Abstract

Abstract

This work extends a multi-phase mixing model framework designed for a Smoothed Particle Hydrodynamics context. Specifically, we propose a higher-order variation using the first-order accurate Generalised Finite Difference differential operators to construct an incompressible scheme for simulating fluid-solid coupled systems resolved via a continuum mixture model. The proposed scheme incorporates inter-phase shear between phases and the viscosity dependency of the solid phase concentration. The scheme is verified by simulating a modified lid-driven cavity case at Re = 1000. In this simulation, our method was capable of treating initially discontinuous concentration fields with a maximum solid volume concentration of 0.5 and a solid-to-fluid density ratio of 4.

Abstract

The simulation of fluid-structure interaction (FSI) problems usually involves many degrees of freedom, and a considerable number of particles is generally required to model both fluid and solid domains. In relation to the modeling of solid walls by particles, the use of triangular meshes provides more efficient and smoother representation of complex-shaped solid surfaces as well as the straight coupling between particle and mesh-based methods, which is suitable for FSI applications. However, in the particle-based simulations with solid boundaries modeled by mesh, the computation of the particle-mesh distances is a critical time-consuming task, and a fast technique is of major importance. Taking advantage of the cell linked list structure widely adopted for fixed-radius neighborhood search algorithms in particle methods, we proposed a Fast Point-to-mesh Distance computation technique based on Cell linked list (FPDC). Alongside this new technique, a particle-polygon wall contact model was introduced to enable simulations of the collision between the surface of the moving bodies and fixed wall represented, respectively, by particles and mesh. The results show that the proposed technique provides a significant processing time speedup and can be used for practical large-scale problems.