{"id":"W4283811060","doi":"10.1115/1.4054943","title":"Computational Fluid Dynamics Modeling of a Pressurized Water Reactor Fuel Assembly to Estimate Loss Coefficients in Support of Subchannel Thermalhydraulics Modeling of Pressurized Water Reactor Small Modular Reactors With Advanced Fuels","year":2022,"lang":"en","type":"article","venue":"Journal of Nuclear Engineering and Radiation Science","topic":"Heat transfer and supercritical fluids","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Nuclear Laboratories","funders":"","keywords":"Computational fluid dynamics; Pressurized water reactor; Coolant; Nuclear engineering; Bundle; Modular design; Reynolds number; Materials science; Flow (mathematics); Turbulence; Loss-of-coolant accident; Mixing (physics); Pressure drop; Grid; Light-water reactor; Mechanics; Environmental science; Mechanical engineering; Computer science; Engineering; Mathematics; Physics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.000174738,0.0004667464,0.0003542048,0.0003577087,0.000412723,0.0005561952,0.0004810248,0.0006238082,0.001147328],"category_scores_gemma":[0.0003839224,0.0002729766,0.0005290615,0.0002412183,0.0003014349,0.0003120156,0.0002679008,0.0004339412,0.0001760332],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005221683,"about_ca_system_score_gemma":0.0007617199,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0128554,"about_ca_topic_score_gemma":0.006426008,"domain_scores_codex":[0.9999248,0.00001396889,0.000003981932,0.00001698485,0.00002470035,0.00001561678],"domain_scores_gemma":[0.9998093,0.00009764511,0.00002646538,0.00001485633,0.00003731064,0.00001449134],"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":[0.00003635456,0.00003077742,0.001804091,0.00001607316,0.000006282436,0.00006788772,0.00002573225,0.9857892,0.009671054,0.0003153373,0.00008311537,0.002154078],"study_design_scores_gemma":[0.00000207121,0.00001163101,0.0003530221,9.366831e-7,8.899364e-7,0.000002996506,0.00000422733,0.9980105,0.001515287,0.00002525777,0.00007157573,0.000001660104],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9279997,0.00008167993,0.06659061,0.00008650848,0.00002518669,0.00006210747,0.0004507786,0.0003424401,0.004361035],"genre_scores_gemma":[0.9890987,0.00004810355,0.009151164,0.000008556469,0.000002617781,0.00005965183,0.0001759541,0.00002196047,0.001433269],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0128554,"threshold_uncertainty_score":0.02556115,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008980719370015608,"score_gpt":0.2246598155388288,"score_spread":0.2156790961688132,"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."}}