{"id":"W2794789976","doi":"10.1016/j.jnnfm.2018.03.017","title":"Distinct transition in flow statistics and vortex dynamics between low- and high-extent turbulent drag reduction in polymer fluids","year":2018,"lang":"en","type":"article","venue":"Journal of Non-Newtonian Fluid Mechanics","topic":"Rheology and Fluid Dynamics Studies","field":"Chemical Engineering","cited_by":30,"is_retracted":false,"has_abstract":false,"ca_institutions":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada; Kavli Institute for Theoretical Physics, University of California, Santa Barbara; Compute Canada; University of California, Santa Barbara; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; National Science Foundation","keywords":"Drag; Turbulence; Mechanics; Parasitic drag; Vortex; Reduction (mathematics); Direct numerical simulation; Drag coefficient; Physics; Statistical physics; Classical mechanics; Materials science; Mathematics; Reynolds number; Geometry","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":[0.00007421093,0.00007112771,0.00009359271,0.0003025151,0.0001749526,0.000308014,0.00008564432,0.000107653,0.0007776642],"category_scores_gemma":[0.0002929083,0.0001020799,0.0001022107,0.0001074459,0.0003033367,0.000268963,0.0002270154,0.000238338,0.00009002369],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000846878,"about_ca_system_score_gemma":0.0001376019,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002312882,"about_ca_topic_score_gemma":0.0003437272,"domain_scores_codex":[0.9999696,0.000003883675,0.00000160648,0.000006593664,0.00000676436,0.00001161787],"domain_scores_gemma":[0.9998555,0.00003763605,0.00002868983,0.00001205218,0.00001836379,0.00004774155],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.00115413,0.0004590699,0.03533678,0.00005997163,0.00003591549,0.0002096084,0.0004474803,0.005734497,0.9334518,0.003318447,0.0003007999,0.0194915],"study_design_scores_gemma":[0.0000581704,0.0006017537,0.5462343,0.00001469073,0.00004615026,0.0002711853,0.0004283982,0.08589748,0.3637639,0.001776211,0.0008509207,0.00005688241],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9991125,0.00002524673,0.000553739,0.00000778305,0.000001056527,0.000001454963,0.00001267603,0.00001234264,0.000273069],"genre_scores_gemma":[0.9997411,0.000009590729,0.000115817,0.000001966785,8.738159e-7,0.000001243217,0.00001421646,0.000002219245,0.0001129347],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0007776642,"threshold_uncertainty_score":0.002601564,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004836245703163243,"score_gpt":0.21703523170404,"score_spread":0.2121989860008768,"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."}}