Biblio

Found 274 results
Author [ Keyword(Desc)] Type Year
Filters: Author is Michele Marconcini  [Clear All Filters]
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Generalized Shape Correction Method
Giovannini M, Marconcini M, Arnone A, Bertini F.  2014.  Evaluation of Unsteady Computational Fluid Dynamics Models Applied to the Analysis of a Transonic High-Pressure Turbine Stage. Proceedings of the Institution of Mechanical Engineers, Part A: Journal of Power and Energy. 228(7):813-824.
Grid dependence
Arnone A, Carnevale E A, Marconcini M.  1997.  Grid Dependency Study for the NASA Rotor 37 Compressor Blade. ASME 1997 International Gas Turbine and Aeroengine Congress and Exhibition. 1: Aircraft Engine; Marine; Turbomachinery; Microturbines and Small Turbomachinery:pp.V001T03A056;(9pages).
Harmonic Balance
Cavazzini A, Campobasso MS, Marconcini M, Pacciani R, Arnone A.  2019.  Harmonic balance Navier-Stokes analysis of tidal stream turbine wave loads. Recent Advances in CFD for Wind and​ Tidal Offshore Turbines. :pp37-49.
High Lift
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.
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.  2012.  URANS Analysis of Wake-Induced Effects in High-Lift, Low Reynolds Number Cascade Flows. ASME Turbo Expo 2012: Turbine Technical Conference and Exposition. 8: Turbomachinery, Parts A, B, and C:1521-1530.
ASME paper GT2012-69479.
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.
High-performance computing
Savio P, Scionti A, Vitali G, Viviani P, Vercellino C, Terzo O, Nguyen H-N, Magarielli D, Spano E, Marconcini M et al..  2023.  Accelerating Legacy Applications with Spatial Computing Devices. Journal of Supercomputing. 79:7461–7483.
Hill Chart
Arnone A, Marconcini M, Rubechini F, Schneider A, Alba G.  2009.  Kaplan Turbine Performance Prediction Using CFD: an Artificial Neural Network Approach. HYDRO 2009 Conference Proceedings.
Lyon, France, 26-28 October 2009, paper n.263
HP turbine
Burberi C, Ghignoni E, Pinelli L, Marconcini M.  2018.  Numerical Analysis of Direct and Indirect Noise Produced by a High Pressure Turbine Stage. ATI 2018 - 73rd Conference of the Italian Thermal Machines Engineering Association. Energy Procedia 148:130-137.
Impellers
Agnolucci A, Marconcini M, Arnone A, Toni L, Grimaldi A, Giachi M.  2021.  Centrifugal Compressor Stage Efficiency and Rotor Stiffness Augmentation via Artificial Neural Networks. ASME Turbo Expo 2021 Turbomachinery Technical Conference and Exposition. 2D: Turbomachinery: Radial Turbomachinery Aerodynamics
ASME paper GT2021-59998
Kacker-Okapuu
Bertini F, Ampellio E, Marconcini M, Giovannini M.  2013.  A Critical Numerical Review of Loss Correlation Models and Smith Diagram for Modern Low Pressure Turbine Stages. ASME Turbo Expo 2013: Turbine Technical Conference and Exposition. 6B: Turbomachinery:V06BT37A018-;14pages.
ASME paper GT2013-94849
Kaplan Turbine
Arnone A, Marconcini M, Rubechini F, Schneider A, Alba G.  2009.  Kaplan Turbine Performance Prediction Using CFD: an Artificial Neural Network Approach. HYDRO 2009 Conference Proceedings.
Lyon, France, 26-28 October 2009, paper n.263
Laminar kinetic energy
Pacciani R, Fang Y, Metti L, Marconcini M, Sandberg RD.  2024.  A Reformulation of the Laminar Kinetic Energy Model to Enable Multi-Mode Transition Predictions.. Flow, Turbulence and Combustion. s10494-024-00590-y

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