{"id":"W4391151025","doi":"10.1002/we.2886","title":"ExaWind: Open‐source CFD for hybrid‐RANS/LES geometry‐resolved wind turbine simulations in atmospheric flows","year":2024,"lang":"en","type":"article","venue":"Wind Energy","topic":"Advanced Numerical Methods in Computational Mathematics","field":"Engineering","cited_by":45,"is_retracted":false,"has_abstract":true,"ca_institutions":"Advanced Micro Devices (Canada)","funders":"Oak Ridge National Laboratory; Office of Energy Efficiency; Wind Energy Technologies Office; National Nuclear Security Administration; Sandia National Laboratories; U.S. Department of Energy; Office of Energy Efficiency and Renewable Energy; Office of Science","keywords":"Reynolds-averaged Navier–Stokes equations; Computational fluid dynamics; Aerospace engineering; Turbulence; Large eddy simulation; Solver; Computer science; Turbine; Turbine blade; Computational science; Geometry; Mechanics; Physics; Engineering; Mathematics","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001718653,0.0002521872,0.000336709,0.0000772137,0.00007730582,0.0001189729,0.0003470222,0.00008846811,0.0001191438],"category_scores_gemma":[0.000216197,0.0002544959,0.00009134224,0.0007342689,0.00003125902,0.0002367725,0.00009605518,0.0001835189,0.00001466991],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000141852,"about_ca_system_score_gemma":0.00002693934,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003817297,"about_ca_topic_score_gemma":0.00001952884,"domain_scores_codex":[0.9986691,0.00004086292,0.0004330406,0.0003284341,0.0001882575,0.0003402775],"domain_scores_gemma":[0.9979833,0.001595694,0.00003076769,0.000255253,0.00004253231,0.00009239541],"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":[0.000009456176,0.00003058244,0.000009999038,0.000120287,0.00004554079,0.000008651921,0.0001345213,0.9064401,0.0004915813,0.00215127,0.0001880455,0.09037],"study_design_scores_gemma":[0.0003279035,0.00003297601,0.00005266355,0.0001100228,0.00001846998,0.000007077907,0.00003102135,0.8626655,0.0005568158,0.0314642,0.104465,0.0002683836],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06018454,0.001120251,0.9368504,0.00009295938,0.0004908433,0.0002272197,0.00004217571,0.000323271,0.0006683046],"genre_scores_gemma":[0.5905036,0.00001950403,0.4084684,0.00007373588,0.0002368826,0.00003379088,0.00005136988,0.0001089238,0.0005038618],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.530319,"threshold_uncertainty_score":0.9999907,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0266465153995192,"score_gpt":0.2981765977286331,"score_spread":0.271530082329114,"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."}}