Journal Cover
Journal Papers (Corresponding author*)
30. H. Xie, H. Qi, M. Xiao, Y. Zhang* and Y. Zhao*, An intermittency based Reynolds-averaged transition model for mixing flows induced by interfacial instabilities, Journal of Fluid Mechanics, in revision
29. H. Li, J. Xie, C. Zhang, Y. Zhang and Y. Zhao*, A transformer-based convolutional method to model inverse cascade in forced two-dimensional turbulence, Journal of Computational Physics, 520, 113475, 2025
28. F. Waschkowski*, H. Li, A. Deshmukh, T. Grenga, Y. Zhao, H. Pitsch, J. Klewicki, R. D. Sandberg, Gradient information and regularization for gene expression programming to develop data-driven physics closure models, Flow, Turbulence and Combustion, 2024
27. H. Li, Y. Zhao*, F. Waschkowski, and R. D. Sandberg, Evolutionary neural networks for learning turbulence closure models with explicit expressions, Physics of Fluids, 36, 055126, 2024
26. Y. Fang*, Y. Zhao, H. D. Akolekar, A. S. H. Ooi, R. D. Sandberg, R. Pacciani, and M. Marconcini, A data-driven approach for generalizing the laminar kinetic energy model for separation and bypass transition in low- and high-pressure turbines, Journal of Turbomachinery, 146(9): 091005, 2024
25. H. Zhou, H. Li, and Y. Zhao*, Identification of partial differential equations from noisy data with integrated knowledge discovery and embedding using evolutionary neural networks, Theoretical and Applied Mechanics Letters, 14(2), 100511, 2024
24. T. Wang, Y. Zhao*, J. Leggett, and R. D. Sandberg, Direct numerical simulation of an HPT stage: unsteady boundary layer transition and the resulting flow structures, Journal of Turbomachinery, 145(12), 121009, 2023
23. Y. Fang, Y. Zhao, F. Waschkowski, A. S. H. Ooi, and R. D. Sandberg, Toward more general turbulence models via multicase computational-fluid-dynamics-driven training, AIAA Journal, 65(5), 2023
22. H. Xie, Y. Zhao*, and Y. Zhang*, Data-driven nonlinear K-L turbulent mixing model via gene expression programming method, Acta Mechanica Sinica, 39, 322315, 2023
21. B. Xu, H. Li, X. Liu, Y. Xiang, P. Lv, X. Tan, Y. Zhao, C. Sun and H. Duan*, Effect of micro-grooves on drag reduction in Taylor–Couette flow, Physics of Fluids, 35, 063608, 2023
20. C. Lav*, A. J. Banko, F. Waschkowski, Y. Zhao, C. J. Elkins, J. K. Eaton, R. D. Sandberg, A coupled framework for symbolic turbulence models from deep-learning, International Journal of Heat and Fluid Flow, 101, 109140, 2023
19. J. Leggett, Y. Zhao*, and R. D. Sandberg, High-fidelity simulation study of the unsteady flow effects on high-rressure turbine blade performance, Journal of Turbomachinery, 145(1), 011002, 2023
18. Q. Wu, Y. Zhao*, Y. Shi and S. Chen, Large eddy simulation of particle-laden isotropic turbulence using machine-learned subgrid scale model, Physics of Fluids, 34, 065129, 2022 (Editor's Pick)
17. R. D. Sandberg* and Y. Zhao, Machine-learning for turbulence and heat-flux model development : A review of challenges associated with distinct physical phenomena and progress to date, International Journal of Heat and Fluid Flow, 95, 108983, 2022 (Review Paper)
16. F. Waschkowski*, Y. Zhao, R. D. Sandberg, and J. Klewicki, Multi-objective CFD-driven development of coupled turbulence closure models, Journal of Computational Physics, 452, 110922, 2022
15. H. Li, Y. Zhao*, J. Wang and R. D. Sandberg, Data-driven model development for large- eddy simulation of turbulence using gene- expression programing, Physics of Fluids, 33, 125127, 2021
14. Y. Zhao* and X. Xu, Data-driven turbulence modelling based on gene-expression programming, Chinese Journal of Theoretical and Applied Mechanics, 53(10), 1-16, 2021 (In Chinese)
13. H. D. Akolekar*, Y. Zhao, R. D. Sandberg and R. Pacciani, Integration of machine learning and computational fluid dynamics to develop turbulence models for improved low-pressure turbine wake mixing prediction, Journal of Turbomachinery, 143, 121001, 2021
