{"id":"W4376638582","doi":"10.1016/j.powtec.2023.118652","title":"In-depth validation of unresolved CFD-DEM simulations of liquid fluidized beds","year":2023,"lang":"en","type":"article","venue":"Powder Technology","topic":"Granular flow and fluidized beds","field":"Engineering","cited_by":31,"is_retracted":false,"has_abstract":false,"ca_institutions":"Polytechnique Montréal","funders":"Financiadora de Estudos e Projetos; Polytechnique Montréal; Conselho Nacional de Desenvolvimento Científico e Tecnológico; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior; Fundação de Amparo à Pesquisa do Estado de São Paulo; Natural Sciences and Engineering Research Council of Canada; Ministério da Ciência, Tecnologia e Inovação","keywords":"Computational fluid dynamics; Drag; Mechanics; Lift (data mining); Inlet; Fluidization; Work (physics); CFD-DEM; Range (aeronautics); Dispersion (optics); Fluidized bed; Flow (mathematics); Physics; Thermodynamics; Aerospace engineering; Engineering; Mechanical engineering; Optics; Computer science","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":[0.0006591546,0.0008003894,0.0009649406,0.000619668,0.0007983308,0.001197862,0.001464482,0.001687563,0.004034656],"category_scores_gemma":[0.00296527,0.000369081,0.0005407368,0.0006003352,0.001021655,0.0009868137,0.001228564,0.00121721,0.0004718737],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008890468,"about_ca_system_score_gemma":0.00124074,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0121625,"about_ca_topic_score_gemma":0.006046725,"domain_scores_codex":[0.9996058,0.00007584059,0.00003119362,0.00006434586,0.0001253103,0.00009740985],"domain_scores_gemma":[0.998768,0.0005678363,0.00008894245,0.0001984424,0.0002706631,0.0001060896],"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":[0.0003205626,0.0003630175,0.003567724,0.0001481171,0.00003182532,0.0002248488,0.0001240024,0.9765567,0.008736007,0.002246321,0.0006357626,0.007045168],"study_design_scores_gemma":[0.00005548432,0.00005494313,0.0008291171,0.000009122401,0.000004616153,0.00001526788,0.0000384103,0.9952629,0.003141154,0.0002808289,0.00029861,0.00000945325],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9461817,0.000363473,0.02928726,0.0005500403,0.0001808255,0.0001225775,0.002275067,0.0013801,0.01965898],"genre_scores_gemma":[0.9931206,0.00007042129,0.005288619,0.00005483485,0.00001369245,0.00002127453,0.0006248613,0.00004415072,0.0007616337],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0121625,"threshold_uncertainty_score":0.02418345,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01394990727380549,"score_gpt":0.2501838654597439,"score_spread":0.2362339581859384,"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."}}