Junkai Sun

Bio: Junkai Sun is an academic researcher from China University of Geosciences (Wuhan). The author has contributed to research in topics: Wave height & Immersed boundary method. The author has an hindex of 3, co-authored 3 publications receiving 25 citations.

Numerical Study on the Hydrodynamic Characteristics and Responses of Moored Floating Marine Cylinders Under Real-World Tsunami-Like Waves

Abstract: In recent tsunami events like those happened in the Indian Ocean in 2004 and 2018 and in Japan in 2011, many ocean and coastal infrastructures have been damaged tremendously, revealing the need of the work to study the hydrodynamic interaction between real-world tsunami waves and offshore structures. Besides, the destructive energy of tsunami wave on the offshore structures should also be assessed. However, to study the impact of tsunami waves on offshore structures, solitary waves are used generally instead of real-world tsunami waves despite the vast differences in hydrodynamic characteristics. Using Computational Fluid Dynamics (CFD) approach, this paper investigates the hydrodynamic characteristics of the floating cylinders moored by chains, under the real-world tsunami-like waves (2011 Japan Tohoku tsunami). Comparisons of hydrodynamic characteristics between real-world tsunami-like wave and solitary wave propagating over the moored cylinders are analyzed and discussed, indicating that the flow fields, the tension forces of mooring chains, the impinging forces and the movement trajectories of the cylinder caused by the two types of waves are greatly different from each other. Moreover, the influences of single cylinder diameter and tandem cylinder spacing on the interaction between real-world tsunami-like waves and cylinders are considered through a series of simulation tests, while meaningful conclusions are drawn.

<scp>Multiple‐GPU</scp> parallelization of three‐dimensional material point method based on single‐root complex

Abstract: As one of the arbitrary Lagrangian–Eulerian methods, the material point method (MPM) owns intrinsic advantages in simulation of large deformation problems by combining the merits of the Lagrangian and Eulerian approaches. Significant computational intensity is involved in the calculations of the MPM due to its very fine mesh needed to achieve a sufficiently high accuracy. A new multiple-GPU parallel strategy is developed based on a single-root complex architecture of the computer purely within a CUDA environment. Peer-to-Peer (P2P) communication between the GPUs is performed to exchange the information of the crossing particles and ghost element nodes, which is faster than the heavy send/receive operations between different computers through the infiniBand network. Domain decomposition is performed to split the whole computational task over the GPUs with a number of subdomains. The computations within each subdomain are allocated on a corresponding GPU using an enhanced “Particle-List” scheme to tackle the data race during the interpolation from associated particles to common nodes. The acceleration effect of the parallelization is evaluated with two benchmarks cases, mini-slump test after a dam break and cone penetration test in clay, where the maximum speedups with 1 and 8 GPUs are 88 and 604, respectively.