12. Y. Zhao* and R. D. Sandberg, High-fidelity simulations of a high-pressure turbine vane subject to large disturbances: effect of exit Mach number on losses, Journal of Turbomachinery, 143, 091002, 2021
11. Y. Zhao* and R. D. Sandberg, Bypass transition in boundary layers subject to strong pressure gradient and curvature effects, Journal of Fluid Mechanics, 888, A4, 2020
10. Y. Zhao*, H. D. Akolekar, J. Weatheritt, V. Michelassi, and R. D. Sandberg, RANS turbulence model development using CFD-driven machine learning, Journal of Computational Physics, 411, 109413, 20209. Y. Zhao* and R. D. Sandberg, Using a new entropy loss analysis to assess the accuracy of RANS predictions of an high-pressure turbine vane, Journal of Turbomachinery, 142, 081008, 2020
8. J. Weatheritt, Y. Zhao, R. D. Sandberg*, S. Mizukami, and K. Tanimoto, Data-driven scalar-flux model development with application to jet in cross flow, International Journal of Heat and Mass Transfer, 147, 118931, 2020
7. R. Pichler, Y. Zhao, R. D. Sandberg*, V. Michelassi, R. Pacciani, M. Marconcini, and A. Arnone, Large-eddy simulation and RANS analysis of the end-wall flow in a linear low-pressure turbine cascade cascade, part I: flow and secondary vorticity fields under varying inlet condition, Journal of Turbomachinery, 141, 121005, 2019
6. M. Marconcini*, R. Pacciani, A. Arnone, V. Michelassi, R. Pichler, Y. Zhao, and R. D. Sandberg , Large-eddy simulation and RANS analysis of the end-wall flow in a linear low-pressure turbine cascade cascade, part II: loss generation, Journal of Turbomachinery, 141, 051004, 2019
5. Y. Zhao, S. Xiong, Y. Yang*, and S. Chen, Sinuous distortion of vortex surfaces in the lateral growth of turbulent spots, Physical Review Fluids, 3, 074701, 2018
4. Y. Zhao, Y. Yang*, and S. Chen, Vortex reconnection in the late transition in channel flow, Journal of Fluid Mechanics, 802, R4, 2016
3. Y. Zhao, Y. Yang*, and S. Chen, Evolution of material surfaces in the temporal transition in channel flow, Journal of Fluid Mechanics, 793, 840-876, 2016
2. Z. Xia*, Y. Shi, and Y. Zhao, Assessment of the shear-improved Smagorinsky model in laminar- turbulent transitional channel flow, Journal of Turbulence, 16(10), 925-936, 2015
1. Y. Zhao, Z. Xia*, Y. Shi, Z. Xiao, and S. Chen, Constrained large-eddy simulation of laminar-turbulent transition in channel flow, Physics of Fluids, 26, 095103, 2014
Refereed Proceeding Papers
7. Y. Zhao and R. D. Sandberg, High-fidelity simulations of a high-pressure turbine vane with endwalls: impact of secondary structures and spanwise temperature profiles on losses, ASME Turbo Expo, Online, GT2021-58816, 2021
6. Y. Zhao and R. D. Sandberg, High-fidelity simulations of a high-pressure turbine
stage: effects of Reynolds number and inlet turbulence, ASME Turbo Expo, Online, GT2021-58995, 2021
5. J. Leggett, Y. Zhao, E. S. Richardson and R. D. Sandberg, Turbomachinery loss analysis: the relationship between mechanical work potential and entropy analyses, ASME Turbo Expo, Online, GT2021-59436, 2021
3. Y. Zhao, Y. Yang, S. Xiong and S. Chen, Characterization of representative vortex surfaces in transitional boundary layers, Proceeding of the 10th International Symposium on Turbulence and Shear Flow Phenomena (TSFP), Chicago, USA, 1C-4, 2017
2. Y. Yang, Y. Zhao, S. Xiong, P. Hack and J. Kim, Evolution of vortex-surface fields in the K-type Transitional boundary layer, Proceeding of the Summer Program, Center for Turbulence Research, Stanford University, pp. 203-212, 2016
1. Y. Zhao, Y. Yang and S. Chen, Lagrangian evolution of hairpin structures in the temporal transition in channel flow, Proceeding of the 9th International Symposium on Turbulence and Shear Flow Phenomena (TSFP), Melbourne, Australia, 6B-4, 2015