Biblio

Found 13 results
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Filters: Author is Yaomin Zhao  [Clear All Filters]
Journal Article
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.
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).
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).
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.
Fang Y, Reissmann M, Pacciani R, Zhao Y, Ooi ASH, Marconcini M, Akolekar HD, Sandberg RD.  Submitted.  Exploiting a Transformer Architecture to Simultaneous Development of Transition and Turbulence Models for Turbine Flow Predictions. ASME J Turbomach.
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
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.
Conference Proceedings
Pichler R, Zhao Y, Sandberg R, Michelassi V, Pacciani R, Marconcini M, Arnone A.  2018.  LES and RANS Analysis of the End-Wall Flow in a Linear LPT Cascade: Part I — Flow and Secondary Vorticity Fields Under Varying Inlet Condition. ASME Turbo Expo 2018: Turbine Technical Conference and Exposition. Volume 2B: Turbomachinery:pp.V02BT41A020;11pages.
paper GT2018-76233
Marconcini M, Pacciani R, Arnone A, Michelassi V, Pichler R, Zhao Y, Sandberg R.  2018.  LES and RANS analysis of the end-wall flow in a linear LPT cascade with variable inlet conditions, Part II: Loss generation. ASME Turbo Expo 2018: Turbine Technical Conference and Exposition. Volume 2B: Turbomachinery:pp.V02BT41A024;11pages.
paper GT2018-76450
Fang Y, Reissmann M, Pacciani R, Zhao Y, Ooi A, Marconcini M, Akolekar H, Sandberg R.  2024.  Exploiting a Transformer Architecture to Simultaneous Development of Transition and Turbulence Models for Turbine Flow Predictions. ASME Turbo Expo 2024 Turbomachinery Technical Conference and Exposition. Volume 12C: Turbomachinery:V12CT32A023.
GT2024-125550
Conference Paper
Akolekar H, Waschkowski F, Sandberg R, Pacciani R, Zhao Y.  2022.  Multi-Objective Development of Machine-Learnt Closures for Fully Integrated Transition and Wake Mixing Predictions in Low Pressure Turbines. ASME Turbo Expo 2022 Turbomachinery Technical Conference and Exposition. Volume 10C: Turbomachinery
ASME paper GT2022-81091
Akolekar H, Zhao Y, Sandberg R, Pacciani R.  2020.  Integration of Machine Learning and Computational Fluid Dynamics to Develop Turbulence Models for Improved Turbine Wake Mixing Prediction. ASME Turbo Expo 2020 Turbomachinery Technical Conference and Exposition. 2C: Turbomachinery:10.
paper GT2020-14732
Fang Y, Zhao Y, Akolekar HD, Ooi ASH, Sandberg RD, Pacciani R, Marconcini M.  2023.  A Data-Driven Approach for Generalizing the Laminar Kinetic Energy Model for Separation and Bypass Transition in Low- and High-Pressure Turbines. ASME Turbo Expo 2023 Turbomachinery Technical Conference and Exposition. 13C: Turbomachinery
GT2023-102902