{"id":"W4399557112","doi":"10.1016/j.applthermaleng.2024.123599","title":"Hierarchical thermal modeling and surrogate-model-based design optimization framework for cold plates used in battery thermal management systems","year":2024,"lang":"en","type":"article","venue":"Applied Thermal Engineering","topic":"Advanced Battery Technologies Research","field":"Engineering","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canada Research Chairs; University of Toronto","funders":"","keywords":"Thermal management of electronic devices and systems; Surrogate model; Battery (electricity); Thermal; Engineering; Mechanical engineering; Computer science; Systems engineering; Reliability engineering; Thermodynamics","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":[0.0008095006,0.001054565,0.0008517429,0.0005200303,0.0004192327,0.001093535,0.0008372049,0.0009247772,0.002793056],"category_scores_gemma":[0.001133925,0.0006494048,0.001208041,0.0003843918,0.0006722532,0.0006448246,0.0008206243,0.001082442,0.0004651563],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001060203,"about_ca_system_score_gemma":0.001851241,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006473519,"about_ca_topic_score_gemma":0.006974006,"domain_scores_codex":[0.9996251,0.0001188901,0.0000124083,0.00004663474,0.0001529122,0.00004411993],"domain_scores_gemma":[0.999663,0.000153275,0.00004220544,0.00002956012,0.00009085087,0.00002113433],"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":[0.000004836912,0.000004190297,0.00006996894,0.00001408698,0.000003882797,0.00000947936,0.000006000348,0.9962564,0.0004502852,0.001533678,0.00007957972,0.001567652],"study_design_scores_gemma":[0.000001901104,0.000007846517,0.00002621731,0.000002993202,0.000002269797,0.000002642963,0.000003126765,0.9986991,0.00016342,0.0007892184,0.0002998264,0.000001469543],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0164358,0.0003946912,0.972522,0.0001886493,0.00003004721,0.00006087778,0.0001586882,0.0003302368,0.009878986],"genre_scores_gemma":[0.7936145,0.0006381467,0.1949378,0.000168779,0.00004010038,0.0005528754,0.0005216931,0.0002466653,0.009279345],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006473519,"threshold_uncertainty_score":0.01287168,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02015583022796887,"score_gpt":0.2410080700458816,"score_spread":0.2208522398179127,"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."}}