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A Reproducing Kernel Particle Method Framework for Modeling Failure of Structures Subjected to Blast Loadings

  • Author(s): Zhou, Guohua
  • Advisor(s): Chen, Jiun-Shyan
  • et al.

The numerical simulation of transient dynamic failure of structures subjected to blast loadings requires the key physics such as strong shocks in fluid (explosive gas and air) and solid media, fluid-structure interaction, material damage and fragmentations, and multi-body contact to be properly considered in the mathematical formulation and the associated numerical algorithms. These dominant phenomena in blast events yield “rough solution” in the conservation equations in the form of moving discontinuities that cannot be effectively modeled by the conventional finite element methods. A semi-Lagrangian meshfree Reproducing Kernel Particle Method (RKPM) framework is proposed to model such extreme events in this study. In this work, shock waves in both air and solid are modeled by embedding the Godunov flux into the semi-Lagrangian RKPM formulation in a unified manner. The essential shock physics are introduced in the proposed node-based Riemann solver, and the Gibbs oscillation is limited by introducing a gradient smoothing technique. In this thesis, two formulations are proposed and verified by solving a set of multi-dimensional benchmark problems involving strong shocks in fluids and solids. The air-structure interface is treated by a level set enhanced natural kernel contact algorithm, which does not require the definition of potential contact surfaces a priori. The blast-induced fragmentation is simulated by the damage model under the semi-Lagrangian RKPM discretization without using the artificial element erosion technique. Several benchmark problems have been solved to verify the accuracy and performance of the proposed numerical formulation. This computational framework is then applied to the simulation of a reinforced concrete column subjected to blast loading and explosive welding processes, demonstrating the effectiveness and robustness of the proposed methods.

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