{"id":"W4367841152","doi":"10.1016/j.jcp.2023.112189","title":"A parallel and adaptative Nitsche immersed boundary method to simulate viscous mixing","year":2023,"lang":"en","type":"article","venue":"Journal of Computational Physics","topic":"Lattice Boltzmann Simulation Studies","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":false,"ca_institutions":"Polytechnique Montréal","funders":"Alliance de recherche numérique du Canada; Natural Sciences and Engineering Research Council of Canada; Mitacs; Ministère de l'Économie, de la Science et de l'Innovation - Québec","keywords":"Impeller; Reynolds number; Mixing (physics); Software; Rushton turbine; Computer science; Mechanics; Boundary (topology); Mechanical engineering; Viscous liquid; Flow (mathematics); Vortex; Immersed boundary method; Computational fluid dynamics; Simulation; Mathematics; Engineering; Physics; Turbulence; Mathematical analysis","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":[0.0005674553,0.0005959067,0.0008886996,0.0004977643,0.0008851456,0.0008071374,0.002517828,0.002241037,0.003232907],"category_scores_gemma":[0.001884592,0.0004502578,0.0005367696,0.0004647995,0.0008932648,0.0009202715,0.001584651,0.001211346,0.0005514175],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005828303,"about_ca_system_score_gemma":0.001144584,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007970504,"about_ca_topic_score_gemma":0.006333592,"domain_scores_codex":[0.9998165,0.00005128059,0.000008902711,0.0000226448,0.00007706451,0.00002354862],"domain_scores_gemma":[0.9995131,0.0001948798,0.00003854859,0.00006020811,0.000135258,0.00005787319],"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":[0.0001186662,0.000184107,0.0006261212,0.00007207129,0.00003985974,0.0001297132,0.0001100212,0.9478999,0.008380804,0.02423754,0.0008947807,0.01730643],"study_design_scores_gemma":[0.000009617498,0.000008078411,0.00002361641,0.00000148807,0.000001625425,0.000004876989,0.000003123182,0.9987299,0.0003251118,0.0005982514,0.0002909283,0.000003405567],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0966109,0.0002910257,0.8873786,0.0003703856,0.0003793387,0.000206489,0.0002231014,0.0009978281,0.0135424],"genre_scores_gemma":[0.5057578,0.0001824828,0.4828163,0.0001986782,0.0001058147,0.0004079075,0.0002481623,0.000519735,0.00976307],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007970504,"threshold_uncertainty_score":0.01584822,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03533081507635737,"score_gpt":0.3219649595671538,"score_spread":0.2866341444907964,"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."}}