{"id":"W3008313162","doi":"10.3329/jname.v16i1.34756","title":"Improving accuracy and efficiency of CFD predictions of propeller open water performance","year":2019,"lang":"en","type":"article","venue":"Journal of Naval Architecture and Marine Engineering","topic":"Cavitation Phenomena in Pumps","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Computational fluid dynamics; Reynolds-averaged Navier–Stokes equations; Propeller; Solver; Computer science; Thrust; Marine engineering; Computation; Propulsion; Performance prediction; Simulation; Aerospace engineering; Mechanics; Algorithm; Physics; Engineering","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008446134,0.0007143078,0.0005415864,0.0005657786,0.0003042142,0.0009074001,0.0008448126,0.0006706365,0.001135146],"category_scores_gemma":[0.005222652,0.0003770877,0.0005188845,0.0002801717,0.0004271255,0.0009560834,0.0005997748,0.0006885083,0.0004637157],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004242864,"about_ca_system_score_gemma":0.0007182105,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00451703,"about_ca_topic_score_gemma":0.002334038,"domain_scores_codex":[0.9994957,0.00009552127,0.00003555677,0.00008623506,0.0002393548,0.00004762779],"domain_scores_gemma":[0.9981079,0.001060324,0.0001652975,0.0003261766,0.0003201342,0.00002016947],"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":[0.0001188549,0.00008609914,0.00563568,0.00008673306,0.00003375281,0.0001116509,0.00009451029,0.8845825,0.04179182,0.00198467,0.0002594517,0.06521431],"study_design_scores_gemma":[0.000009279592,0.00004551217,0.001028542,0.000009765506,0.000006609291,0.00002065186,0.00001808529,0.9744692,0.02346038,0.0004240163,0.0004928096,0.00001509157],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2610482,0.0002687619,0.7306704,0.0001633644,0.00004690908,0.00008020956,0.0002072786,0.001908539,0.005606399],"genre_scores_gemma":[0.9010681,0.0001323136,0.0975479,0.00002189883,0.000009924284,0.00004450233,0.0001694749,0.00019637,0.0008096385],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00451703,"threshold_uncertainty_score":0.008981466,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.003220244684908781,"score_gpt":0.1778129519893656,"score_spread":0.1745927073044568,"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."}}