Biblio

Found 334 results
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low pressure axial turbine
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.
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.
low pressure steam turbine blade
Bessone A, Carassale L, Guida R, Kubin Z, Lo Balbo A A, Marrè Bruneghi M, Pinelli L.  2018.  Experimental and Numerical Assessment of a Downscaled Steam Turbine Last Stage Blade Damping at Low Load Operation. Acta Polytechnica CTU Proceedings. Vol 20 Turbomachines 2018:16-28.
Low Pressure Turbine; low Reynolds number aerodynamics; Off Design performance; Wake-Induced Transition
Marconcini M, Pacciani R, Arnone A, Bertini F.  2015.  Low-Pressure Turbine Cascade Performance Calculations With Incidence Variation and Periodic Unsteady Inflow Conditions. ASME Turbo Expo 2015: Turbine Technical Conference and Exposition. 2A: Turbomachinery
ASME paper GT2015–42276
low-pressure turbine; wake mixing; transition; machine learning; explicit algebraic Reynolds stress model; laminar kinetic energy
Pacciani R, Marconcini M, Bertini F, Rosa Taddei S, Spano E, Zhao Y, Akolekar H, Sandberg R, Arnone A.  2021.  Assessment of Machine-Learned Turbulence Models Trained for Improved Wake-Mixing in Low Pressure Turbine Flows. Energies. 14(24):8327.
LPT
Arnone A, Marconcini M, Pacciani R, Spano E.  1999.  Numerical Prediction of Wake–Induced Transition in a Low Pressure Turbine. 14th ISABE Symposium.
September 5–10, Florence, Italy, ISABE paper 99–058
Arnone A, Bonaiuti D, Focacci A, Pacciani R, Scotti Del Greco A, Spano E.  2004.  Parametric Optimization of a High-Lift Turbine Vane. Conf. Proc. Vol. 5: Turbo Expo 2004, Parts A and B. :1469–1479.
June 14–17, Vienna, Austria, ASME paper GT2004-54308
Pacciani R, Spano E.  2006.  Numerical Investigation of the Effect of Roughness and Passing Wakes on LP Turbine Blades Performance. ASME Turbo Expo. Conf. Proc. Vol. 6: Turbomachinery, Parts A and B:1713–1722.
May 8–11, Barcelona, Spain, ASME paper GT2006-90221
Pacciani R, Marconcini M, Bertini F.  2009.  Assessment of an Intermittency Based Approach to Separated Flow Transition Predictions in Low-Reynolds Number High-Lift Cascade Flow. 8th European Turbomachinery Conference (ETC8). :141-151.
23-27 March, Graz, Austria
Marconcini M, Rubechini F, Pacciani R, Arnone A, Bertini F.  2012.  Redesign of High-Lift Low Pressure Turbine Airfoils For Low Speed Testing. ASME Journal of Turbomachinery. 134(5):051017.
Marconcini M, Pacciani R, Arnone A.  2010.  URANS Predictions of the Effects of Synthetic Jets on the Separated, Transitional Flow Over a Low-Pressure-Turbine-Like Flat Plate. ASME Turbo Expo. 7: Turbomachinery, Parts A, B and C:2605–2613.
ASME paper GT2010-23297
Pacciani R, Marconcini M, Arnone A, Bertini F.  2010.  Sull'impiego del concetto di energia cinetica laminare per la previsione della transizione per separazione in profili high-lift per turbine aeronautiche di bassa pressione. 65o Congresso Nazionale ATI.
Domus de Maria (CA), Italy, 13-17 Settembre 2010, articolo 12-084
Pacciani R, Marconcini M, Arnone A, Bertini F.  2011.  An Assessment of the Laminar Kinetic Energy Concept for the Prediction of High-Lift, Low-Reynolds Number Cascade Flows. Proceedings of the Institution of Mechanical Engineers, Part A: Journal of Power and Energy. 225(7):995-1003.
winner of 2011 SAGE Best Paper Award (JPE) http://pia.sagepub.com/cgi/collection/2011
Pacciani R, Marconcini M, Fadai-Ghotbi A, Lardeau S, Leschziner MA.  2011.  Calculation of High-Lift Cascades in Low Pressure Turbine Conditions Using a Three-Equation Model. ASME Journal of Turbomachinery. 133(3):031016.
Pacciani R, Marconcini M, Arnone A, Bertini F.  2014.  Predicting High-Lift Low-Pressure Turbine Cascades Flows Using Transition-Sensitive Turbulence Closures. ASME Journal of Turbomachinery. 136(5):051007.
Pinelli L, Poli F, Di Grazia E, Arnone A, Torzo D.  2013.  A Comprehensive Numerical Study of Tone Noise Emissions in a Multistage Cold Flow Rig. 19th AIAA/CEAS Aeroacoustics Conference (34th AIAA Aeroacoustics Conference), 27-29 May.
AIAA paper 2013-2104
Pinelli L, Poli F, Di Grazia E, Arnone A, Torzo D.  2013.  A Comprehensive Numerical Study of Tone Noise Emissions in a Multistage Cold Flow Rig. 19th AIAA/CEAS Aeroacoustics Conference (34th AIAA Aeroacoustics Conference), 27-29 May.
AIAA paper 2013-2104
Marconcini M, Pacciani R, Arnone A, Michelassi V, Pichler R, Zhao Y, Sandberg R.  2019.  Large Eddy Simulation and RANS Analysis of the End-Wall Flow in a Linear Low-Pressure-Turbine Cascade - Part II: Loss Generation. ASME Journal of Turbomachinery. 141(5):051004(9pages).
Marconcini M, Pacciani R, Arnone A, Michelassi V, Pichler R, Zhao Y, Sandberg R.  2019.  Large Eddy Simulation and RANS Analysis of the End-Wall Flow in a Linear Low-Pressure-Turbine Cascade - Part II: Loss Generation. ASME Journal of Turbomachinery. 141(5):051004(9pages).
Pichler R, Zhao Y, Sandberg R, Michelassi V, Pacciani R, Marconcini M, Arnone A.  2019.  Large-Eddy Simulation and RANS Analysis of the End-Wall Flow in a Linear Low-Pressure Turbine Cascade, Part I: Flow and Secondary Vorticity Fields Under Varying Inlet Condition. ASME Journal of Turbomachinery. 141(12):121005(10pages).
Pichler R, Zhao Y, Sandberg R, Michelassi V, Pacciani R, Marconcini M, Arnone A.  2019.  Large-Eddy Simulation and RANS Analysis of the End-Wall Flow in a Linear Low-Pressure Turbine Cascade, Part I: Flow and Secondary Vorticity Fields Under Varying Inlet Condition. ASME Journal of Turbomachinery. 141(12):121005(10pages).
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.

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