{"id":"W2051031955","doi":"10.1016/j.compfluid.2010.12.016","title":"The Navier–Stokes-αβ equations as a platform for a spectral multigrid method to solve the Navier–Stokes equations","year":2010,"lang":"en","type":"article","venue":"Computers & Fluids","topic":"Advanced Numerical Methods in Computational Mathematics","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"U.S. Department of Energy","keywords":"Multigrid method; Navier–Stokes equations; Mathematics; Reynolds-averaged Navier–Stokes equations; Non-dimensionalization and scaling of the Navier–Stokes equations; Hagen–Poiseuille flow from the Navier–Stokes equations; Spectral method; Mathematical analysis; Computational fluid dynamics; Applied mathematics; Physics; Partial differential equation; Compressibility; Mechanics","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001041663,0.0003596826,0.0003474447,0.0001134327,0.0008309807,0.0002619207,0.0008939064,0.0001175164,0.00001705596],"category_scores_gemma":[0.001494865,0.0002542871,0.0002583882,0.0005469685,0.0001474927,0.0002879311,0.0001596886,0.0006129653,0.00009039239],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001305615,"about_ca_system_score_gemma":0.00007317477,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007745396,"about_ca_topic_score_gemma":0.00001618279,"domain_scores_codex":[0.9979587,0.00006519902,0.0006204168,0.000349754,0.0004100662,0.000595885],"domain_scores_gemma":[0.9855569,0.01324999,0.00009183495,0.0006762705,0.0002110556,0.0002139596],"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":[0.00002860069,0.00007378207,0.000003757226,0.00006615544,0.0001907077,0.00000273502,0.002790333,0.3474079,0.01226205,0.327561,0.002773651,0.3068393],"study_design_scores_gemma":[0.0003304883,0.000105884,0.00005311066,0.00004141976,0.00005282729,0.00001970641,0.0001744378,0.8508358,0.002264232,0.1156769,0.03011131,0.000333855],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002302713,0.0002016397,0.9912258,0.001524795,0.002556236,0.001262057,0.00003825647,0.0003917834,0.0004966569],"genre_scores_gemma":[0.02729517,0.00001216491,0.9707425,0.0005291561,0.0007073233,0.0004980452,0.00002081462,0.00008753217,0.0001073309],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.5034279,"threshold_uncertainty_score":0.9999909,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0332605130646763,"score_gpt":0.349743106356164,"score_spread":0.3164825932914876,"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."}}