{"id":"W2612177409","doi":"10.1155/2017/4120862","title":"Active Flow Control in a Radial Vaned Diffuser for Surge Margin Improvement: A Multislot Suction Strategy","year":2017,"lang":"en","type":"article","venue":"International Journal of Rotating Machinery","topic":"Turbomachinery Performance and Optimization","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke","funders":"Office National d'études et de Recherches Aérospatiales; Université de Toulouse","keywords":"Diffuser (optics); Mechanics; Centrifugal compressor; Suction; Reynolds-averaged Navier–Stokes equations; Surge; Gas compressor; Boundary layer suction; Boundary layer; Flow (mathematics); Work (physics); Flow separation; Control theory (sociology); Computational fluid dynamics; Impeller; Computer science; Physics; Meteorology; Thermodynamics; Boundary layer control; Optics","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":[0.0001783738,0.0003816052,0.0003625614,0.0002792205,0.0002006991,0.0004474008,0.0004680132,0.0002169235,0.0007506966],"category_scores_gemma":[0.0002079557,0.0001367484,0.0001734532,0.000107466,0.0003247453,0.0003365978,0.0004929366,0.0002105612,0.000155083],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001997468,"about_ca_system_score_gemma":0.0003199732,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008344138,"about_ca_topic_score_gemma":0.001271473,"domain_scores_codex":[0.9999208,0.00001495019,0.000005621371,0.00001941871,0.00002373032,0.00001547877],"domain_scores_gemma":[0.9998795,0.00002773632,0.00003179127,0.00001117965,0.00002855692,0.0000212164],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0009613039,0.0003053755,0.001585922,0.0002418558,0.00003198289,0.0002222298,0.0001657681,0.1593533,0.7170217,0.003129105,0.0003691895,0.1166122],"study_design_scores_gemma":[0.0001227124,0.001392197,0.001880108,0.00001848956,0.0000234406,0.00009029951,0.00007104533,0.8655751,0.1285647,0.0006363985,0.001594775,0.00003077972],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6848349,0.0003484684,0.3109138,0.00009267961,0.00003276779,0.0000688611,0.00003293047,0.0004072043,0.003268326],"genre_scores_gemma":[0.985892,0.00006235121,0.01321306,0.00001100578,0.000005629656,0.00001251513,0.00001128475,0.000008852623,0.0007833134],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0008344138,"threshold_uncertainty_score":0.002511322,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008332923196352024,"score_gpt":0.255204583920586,"score_spread":0.246871660724234,"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."}}