Yaomin Zhao

Yaomin Zhao

Assistant Professor,
Peking University.
Turbulence simulation and machine-learning turbulence modeling.

Publications

(* corresponding author) 45 journal papers, with over 1,500 citations.

You can also find my articles on my Google Scholar profile.

Journal Cover

JFM cover, 2020

Journal of Fluid Mechanics, Vol. 888, 2020.

First- and Corresponding-Author Papers

  1. Y. Zhao, T. Wang, B. Lyu*. (2026). "Low-wavenumber wall pressure fluctuations in turbulent flows within concentric annular ducts." Journal of Fluid Mechanics, 1037, A56. [DOI]
  2. T. Wang, C. Zhang, Y. Zhao*. (2026). "Asymmetric particle transport in turbulent flows within concentric annular ducts." Journal of Fluid Mechanics, 1037, A18. [DOI]
  3. H. Ji, Y. Luo, H. Zhou, Y. Zhao*. (2026). "Progressive mixture-of-experts with autoencoder routing for continual RANS turbulence modelling." Journal of Fluid Mechanics, 1036, A54. [DOI]
  4. X. Zhu, Y. Ge, Y. Zhao*, Z. Xiao, R. D. Sandberg. (2026). "Boundary layer transition induced by surface roughness distributed over a low-pressure turbine blade." Journal of Turbomachinery, 148(8), 081014. [DOI]
  5. W. Shen, Y. Ge, Z. Han, Y. Zhao*, Y. Yang*. (2026). "Constructing wall turbulence using hierarchical hairpin vortices." Physical Review Fluids, 11, 044604. [DOI]
  6. T. Wang, B. Lyu, Y. Zhao*. (2026). "Frequency response of the unsteady separating boundary layer in a compressor cascade." Acta Mechanica Sinica, in press.
  7. Y. Ge, X. Zhu, Y. Fang, Y. Zhao*. (2026). "Machine-learning-enhanced four-equation model for predicting roughness-induced transition." AIAA Journal. [DOI]
  8. T. Wang, B. Meng, B. Tian, Y. Zhao*. (2026). "A high-fidelity and efficient framework for point-particle direct numerical simulation based on multi-block overset grids." Computer Physics Communications, 322, 110059. [DOI]
  9. H. Xie, T. Luo, Y. Zhao*, Y. Zhang*, J. Wang. (2025). "A compressible Reynolds-averaged mixing model considering turbulent composition and heat fluxes." Journal of Fluid Mechanics, 1019, A56. [DOI]
  10. H. Xie, M. Xiao, Y. Zhao*, Y. Zhang*, J. Wang, Y. Shi. (2025). "A detached-eddy simulation methodology for interfacial mixing flows." Physica D: Nonlinear Phenomena, 482, 134892. [DOI]
  11. H. Xie, H. Qi, M. Xiao, Y. Zhang*, Y. Zhao*. (2025). "An intermittency-based Reynolds-averaged transition model for mixing flows induced by interfacial instabilities." Journal of Fluid Mechanics, 1002, A31. [DOI]
  12. H. Li, J. Xie, C. Zhang, Y. Zhang, Y. Zhao*. (2025). "A transformer-based convolutional method to model inverse cascade in forced two-dimensional turbulence." Journal of Computational Physics, 520, 113475. [DOI]
  13. H. Li, Y. Zhao*, F. Waschkowski, R. D. Sandberg. (2024). "Evolutionary neural networks for learning turbulence closure models with explicit expressions." Physics of Fluids, 36, 055126. [DOI]
  14. H. Zhou, H. Li, Y. Zhao*. (2024). "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. [DOI]
  15. T. Wang, Y. Zhao*, J. Leggett, R. D. Sandberg. (2023). "Direct numerical simulation of a high-pressure turbine stage: unsteady boundary layer transition and the resulting flow structures." Journal of Turbomachinery, 145(12), 121009. [DOI]
  16. H. Xie, Y. Zhao*, Y. Zhang*. (2023). "Data-driven nonlinear K–L turbulent mixing model via gene expression programming method." Acta Mechanica Sinica, 39, 322315. [DOI]
  17. J. Leggett, Y. Zhao*, R. D. Sandberg. (2023). "High-fidelity simulation study of the unsteady flow effects on high-pressure turbine blade performance." Journal of Turbomachinery, 145(1), 011002. [DOI]
  18. Q. Wu, Y. Zhao*, Y. Shi, S. Chen. (2022). "Large-eddy simulation of particle-laden isotropic turbulence using machine-learned subgrid-scale model." Physics of Fluids, 34, 065129 (Editor's Pick). [DOI]
  19. H. Li, Y. Zhao*, J. Wang, R. D. Sandberg. (2021). "Data-driven model development for large-eddy simulation of turbulence using gene-expression programming." Physics of Fluids, 33, 125127. [DOI]
  20. Y. Zhao*, X. Xu. (2021). "Data-driven turbulence modelling based on gene-expression programming." Chinese Journal of Theoretical and Applied Mechanics, 53(10), 1–16 (in Chinese).
  21. Y. Zhao*, R. D. Sandberg. (2021). "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. [DOI]
  22. Y. Zhao*, R. D. Sandberg. (2020). "Bypass transition in boundary layers subject to strong pressure gradient and curvature effects." Journal of Fluid Mechanics, 888, A4 (Featured on cover). [DOI]
  23. Y. Zhao*, H. D. Akolekar, J. Weatheritt, V. Michelassi, R. D. Sandberg. (2020). "RANS turbulence model development using CFD-driven machine learning." Journal of Computational Physics, 411, 109413. [DOI]
  24. Y. Zhao*, R. D. Sandberg. (2020). "Using a new entropy loss analysis to assess the accuracy of RANS predictions of a high-pressure turbine vane." Journal of Turbomachinery, 142, 081008. [DOI]
  25. Y. Zhao, S. Xiong, Y. Yang*, S. Chen. (2018). "Sinuous distortion of vortex surfaces in the lateral growth of turbulent spots." Physical Review Fluids, 3, 074701. [DOI]
  26. Y. Zhao, Y. Yang*, S. Chen. (2016). "Vortex reconnection in the late transition in channel flow." Journal of Fluid Mechanics, 802, R4. [DOI]
  27. Y. Zhao, Y. Yang*, S. Chen. (2016). "Evolution of material surfaces in the temporal transition in channel flow." Journal of Fluid Mechanics, 793, 840–876. [DOI]
  28. Y. Zhao, Z. Xia*, Y. Shi, Z. Xiao, S. Chen. (2014). "Constrained large-eddy simulation of laminar-turbulent transition in channel flow." Physics of Fluids, 26, 095103. [DOI]

