Biblio

Found 116 results
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Conference Proceedings
Rubechini F, Schneider A, Arnone A, Cecchi S, Malavasi F.  2009.  A Redesign Strategy to Improve the Efficiency of a 17-Stage Steam Turbine. ASME Turbo Expo 2009: Power for Land, Sea, and Air. 7: Turbomachinery, Parts A and B:1463-1470.
Paper GT2009-60083
Pinelli L, Poli F, Arnone A, D’Ettole A G, Rosso E, Sartor G, Kharyton V.  2024.  The Role of Operating Conditions on Flutter Instability of a Low-Pressure Turbine Rotor. ASME Turbo Expo 2024 Turbomachinery Technical Conference and Exposition. Volume 10A: Structures and Dynamics:V10AT21A014.
Rubechini F, Marconcini M, Arnone A, Scotti Del Greco A, Biagi R.  2013.  Special Challenges in the CFD Modeling of Transonic Turbo-Expanders. ASME Turbo Expo 2013: Turbine Technical Conference and Exposition. 6C: Turbomachinery:V06CT40A016;10pages.
ASME paper GT2013-95554
Boncinelli P, Marconcini M, Poli F, Arnone A, Schipani C.  2006.  Time-Linearized Quasi-3D Tone Noise Computations in Cascade Flows. ASME Turbo Expo. 6, Turbomachinery, Parts A and B:1633–1642.
ASME paper GT2006-90080
Bellucci J, Sazzini F, Rubechini F, Arnone A, Arcangeli L, Maceli N.  2013.  Using CFD to Enhance the Preliminary Design of High-Pressure Steam Turbines. ASME Turbo Expo 2013: Turbine Technical Conference and Exposition. 5B: Oil and Gas Applications; Steam Turbines:V05BT25A004-;11pages.
ASME paper GT2013-94071
Pinelli L, Poli F, Marconcini M, Arnone A, Spano E, Torzo D.  2011.  Validation of a 3D Linearized Method for Turbomachinery Tone Noise Analysis. ASME Turbo Expo. 7: Turbomachinery, Parts A, B, and C :1033-1042.
ASME paper GT2011-45886
Journal Article
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.
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.
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.
Scotti Del Greco A, Biagiotti S, Michelassi V, Jurek T, Di Benedetto D, Francini S, Marconcini M.  2022.  Analysis of Measured and Predicted Turbine Maps From Start-Up to Design Point. ASME J. Turbomach. 144(8):081009.
Checcucci M, Sazzini F, Marconcini M, Arnone A, Coneri M, De Franco L, Toselli M.  2011.  Assessment of a Neural-Network-Based Optimization Tool: a Low Specific-Speed Impeller Application. International Journal of Rotating Machinery. 2011:1-11.
Innocenti G, Marconcini M, Michelassi V, Ciani A, Jurek T, Scotti Del Greco A, Pacciani R.  2025.  On the Assessment of CFD Assumptions for the Preliminary Design of a Two-Stage High-Pressure Turbine: Impact of Unsteady Effects on Thermal Loads. Int. J. Heat Mass Transf. 236(1)
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.
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.
Cozzi L, Rubechini F, Giovannini M, Marconcini M, Arnone A, Schneider A, Astrua P.  2019.  Capturing Radial Mixing in Axial Compressors With Computational Fluid Dynamics. ASME Journal of Turbomachinery. 141(3):031012(9pages).
Berrino M, Bigoni F, Simoni D, Giovannini M, Marconcini M, Pacciani R, Bertini F.  2016.  Combined Experimental and Numerical Investigations on the Roughness Effects on the Aerodynamic Performances of LPT Blades. Journal of Thermal Science. 25(1):32-42.
Fang Y, Zhao Y, Akolekar HD, Ooi ASH, Sandberg RD, Pacciani R, Marconcini M.  2024.  A Data-Driven Approach for Generalizing the Laminar Kinetic Energy Model for Separation and Bypass Transition in Low- and High-Pressure Turbines. ASME J. Turbomach.. 146(9):091005.
TURBO-23-1139
Sláma V, Rudas B, Eret P, Tsymbalyuk V, Ira J, Macalka A, Pinelli L, Vanti F, Arnone A, Lo Balbo A A.  2018.  Experimental and Numerical Study of Controlled Flutter Testing in a Linear Turbine Blade Cascade. Acta Polytechnica CTU Proceedings. Vol 20 Turbomachines 2018:98-107.
Giannini G, Pinelli L, Pacciani R, Arnone A, Bertini F, Spano E, Marconcini M.  Submitted.  The Impact of Modeling Assumptions on the Hot Spots Convection Within a Cooled High-Pressure Turbine Stage. Aerospace Science and Technology.
Metti L, Marconcini M, Salvadori S, Misul DAnna, Rosafio N, Lopes G, Lavagnoli S, Fang Y, Sandberg RD, Pacciani R.  Submitted.  The Impact of Transition and Turbulence Modelling on the SPLEEN High-Speed Low-Pressure Turbine Cascade. ASME J Turbomach.
Metti L, Marconcini M, Salvadori S, Misul DAnna, Rosafio N, Lopes G, Lavagnoli S, Fang Y, Sandberg RD, Pacciani R.  Submitted.  The Impact of Transition and Turbulence Modelling on the SPLEEN High-Speed Low-Pressure Turbine Cascade. ASME J Turbomach.
Pacciani R, Marconcini M, Arnone A, Bertini F, Spano E, Rosa Taddei S, Sandberg RD.  2024.  Improvements in the Prediction of Steady and Unsteady Transition and Mixing in Low Pressure Turbines by Means of Machine-Learnt Closures. ASME J. Turbomach.. 146(5):051009.
Pacciani R, Marconcini M, Arnone A, Bertini F, Spano E, Rosa Taddei S, Sandberg RD.  2024.  Improvements in the Prediction of Steady and Unsteady Transition and Mixing in Low Pressure Turbines by Means of Machine-Learnt Closures. ASME J. Turbomach.. 146(5):051009.
Cozzi L, Rubechini F, Arnone A, Schneider A, Astrua P.  2020.  Improving Steady Computational Fluid Dynamics to Capture the Effects of Radial Mixing in Axial Compressors. ASME J. Turbomach.. 142(9)
Akolekar H, Zhao Y, Sandberg R, Pacciani R.  2021.  Integration of Machine Learning and Computational Fluid Dynamics to Develop Turbulence Models for Improved Low-Pressure Turbine Wake Mixing Prediction. ASME J. Turbomach.. 143(12):121001.

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