{"id":"W2320928796","doi":"10.2514/6.2015-0914","title":"Adaptive curvature control grid generation algorithms for complex glaze ice shapes RANS simulations","year":2015,"lang":"en","type":"article","venue":"53rd AIAA Aerospace Sciences Meeting","topic":"Icing and De-icing Technologies","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Algorithm; Mesh generation; Curvature; Multigrid method; Computer science; Solver; Context (archaeology); Mathematics; Mathematical optimization; Geometry; Mathematical analysis; Partial differential equation; Engineering; Finite element method","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":[0.0004579987,0.0003554873,0.0003266176,0.0003845275,0.0002391155,0.0005029986,0.0009227335,0.0005013238,0.001738513],"category_scores_gemma":[0.001352725,0.0002579853,0.000347685,0.0002942998,0.0004314542,0.0004023981,0.0008234677,0.0006223331,0.0004759881],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004281364,"about_ca_system_score_gemma":0.000388275,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002707567,"about_ca_topic_score_gemma":0.002264743,"domain_scores_codex":[0.9997786,0.00004924835,0.00001428253,0.00002654227,0.0001085681,0.00002275837],"domain_scores_gemma":[0.9995454,0.0001519612,0.00006683816,0.00008441403,0.0001298871,0.0000215343],"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":[0.00005703625,0.0000244498,0.0006712731,0.00004085943,0.00001137501,0.00006444792,0.0001226783,0.9019428,0.01267523,0.01479171,0.001050679,0.06854746],"study_design_scores_gemma":[0.000004225696,0.000006370055,0.00006190562,0.000001831681,6.115774e-7,0.000008237326,0.000004790359,0.9968693,0.001507351,0.000855353,0.0006766815,0.000003371451],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01190364,0.00003520387,0.9861935,0.00003878013,0.00001309552,0.00003100112,0.00002439733,0.0005584366,0.001201927],"genre_scores_gemma":[0.3444241,0.00008589673,0.6525576,0.00004618644,0.00001772791,0.0001305124,0.0001532588,0.0003446907,0.002239976],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002707567,"threshold_uncertainty_score":0.005815923,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1078662898428898,"score_gpt":0.2976436971213055,"score_spread":0.1897774072784157,"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."}}