{"id":"W4210696038","doi":"10.3390/en15030960","title":"A Redesign Methodology to Improve the Performance of a Thermal Energy Storage with Phase Change Materials: A Numerical Approach","year":2022,"lang":"en","type":"article","venue":"Energies","topic":"Phase Change Materials Research","field":"Engineering","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"Secretaría Nacional de Ciencia, Tecnología e Innovación","keywords":"Thermal energy storage; Phase-change material; Heat exchanger; Process engineering; Phase change; Thermal; Environmental science; Energy storage; Thermal energy; Thermal conductivity; Efficient energy use; Nuclear engineering; Materials science; Computer science; Mechanical engineering; Engineering; Meteorology; Thermodynamics; Electrical engineering; Engineering physics; Composite material","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":[0.0004343994,0.0005171978,0.0004562687,0.0004341274,0.0003535159,0.0005439584,0.0007233491,0.0006155478,0.001944486],"category_scores_gemma":[0.0008265167,0.0003396325,0.0006287142,0.0002893296,0.0004103205,0.0004939924,0.0003441945,0.0005890738,0.0003171999],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005103503,"about_ca_system_score_gemma":0.0007382945,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002048005,"about_ca_topic_score_gemma":0.002009733,"domain_scores_codex":[0.999846,0.00002241651,0.000008808268,0.00002407382,0.00007630402,0.00002236428],"domain_scores_gemma":[0.9998103,0.0000640795,0.00002888317,0.00002853333,0.00006094162,0.000007417324],"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":[0.00008149639,0.0001588892,0.001228925,0.0004851343,0.00003153198,0.000187723,0.0001330219,0.8305212,0.09603224,0.01119351,0.0006250074,0.05932136],"study_design_scores_gemma":[0.00001344104,0.0001099289,0.000165992,0.00001340634,0.0000117821,0.00003898595,0.00002286546,0.9846836,0.01152956,0.0008902595,0.002510387,0.000009788657],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1595877,0.0006138238,0.8252549,0.0002814902,0.0001618515,0.0003579814,0.0001464138,0.0008897142,0.01270609],"genre_scores_gemma":[0.6056113,0.0004059721,0.3903275,0.0000478154,0.00001596868,0.0003840858,0.0001137164,0.00009444599,0.002999317],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002048005,"threshold_uncertainty_score":0.006504953,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09617033932353038,"score_gpt":0.3030625233906665,"score_spread":0.2068921840671361,"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."}}