{"id":"W3099645355","doi":"10.1016/j.ceja.2020.100054","title":"A chimera approach for MP-PIC simulations of dense particulate flows using large parcel size relative to the computational cell size","year":2020,"lang":"en","type":"article","venue":"Chemical Engineering Journal Advances","topic":"Granular flow and fluidized beds","field":"Engineering","cited_by":38,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Mitacs","keywords":"Cell size; Chimera (genetics); Particulates; Environmental science; Physics; Statistical physics; Mechanics; Chemistry; Biology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001015781,0.0001900372,0.0002769252,0.00002954721,0.00009251657,0.00003119788,0.0001688139,0.0000600162,0.00001448524],"category_scores_gemma":[0.0003973162,0.0001525833,0.0001590324,0.0002777617,0.00001659494,0.0001788349,0.00003065652,0.0003062869,0.000001818206],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004045987,"about_ca_system_score_gemma":0.00001785495,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":2.064905e-7,"about_ca_topic_score_gemma":5.695851e-8,"domain_scores_codex":[0.998937,0.00001308316,0.0003943237,0.0001510845,0.0002002075,0.0003042797],"domain_scores_gemma":[0.9990178,0.0005479506,0.00005413367,0.00008682586,0.00009097336,0.000202276],"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":[0.00002001299,0.00002014464,0.00002205243,0.000095941,0.00004204273,0.000001307255,0.0004546311,0.7759069,0.223289,0.00004089249,0.00002265399,0.00008437556],"study_design_scores_gemma":[0.0007014752,0.0000265384,0.00002787392,0.00003987494,0.00005384798,0.00001351061,0.00002834194,0.9512851,0.04596443,0.0001508069,0.001528533,0.0001796685],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3422651,0.001111455,0.6561375,0.00009863618,0.00009513818,0.0001826741,0.00003544208,0.00005797827,0.00001606661],"genre_scores_gemma":[0.8234155,0.00001355343,0.1762387,0.00006576729,0.000214733,0.000009960236,0.000005766814,0.00003400406,0.000002069133],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.4811504,"threshold_uncertainty_score":0.6222167,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0123081675906366,"score_gpt":0.2249295778545641,"score_spread":0.2126214102639274,"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."}}