{"id":"W2131582599","doi":"10.1002/we.1877","title":"Time‐adaptive wind turbine model for an LES framework","year":2015,"lang":"en","type":"article","venue":"Wind Energy","topic":"Wind Energy Research and Development","field":"Engineering","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; École Polytechnique Fédérale de Lausanne; National Supercomputing Center, Korea Institute of Science and Technology Information; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; National Science Foundation","keywords":"Wind gradient; Wind power; Geostrophic wind; Turbine; Wind direction; Planetary boundary layer; Wind profile power law; Wind shear; Thermal wind; Wind speed; Meteorology; Log wind profile; Environmental science; Marine engineering; Computer science; Geology; Engineering; Aerospace engineering; Turbulence; Physics; Electrical engineering","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":[0.0004005515,0.0007216199,0.0007162307,0.0002462542,0.0004155845,0.001035874,0.001260537,0.001578067,0.004758789],"category_scores_gemma":[0.0008725916,0.0003187997,0.00064022,0.0003158543,0.0005323254,0.0007143946,0.0007211067,0.001263385,0.0008982457],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004345435,"about_ca_system_score_gemma":0.0008592555,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008504795,"about_ca_topic_score_gemma":0.004869864,"domain_scores_codex":[0.9998324,0.00005776638,0.00001382656,0.00002319514,0.00005747657,0.00001535448],"domain_scores_gemma":[0.9997104,0.0001081382,0.00003421187,0.00003224275,0.00008679157,0.00002829224],"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":[0.00004234594,0.00004262815,0.0003320841,0.00004786503,0.0000188045,0.00009910321,0.00003188383,0.9844572,0.002689833,0.007462504,0.0008791692,0.003896625],"study_design_scores_gemma":[0.000007912057,0.000005144695,0.00003016922,0.000002026542,0.000001287405,0.000003218725,0.000002740171,0.9988135,0.0001205646,0.0003948677,0.0006160618,0.000002513359],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08113397,0.000642759,0.8809748,0.0007587371,0.0003769987,0.0004044117,0.001505568,0.003259316,0.03094339],"genre_scores_gemma":[0.763652,0.0006488008,0.2094769,0.0002845283,0.0002372544,0.001419591,0.0017003,0.000442667,0.02213797],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008504795,"threshold_uncertainty_score":0.01691061,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04697559537868538,"score_gpt":0.2537373426362856,"score_spread":0.2067617472576002,"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."}}