{"id":"W3014375872","doi":"10.3390/en13071638","title":"High Reynold’s Number Turbulent Model for Micro-Channel Cold Plate Using Reverse Engineering Approach for Water-Cooled Battery in Electric Vehicles","year":2020,"lang":"en","type":"article","venue":"Energies","topic":"Advanced Battery Technologies Research","field":"Engineering","cited_by":91,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"University of Waterloo","keywords":"Coolant; Battery (electricity); Turbulence; Water cooling; Materials science; Mechanical engineering; Heat flux; Thermal; Mass flow rate; Nuclear engineering; Air cooling; Lithium-ion battery; Mechanics; Heat transfer; Engineering; Thermodynamics; Power (physics); Physics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0003820143,0.0009262366,0.0008233845,0.000668856,0.0008140687,0.001232679,0.00129259,0.001711139,0.002834959],"category_scores_gemma":[0.0006955327,0.0004869328,0.001156794,0.0003948576,0.001159001,0.0009400469,0.0007745303,0.001259259,0.0005603747],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001407788,"about_ca_system_score_gemma":0.001843677,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0267818,"about_ca_topic_score_gemma":0.0139191,"domain_scores_codex":[0.9998146,0.00004391707,0.000009266894,0.00003110773,0.00005834068,0.00004281206],"domain_scores_gemma":[0.9997613,0.00008037658,0.0000344861,0.00001193266,0.00008647575,0.00002543209],"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":[0.00003587733,0.00003287655,0.0006334024,0.0000735807,0.00001236146,0.0001325959,0.0000599816,0.9785107,0.003634478,0.01404557,0.0005197544,0.002308868],"study_design_scores_gemma":[0.000003200915,0.000005326732,0.00005352154,0.000003559835,0.000001548412,0.000006098067,0.000008168991,0.9989088,0.0002076198,0.0004069373,0.0003911599,0.000004166039],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1170743,0.002521082,0.8264473,0.0009075383,0.0004947425,0.0003081808,0.0005505464,0.0005414652,0.05115495],"genre_scores_gemma":[0.9083242,0.001402102,0.04177242,0.0001779562,0.0001017746,0.0005548802,0.000395023,0.0002152096,0.04705646],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0267818,"threshold_uncertainty_score":0.0532518,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02825881869514909,"score_gpt":0.2360504708844639,"score_spread":0.2077916521893148,"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."}}