain_scores_gemma":[0.9992453,0.0002320395,0.00009795901,0.0001144498,0.0002250461,0.00008513717],"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":[0.0001175588,0.00006880001,0.0002164121,0.0000268601,0.0001034223,0.000002238629,0.0007030378,0.9758501,0.004693849,0.00002272422,0.0004196119,0.01777535],"study_design_scores_gemma":[0.0005600947,0.00008705683,0.00001342957,0.00007227597,0.00002981289,0.00009434277,0.00007691987,0.9958874,0.001084269,0.001175552,0.000759271,0.0001595822],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01593052,0.001085141,0.9812269,0.0002917605,0.001149638,0.0001267835,0.0001020566,0.0000420051,0.00004514873],"genre_scores_gemma":[0.3821483,0.0002159223,0.6173529,0.00005761283,0.0001061455,0.00002822374,0.0000456172,0.00002974607,0.00001556312],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.3662178,"threshold_uncertainty_score":0.74103,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01818147134675234,"score_gpt":0.3135403253829868,"score_spread":0.2953588540362345,"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."}}