About Me
Welcome to my homepage!
I am Dr. Shijie Qin, currently a tenure-track associate professor in the State Key Laboratory of Ocean Engineering at Shanghai Jiao Tong University (SJTU). My research primarily focuses on high-precision numerical simulation and turbulence. Prior to joining SJTU, I worked as a postdoctoral fellow in the Department of Mathematics at the Hong Kong University of Science and Technology (HKUST). In 2023, I received my Ph.D. in Naval Architecture and Ocean Engineering from SJTU, where I subsequently worked as a postdoctoral and assistant research fellow.
My research interests lie deeply in the fields of fluid mechanics and nonlinear dynamics, specifically targeting turbulence (such as Figure 1), spatio-temporal chaos (such as Figure 2), and high-precision computational methods: A core focus of my work is the development and application of Clean Numerical Simulation (CNS) [1]. I have successfully extended CNS to spatio-temporal chaos (such as Figure 3) and two- (such as Figure 4) and three-dimensional (such as Figure 5) Navier-Stokes turbulence, while also proposing self-adaptive CNS strategies to significantly increase computational efficiency [2].
Figure 1: Instantaneous temperature departure fields of two-dimensional turbulent Rayleigh-Bénard convection (RBC) given by Clean Numerical Simulation (left, marked by CNS) and Runge-Kutta method with double precision (right, marked by RKwD). For more details, please see Journal of Fluid Mechanics (2022), vol. 948, A7 and the corresponding movie.
Figure 2: Spatio-temporal distributions of the modulus of the numerical solutions to the one-dimensional nonlinear Schrödinger equation (NLSE), obtained via CNS (left) and RKwD (right). For more details, please see Physica D: Nonlinear Phenomena (2024), vol. 470, 134355.
Figure 3: A periodic pendulum chain. For more details, please see Chaos, Solitons & Fractals (2020), vol. 136, 109790.
Figure 4: Instantaneous vorticity fields of two-dimensional turbulent Kolmogorov flow given by CNS with two initial conditions: without tiny disturbance (left, marked by Flow CNS) and with tiny disturbance (right, marked by Flow CNS’). For more details, please see Journal of Fluid Mechanics (2025), vol. 1009, A2 and the corresponding movie.
Figure 5: Instantaneous distributions of vorticity modulus of three-dimensional turbulent Kolmogorov flow given by Clean Numerical Simulation (left, marked by CNS) and Direct Numerical Simulation (right, marked by DNS). For more details, please see Physics of Fluids (2025), vol. 37, 105160 and the corresponding movie.
Through my research, I have systematically explored the influence of minute stochastic disturbances on chaotic dynamical systems, including turbulence. I discovered that such micro-disturbances—even at the level of numerical noise—can induce large-scale deviations not only in macroscopic spatio-temporal trajectories but also in the statistical properties and flow states of turbulent flows (as shown in Figures 1, 4 and 5). Furthermore, I found that “ultra-chaos” widely exists in the Lagrangian trajectories of fluid particles within the Arnold-Beltrami-Childress (ABC) flow (as shown in Figure 6). Recently, I have also co-proposed the “noise-expansion cascade” theory to explain the origin of randomness in turbulence, demonstrating how microscopic noise amplifies to macroscopic scales via nonlinear effects (as shown in Figure 7).
Figure 6: Chaotic states of the fluid particles starting from different points r0=(x(0), y(0), z(0)) in Arnold-Beltrami-Childress (ABC) flow. For more details, please see Journal of Fluid Mechanics (2023), vol. 960, A15.
Figure 7: Evolutions of the first disturbance δ1 (green dashed line) and the second disturbance δ2 (blue dash-dotted line) in a two-dimensional turbulent Kolmogorov flow. For more details, please see Journal of Fluid Mechanics (2025), vol. 1009, A2.
My academic efforts have been recognized with several honors, including the 2025 Excellent Doctoral Dissertation Award in Naval Architecture and Ocean Engineering and the 2023 Shanghai Post-doctoral Excellence Program. I have published extensively in top-tier journals, including multiple first-author or co-first-author papers in Journal of Fluid Mechanics and Physics of Fluids.
Feel free to explore my website to learn more about my publications, research projects, and academic background.
Our research group has ongoing openings for Master’s students, PhD students, and Postdoctoral researchers. We warmly welcome students and scholars with backgrounds in Mechanics, Applied Mathematics, Ocean Engineering, Machine Learning, and related fields to inquire via email.
REFERENCES
[1] S. Liao, Clean Numerical Simulation (Chapman and Hall/CRC, 2023).
[2] S. Qin and S. Liao, A self-adaptive algorithm of the clean numerical simulation (CNS) for chaos, Adv. Appl. Math. Mech. 15(5), 1191–1215 (2023).