{"id":"W7101390974","doi":"10.1016/j.energy.2025.139033","title":"Multi-objective optimization of supercritical carbon dioxide Brayton cycles using Bayesian physics-informed neural networks: A comprehensive analysis of energy, exergy, economic, and environmental performance","year":2025,"lang":"en","type":"article","venue":"Energy","topic":"Thermodynamic and Exergetic Analyses of Power and Cooling Systems","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia, Okanagan Campus; University of British Columbia","funders":"","keywords":"Brayton cycle; Artificial neural network; Bayesian optimization; Gas compressor; Exergy; Sensitivity (control systems); Consistency (knowledge bases); Heat exchanger; Cost of electricity by source","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":[0.001549258,0.001181195,0.001344034,0.001064779,0.0005799679,0.001367657,0.0007749559,0.001770114,0.001364511],"category_scores_gemma":[0.003014438,0.0008585557,0.0007697149,0.0006246051,0.0009005522,0.001013701,0.0008191305,0.0008960073,0.0001137014],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002000782,"about_ca_system_score_gemma":0.00260206,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04171272,"about_ca_topic_score_gemma":0.03692869,"domain_scores_codex":[0.9997534,0.00009593173,0.00000999993,0.00003703715,0.00005555429,0.00004804615],"domain_scores_gemma":[0.9985856,0.001080014,0.00009871783,0.00002771723,0.0001537397,0.00005433099],"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":[0.00001813553,0.00001497224,0.0001302648,0.000007622893,0.000009394281,0.000005054484,0.000003110012,0.9983035,0.00008542134,0.0003088089,0.00003532621,0.001078356],"study_design_scores_gemma":[0.00000251441,0.000007003235,0.00007161942,0.000001005295,0.000001492799,4.125819e-7,0.000001560591,0.9997181,0.00005327467,0.0001247794,0.00001706077,0.000001152052],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8388475,0.001542926,0.1425088,0.0009206629,0.00008102104,0.0001679527,0.0003404763,0.0002436265,0.0153471],"genre_scores_gemma":[0.9912243,0.0001372542,0.00635165,0.00004312674,0.00001109181,0.00006511166,0.0001100333,0.00002451536,0.002032893],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04171272,"threshold_uncertainty_score":0.08293986,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005808999218141658,"score_gpt":0.2099583669216408,"score_spread":0.2041493677034992,"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."}}