{"id":"W4281701383","doi":"10.1088/1742-6596/2265/4/042077","title":"Numerical Study using RANS model to Predict Loading of a Wind Turbine Blade with a Trailing Edge Flap","year":2022,"lang":"en","type":"article","venue":"Journal of Physics Conference Series","topic":"Wind Energy Research and Development","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Airfoil; Trailing edge; Angle of attack; Pitching moment; Lift coefficient; Aerodynamic center; Computational fluid dynamics; Mechanics; Wind tunnel; Chord (peer-to-peer); Stall (fluid mechanics); Reynolds-averaged Navier–Stokes equations; Lift (data mining); Turbine; Reynolds number; Turbine blade; Wing; Structural engineering; Physics; Aerodynamics; Engineering; Aerospace engineering; Computer science; Turbulence","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":[0.0002092962,0.0001470917,0.000352837,0.0001179556,0.000107017,0.00003896656,0.0002299205,0.00001624848,0.00002826565],"category_scores_gemma":[0.00001273039,0.0001253883,0.00005885073,0.000347442,0.00003225233,0.0003100781,0.00006394406,0.0003514159,3.774215e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008630346,"about_ca_system_score_gemma":0.0003680114,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001163595,"about_ca_topic_score_gemma":0.000003295888,"domain_scores_codex":[0.9986717,0.00003989739,0.0003490266,0.0001048423,0.000580497,0.0002540371],"domain_scores_gemma":[0.9994422,0.00002358771,0.00009417458,0.000112099,0.000178719,0.0001492881],"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":[0.0002617436,0.0001265517,0.0006669388,0.00003613125,0.0001748279,0.0000337027,0.007610474,0.9562175,0.03226019,0.0001616022,0.0000294408,0.002420925],"study_design_scores_gemma":[0.003304242,0.004444904,0.00234482,0.0003677867,0.0001570181,0.0002265221,0.01773845,0.7592349,0.2099283,0.001226146,0.0002723625,0.0007545587],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8659914,0.0000314288,0.133448,0.00004088721,0.0000788679,0.0001197667,0.000009050771,0.00001766429,0.0002629333],"genre_scores_gemma":[0.9953841,0.000005031518,0.004440069,0.00000906372,0.00009510987,0.000005776276,0.000001166671,0.0000237197,0.00003595242],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1969826,"threshold_uncertainty_score":0.5113184,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03632513711642461,"score_gpt":0.2538124994498552,"score_spread":0.2174873623334306,"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."}}