Biblio

Found 12 results
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machine learning; multi-objective optimization; low pressure turbine; transition; turbulence modeling
Akolekar H, Waschkowski F, Zhao Y, Pacciani R, Sandberg R.  2021.  Transition Modeling for Low Pressure Turbines Using Computational Fluid Dynamics Driven Machine Learning. Energies. 14(15):4680.
meta-model
Checcucci M, Schneider A, Marconcini M, Rubechini F, Arnone A, De Franco L, Coneri M.  2015.  A Novel Approach to Parametric Design of Centrifugal Pumps for a Wide Range of Specific Speeds. 12th International Symposium on Experimental and Computational Aerothermodynamics of Internal Flows.
paper n.121
mistuning
Biagiotti S, Pinelli L, Poli F, Vanti F, Pacciani R.  2018.  Numerical Study of Flutter Stabilization in Low Pressure Turbine Rotor with Intentional Mistuning. ATI 2018 - 73rd Conference of the Italian Thermal Machines Engineering Association.. Energy Procedia 148:98-105.
Modeling
Cozzi L, Rubechini F, Arnone A, Schneider A, Astrua P.  2019.  Improving Steady CFD to Capture the Effects of Radial Mixing in Axial Compressors. ASME Turbo Expo 2019. Volume 2C: Turbomachinery:V02CT41A016.
ASME paper GT2019-90363
Maceli N, Arcangeli L, Arnone A.  2021.  Two Phase Flow CFD Modeling of a Steam Turbine Low Pressure Section: Comparison With Data and Correlations. ASME Turbo Expo 2021 Turbomachinery Technical Conference and Exposition. 8: Oil and Gas Applications; Steam Turbine
ASME paper GT2021-59645