| Subgrid-scale model for large-eddy simulation of isotropic turbulent flows using an artificial neural network Z Zhou, G He, S Wang, G Jin Computers & Fluids 195, 104319, 2019 | 165 | 2019 |
| Wall model based on neural networks for LES of turbulent flows over periodic hills Z Zhou, G He, X Yang Physical Review Fluids 6 (5), 054610, 2021 | 70 | 2021 |
| A structural subgrid-scale model for relative dispersion in large-eddy simulation of isotropic turbulent flows by coupling kinematic simulation with approximate deconvolution … Z Zhou, S Wang, G Jin Physics of Fluids 30 (10), 2018 | 40 | 2018 |
| A structural subgrid-scale model for the collision-related statistics of inertial particles in large-eddy simulations of isotropic turbulent flows Z Zhou, S Wang, X Yang, G Jin Physics of Fluids 32 (9), 2020 | 31 | 2020 |
| A robust super-resolution reconstruction model of turbulent flow data based on deep learning Z Zhou, B Li, X Yang, Z Yang Computers & Fluids 239, 105382, 2022 | 21 | 2022 |
| Reynolds number effect on statistics of turbulent flows over periodic hills Z Zhou, T Wu, X Yang Physics of Fluids 33 (10), 2021 | 16 | 2021 |
| Hydrodynamic force and torque models for a particle moving near a wall at finite particle Reynolds numbers Z Zhou, G Jin, B Tian, J Ren International Journal of Multiphase Flow 92, 1-19, 2017 | 15 | 2017 |
| Investigation of the wake characteristics of an underwater vehicle with and without a propeller Z Zhou, Z Li, X Yang, S Wang, D Xu Ocean Engineering 266, 113107, 2022 | 13 | 2022 |
| Towards multi-fidelity simulation of flows around an underwater vehicle with appendages and propeller Z Zhou, Z Li, G He, X Yang Theoretical and Applied Mechanics Letters 12 (1), 100318, 2022 | 13 | 2022 |
| A wall model learned from the periodic hill data and the law of the wall Z Zhou, XIA Yang, F Zhang, X Yang Physics of Fluids 35 (5), 2023 | 11 | 2023 |
| Deep learning method for the super-resolution reconstruction of small-scale motions in large-eddy simulation Q Zhao, G Jin, Z Zhou AIP Advances 12 (12), 2022 | 9 | 2022 |
| Data driven turbulence modeling in turbomachinery—an applicability study L Fang, TW Bao, WQ Xu, ZD Zhou, JL Du, Y Jin Computers & Fluids 238, 105354, 2022 | 8 | 2022 |
| Prediction of Lagrangian dispersion of fluid particles in isotropic turbulent flows using large-eddy simulation method Z Zhou, J Chen, G Jin Acta Mechanica 228, 3203-3222, 2017 | 7 | 2017 |
| A new single formula for the law of the wall and its application to wall-modeled large-eddy simulation F Zhang, Z Zhou, H Zhang, X Yang European Journal of Mechanics-B/Fluids 94, 350-365, 2022 | 6 | 2022 |
| Strain self-amplification is larger than vortex stretching due to an invariant relation of filtered velocity gradients PF Yang, ZD Zhou, H Xu, GW He Journal of Fluid Mechanics 955, A15, 2023 | 5 | 2023 |
| Large-Eddy Simulation of Wind Turbine Wakes in Forest Terrain Y Li, Z Li, Z Zhou, X Yang Sustainability 15 (6), 5139, 2023 | 2 | 2023 |
| Homogeneity constraints on the mixed moments of velocity gradient and pressure Hessian in incompressible turbulence Z Zhou, PF Yang Physical Review Fluids 8 (2), 024601, 2023 | 2 | 2023 |
| Effects of wall topology on statistics of cube-roughened wall turbulence S Li, Z Zhou, D Chen, X Yuan, Q Guo, X Yang Boundary-Layer Meteorology 186 (2), 305-336, 2023 | 2 | 2023 |
| A measure–correlate–predict model based on neural networks and frozen flow hypothesis for wind resource assessment D Chen, Z Zhou, X Yang Physics of Fluids 34 (4), 2022 | 1 | 2022 |
| A data-driven wall model for LES of flow over periodic hills Z Zhou, G He, X Yang APS Division of Fluid Dynamics Meeting Abstracts, R01. 020, 2020 | 1 | 2020 |