Co-authored Papers

  1. Z. Wang, J. Zhong, K. Wang, Z. Zhu, Z. Bao, C. Zhu, W. Zhao, Y. Zhao, Y. Yang, C. Song*, S. Xiong*. (2026). "Simulating fluid vortex interactions on a superconducting quantum processor." Nature Communications, 17, 2602. [DOI]
  2. Y. Fang*, M. Reissmann, R. Pacciani, Y. Zhao, A. S. H. Ooi, M. Marconcini, H. D. Akolekar, R. D. Sandberg. (2026). "Accelerating CFD-driven training of transition and turbulence models for turbine flows by one-shot and real-time transformer integration." Computers & Fluids, 306, 106927. [DOI]
  3. B. Wang, Z. Meng, Y. Zhao, Y. Yang*. (2025). "Quantum lattice Boltzmann method for simulating nonlinear fluid dynamics." npj Quantum Information, 11, 196. [DOI]
  4. Z. Meng, Z. Lu, S. Xiong, Y. Zhao, Y. Yang*. (2025). "Advances in quantum computing for fluid dynamics." Advances in Mechanics, 55(3), 541–566 (in Chinese).
  5. C. Zhu, Z. Wang, S. Xiong*, Y. Zhao, Y. Yang. (2025). "Quantum implicit representation of vortex filaments in turbulence." Journal of Fluid Mechanics, 1014, A31. [DOI]
  6. F. Waschkowski*, H. Li, A. Deshmukh, T. Grenga, Y. Zhao, H. Pitsch, J. Klewicki, R. D. Sandberg. (2024). "Gradient information and regularization for gene expression programming to develop data-driven physics closure models." Flow, Turbulence and Combustion. [DOI]
  7. Y. Fang*, Y. Zhao, H. D. Akolekar, A. S. H. Ooi, R. D. Sandberg, R. Pacciani, M. Marconcini. (2024). "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. [DOI]
  8. Y. Fang*, Y. Zhao, F. Waschkowski, A. S. H. Ooi, R. D. Sandberg. (2023). "Toward more general turbulence models via multicase computational-fluid-dynamics-driven training." AIAA Journal, 65(5). [DOI]
  9. B. Xu, H. Li, X. Liu, Y. Xiang, P. Lv, X. Tan, Y. Zhao, C. Sun, H. Duan*. (2023). "Effect of micro-grooves on drag reduction in Taylor–Couette flow." Physics of Fluids, 35, 063608. [DOI]
  10. C. Lav*, A. J. Banko, F. Waschkowski, Y. Zhao, C. J. Elkins, J. K. Eaton, R. D. Sandberg. (2023). "A coupled framework for symbolic turbulence models from deep learning." International Journal of Heat and Fluid Flow, 101, 109140. [DOI]
  11. R. D. Sandberg*, Y. Zhao. (2022). "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 (Review). [DOI]
  12. F. Waschkowski*, Y. Zhao, R. D. Sandberg, J. Klewicki. (2022). "Multi-objective CFD-driven development of coupled turbulence closure models." Journal of Computational Physics, 452, 110922. [DOI]
  13. H. D. Akolekar*, Y. Zhao, R. D. Sandberg, R. Pacciani. (2021). "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. [DOI]
  14. J. Weatheritt, Y. Zhao, R. D. Sandberg*, S. Mizukami, K. Tanimoto. (2020). "Data-driven scalar-flux model development with application to jet in cross flow." International Journal of Heat and Mass Transfer, 147, 118931. [DOI]
  15. R. Pichler, Y. Zhao, R. D. Sandberg*, V. Michelassi, R. Pacciani, M. Marconcini, A. Arnone. (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." Journal of Turbomachinery, 141, 121005. [DOI]
  16. M. Marconcini*, R. Pacciani, A. Arnone, V. Michelassi, R. Pichler, Y. Zhao, R. D. Sandberg. (2019). "Large-eddy simulation and RANS analysis of the end-wall flow in a linear low-pressure turbine cascade, part II: loss generation." Journal of Turbomachinery, 141, 051004. [DOI]
  17. Z. Xia*, Y. Shi, Y. Zhao. (2015). "Assessment of the shear-improved Smagorinsky model in laminar-turbulent transitional channel flow." Journal of Turbulence, 16(10), 925–936. [DOI]