{"id":"W3080167145","doi":"10.1139/tcsme-2020-0080","title":"Design of a nanocoated heat exchanger working with organic nanofluids using a hybrid technique","year":2020,"lang":"en","type":"article","venue":"Transactions of the Canadian Society for Mechanical Engineering","topic":"Nanofluid Flow and Heat Transfer","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Nanofluid; Adaptive neuro fuzzy inference system; Thermal conductivity; Materials science; Particle swarm optimization; Heat exchanger; Heat transfer; Heat transfer coefficient; Artificial neural network; Reynolds number; Composite material; Nanoparticle; Mechanics; Mechanical engineering; Thermodynamics; Computer science; Engineering; Fuzzy logic; Algorithm; Nanotechnology; Artificial intelligence; Fuzzy control system; Physics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002723122,0.0002784671,0.0003524371,0.000192328,0.000317708,0.0003265864,0.0005899191,0.0005322037,0.0007038115],"category_scores_gemma":[0.0002027046,0.0002094545,0.000381537,0.0000980087,0.0002491047,0.0004327602,0.0002326873,0.0002239149,0.000170148],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002846115,"about_ca_system_score_gemma":0.0003612739,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008274197,"about_ca_topic_score_gemma":0.00117694,"domain_scores_codex":[0.9998728,0.00001321479,0.000008350667,0.00004169242,0.00004984907,0.00001415506],"domain_scores_gemma":[0.9999058,0.00002705166,0.00001999952,0.00000862125,0.00003011705,0.000008354798],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002740496,0.0001612066,0.001393326,0.0003318161,0.00005340388,0.0002044821,0.0001355348,0.2066295,0.7332923,0.002647809,0.0003273477,0.05454911],"study_design_scores_gemma":[0.00002931828,0.0003292086,0.0006762206,0.000008980445,0.00002544114,0.00007629878,0.00002706971,0.8659534,0.1308458,0.0003385652,0.00167087,0.00001880601],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.229345,0.0003785574,0.7658175,0.0001500083,0.0001093006,0.0001515158,0.00004502055,0.0004877081,0.003515417],"genre_scores_gemma":[0.8520115,0.0001571195,0.1452374,0.00003829341,0.0000244541,0.0001650307,0.00003397895,0.0000179158,0.002314329],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0008274197,"threshold_uncertainty_score":0.002354503,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02676711926292494,"score_gpt":0.190610131669177,"score_spread":0.163843012406252,"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."}}