{"id":"W2719560105","doi":"10.1299/jsmeicone.2015.23._icone23-1_439","title":"ICONE23-1934 A FORMULATION OF ROD BASED NONLINEAR MODEL PREDICTIVE CONTROL OF NUCLEAR REACTION WITH TEMPERATURE EFFECTS AND XENON POISONING","year":2015,"lang":"en","type":"article","venue":"The Proceedings of the International Conference on Nuclear Engineering (ICONE)","topic":"Advanced Control Systems Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Model predictive control; Optimal control; Nonlinear system; Control theory (sociology); Gradient descent; Hamiltonian (control theory); Temperature control; Descent (aeronautics); Controller (irrigation); Applied mathematics; Mathematics; Computer science; Mathematical optimization; Engineering; Physics; Control (management); Thermodynamics; Artificial neural network","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":[0.000374994,0.0008458235,0.0005689155,0.0002215273,0.0002652242,0.0009491886,0.0007995056,0.001015501,0.003837703],"category_scores_gemma":[0.0003803732,0.0002388574,0.0004952722,0.0002397076,0.0005685369,0.0004854829,0.0005045881,0.0009095802,0.0004917287],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007685727,"about_ca_system_score_gemma":0.0008731912,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007378862,"about_ca_topic_score_gemma":0.006670739,"domain_scores_codex":[0.9998227,0.00004115243,0.000007029214,0.00003304746,0.00008258536,0.00001352364],"domain_scores_gemma":[0.9999079,0.00002174629,0.00001362398,0.00001074752,0.00003971346,0.000006417998],"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":[0.00004073961,0.00002650122,0.00008979846,0.00008516799,0.00001841771,0.0001010641,0.00003697276,0.9221829,0.00489882,0.05840017,0.001668397,0.01245097],"study_design_scores_gemma":[0.000006863723,0.00005234961,0.00005508583,0.000007276758,0.00000504867,0.00000941247,0.000005445873,0.9910687,0.0006928638,0.00458248,0.003509023,0.000005332194],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00762858,0.0004672575,0.9622343,0.0003725301,0.0002272998,0.00006868112,0.0001782573,0.0001627756,0.02866039],"genre_scores_gemma":[0.7738386,0.001048681,0.1535129,0.0003109283,0.0002385105,0.0006187235,0.0004940812,0.0001340818,0.06980342],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007378862,"threshold_uncertainty_score":0.01467186,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00884501696826721,"score_gpt":0.186829419607283,"score_spread":0.1779844026390158,"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."}}