{"id":"W2885614773","doi":"10.1139/tcsme-2017-0088","title":"Hydrokinetic turbine array modeling for performance analysis and deployment optimization","year":2018,"lang":"en","type":"article","venue":"Transactions of the Canadian Society for Mechanical Engineering","topic":"Wind Energy Research and Development","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Resources Canada; Natural Sciences and Engineering Research Council of Canada; Compute Canada","keywords":"Turbine; Reynolds-averaged Navier–Stokes equations; Wake; Computational fluid dynamics; Marine engineering; Aerospace engineering; Power (physics); Turbine blade; Software deployment; Computer science; Environmental science; Mechanics; Engineering; Physics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"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":[0.0002821398,0.0005738553,0.00052391,0.0003475527,0.000232271,0.0005872029,0.0006555707,0.0006023797,0.003936458],"category_scores_gemma":[0.000550678,0.000341288,0.0004019739,0.0005311382,0.0002168159,0.0005989961,0.000377147,0.0004456184,0.001033012],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003541243,"about_ca_system_score_gemma":0.0006983242,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003069355,"about_ca_topic_score_gemma":0.004011625,"domain_scores_codex":[0.9998789,0.00003364242,0.000006180667,0.00001638575,0.00004956225,0.00001531735],"domain_scores_gemma":[0.9997415,0.000101911,0.00003516742,0.00003902436,0.00007038339,0.00001196244],"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":[0.000005640296,0.000009337989,0.0001355578,0.00001398688,0.000003965921,0.00001197837,0.00000548761,0.9940282,0.001523217,0.0007338081,0.0002014511,0.00332739],"study_design_scores_gemma":[0.000001575268,0.000007072746,0.0000636801,0.000001338515,0.000001144777,0.000003370364,0.00000182742,0.9989161,0.0003414727,0.0002461122,0.0004145206,0.000001790854],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07211924,0.0003450594,0.9032621,0.0002460993,0.00005298894,0.0001083718,0.0008628168,0.001249875,0.02175342],"genre_scores_gemma":[0.8357943,0.0006164252,0.1505801,0.00007338404,0.00004529134,0.0003612754,0.0006054444,0.0003975114,0.01152627],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003936458,"threshold_uncertainty_score":0.01316881,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009853280374517109,"score_gpt":0.1940690991768938,"score_spread":0.1842158188023767,"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."}}