| RANS turbulence model development using CFD-driven machine learning Y Zhao, HD Akolekar, J Weatheritt, V Michelassi, RD Sandberg Journal of Computational Physics 411, 109413, 2020 | 263 | 2020 |
| 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 R Pichler, Y Zhao, R Sandberg, V Michelassi, R Pacciani, M Marconcini, ... Journal of turbomachinery 141 (12), 121005, 2019 | 67* | 2019 |
| Large eddy simulation and RANS analysis of the end-wall flow in a linear low-pressure-turbine cascade—Part II: Loss generation M Marconcini, R Pacciani, A Arnone, V Michelassi, R Pichler, Y Zhao, ... Journal of Turbomachinery 141 (5), 051004, 2019 | 57* | 2019 |
| Data-driven scalar-flux model development with application to jet in cross flow J Weatheritt, Y Zhao, RD Sandberg, S Mizukami, K Tanimoto International Journal of Heat and Mass Transfer 147, 118931, 2020 | 51 | 2020 |
| Multi-objective CFD-driven development of coupled turbulence closure models F Waschkowski, Y Zhao, R Sandberg, J Klewicki Journal of Computational Physics 452, 110922, 2022 | 50 | 2022 |
| Bypass transition in boundary layers subject to strong pressure gradient and curvature effects Y Zhao, RD Sandberg Journal of Fluid Mechanics 888, A4, 2020 | 47 | 2020 |
| Vortex reconnection in the late transition in channel flow Y Zhao, Y Yang, S Chen Journal of Fluid Mechanics 802, R4, 2016 | 44 | 2016 |
| Using a new entropy loss analysis to assess the accuracy of RANS predictions of an high-pressure turbine vane Y Zhao, RD Sandberg Journal of Turbomachinery 142 (8), 081008, 2020 | 42* | 2020 |
| Evolution of material surfaces in the temporal transition in channel flow Y Zhao, Y Yang, S Chen Journal of Fluid Mechanics 793, 840-876, 2016 | 42 | 2016 |
| Transition modeling for low pressure turbines using computational fluid dynamics driven machine learning HD Akolekar, F Waschkowski, Y Zhao, R Pacciani, RD Sandberg Energies 14 (15), 4680, 2021 | 38 | 2021 |
| Machine-learning for turbulence and heat-flux model development: A review of challenges associated with distinct physical phenomena and progress to date RD Sandberg, Y Zhao International Journal of Heat and Fluid Flow 95, 108983, 2022 | 37 | 2022 |
| Data-driven model development for large-eddy simulation of turbulence using gene-expression programing H Li, Y Zhao, J Wang, RD Sandberg Physics of Fluids 33 (12), 2021 | 35 | 2021 |
| Integration of machine learning and computational fluid dynamics to develop turbulence models for improved low-pressure turbine wake mixing prediction HD Akolekar, Y Zhao, RD Sandberg, R Pacciani Journal of Turbomachinery 143 (12), 121001, 2021 | 32* | 2021 |
| Constrained large-eddy simulation of laminar-turbulent transition in channel flow Y Zhao, Z Xia, Y Shi, Z Xiao, S Chen Physics of Fluids 26 (9), 2014 | 32 | 2014 |
| Toward more general turbulence models via multicase computational-fluid-dynamics-driven training Y Fang, Y Zhao, F Waschkowski, ASH Ooi, RD Sandberg AIAA Journal 61 (5), 2100-2115, 2023 | 29 | 2023 |
| Sinuous distortion of vortex surfaces in the lateral growth of turbulent spots Y Zhao, S Xiong, Y Yang, S Chen Physical Review Fluids 3 (7), 074701, 2018 | 27 | 2018 |
| Data-driven nonlinear K-L turbulent mixing model via gene expression programming method H Xie, Y Zhao, Y Zhang Acta Mechanica Sinica 39 (2), 322315, 2023 | 20 | 2023 |
| Large-eddy simulation of particle-laden isotropic turbulence using machine-learned subgrid-scale model Q Wu, Y Zhao, Y Shi, S Chen Physics of Fluids 34 (6), 2022 | 18 | 2022 |
| High-fidelity simulations of a high-pressure turbine vane subject to large disturbances: effect of exit Mach number on losses Y Zhao, RD Sandberg Journal of Turbomachinery 143 (9), 091002, 2021 | 18 | 2021 |
| Assessment of machine-learned turbulence models trained for improved wake-mixing in low-pressure turbine flows R Pacciani, M Marconcini, F Bertini, S Rosa Taddei, E Spano, Y Zhao, ... Energies 14 (24), 8327, 2021 | 15 | 2021 |
Richard D SandbergUniversity of MelbourneE-mailová adresa ověřena na: unimelb.edu.au