{"id":"W3000428683","doi":"10.1016/j.apenergy.2019.114426","title":"Optimal design of wind farms in complex terrains using computational fluid dynamics and adjoint methods","year":2020,"lang":"en","type":"article","venue":"Applied Energy","topic":"Wind Energy Research and Development","field":"Engineering","cited_by":61,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto; Canada Research Chairs","funders":"Canada First Research Excellence Fund; University of Toronto; Canada Foundation for Innovation; Government of Ontario; Compute Canada","keywords":"Terrain; Computational fluid dynamics; Turbine; Computer science; Marine engineering; Wind power; Flow (mathematics); Wake; Optimal design; Mathematical optimization; Simulation; Environmental science; Engineering; Aerospace engineering; Mathematics; Mechanics; Physics","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":[0.0007797015,0.0006707835,0.001274766,0.0006058839,0.0006880505,0.00134047,0.0006535994,0.001444224,0.002266235],"category_scores_gemma":[0.002407764,0.0009830522,0.0005416163,0.0004290646,0.0009410949,0.001057357,0.001066764,0.0009630361,0.0002337428],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004812018,"about_ca_system_score_gemma":0.001402894,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003582176,"about_ca_topic_score_gemma":0.004827533,"domain_scores_codex":[0.9997134,0.0001074713,0.0000127155,0.00004574742,0.00007202319,0.00004869528],"domain_scores_gemma":[0.9992483,0.0004582828,0.00007956875,0.00002752184,0.0001260136,0.00006022023],"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":[0.00002286779,0.00002199283,0.0001404004,0.00002141836,0.000009228478,0.00002506755,0.00001185316,0.9936701,0.000778912,0.001526337,0.0001475201,0.003624294],"study_design_scores_gemma":[0.000008462975,0.00001401112,0.00004097189,0.0000028984,0.000001809526,0.000002594546,0.000007750014,0.9986131,0.0001070628,0.00109084,0.0001080752,0.000002299375],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06558971,0.0004497848,0.9272366,0.0003586112,0.0001059462,0.00009645991,0.00006489724,0.0001370018,0.005960963],"genre_scores_gemma":[0.9019256,0.000296575,0.09475659,0.00006281245,0.00004064482,0.0002080416,0.00007771407,0.00007234048,0.002559702],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003582176,"threshold_uncertainty_score":0.007581294,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05430683446203587,"score_gpt":0.2835969498781573,"score_spread":0.2292901154161214,"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."}}