{"id":"W4385914709","doi":"10.1016/j.jweia.2023.105541","title":"Accelerated convergence for city-scale flow fields using immersed boundaries and coupled multigrid","year":2023,"lang":"en","type":"article","venue":"Journal of Wind Engineering and Industrial Aerodynamics","topic":"Wind and Air Flow Studies","field":"Environmental Science","cited_by":7,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo; Defence Research and Development Canada","funders":"","keywords":"Multigrid method; Polygon mesh; Immersed boundary method; Grid; Computer science; Computational fluid dynamics; Convergence (economics); Computational science; Adaptive mesh refinement; Solver; Mathematical optimization; Boundary (topology); Simulation; Engineering; Aerospace engineering; Mathematics; Geometry; Partial differential equation","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001397765,0.0008428596,0.001030265,0.0009478732,0.0006924856,0.001040671,0.001354263,0.001456476,0.002263769],"category_scores_gemma":[0.006559297,0.0006464713,0.001035877,0.0005359243,0.001672552,0.001472456,0.002652301,0.001744162,0.0002917476],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001065621,"about_ca_system_score_gemma":0.001121641,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01694888,"about_ca_topic_score_gemma":0.009102616,"domain_scores_codex":[0.9996614,0.0001422429,0.00001598846,0.00003799225,0.00009406204,0.0000483495],"domain_scores_gemma":[0.9972874,0.001741108,0.0001553895,0.0001951415,0.0004456949,0.0001753147],"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":[0.0001018508,0.0000572816,0.0009190075,0.00004333679,0.00003060545,0.00005119615,0.00008181222,0.9788422,0.001822693,0.01091737,0.0003724917,0.006760077],"study_design_scores_gemma":[0.000004569314,0.000002838215,0.00002613436,9.936124e-7,5.771002e-7,0.000001190859,0.000002242503,0.9992315,0.00008042245,0.0006035907,0.00004465286,0.000001399586],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1680125,0.0003258124,0.8219975,0.0004682766,0.0002255109,0.0001074982,0.0001252196,0.0005974506,0.008140293],"genre_scores_gemma":[0.8499746,0.0001359136,0.1456751,0.00008439231,0.00005533078,0.0001165141,0.0001877745,0.0003047339,0.003465659],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01694888,"threshold_uncertainty_score":0.03370047,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03523071800868354,"score_gpt":0.2386138932223771,"score_spread":0.2033831752136935,"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."}}