Introduction of Generalized Interpolation Material Point (GIMP) method to MPM simulation for CG applications
Adaptive GIMP discretization framework with significantly improved shape functions
Memory efficient and highly regular parallel particle-grid transfer scheme
Highly optimized particle-grid transfer approach
Abstract
We present an adaptive Generalized Interpolation Material Point (GIMP) method for simulating elastoplastic materials. Our approach allows adaptive refining and coarsening of different regions of the material, leading to an efficient MPM solver that concentrates most of the computation resources in specific regions of interest. We propose a C1 continuous adaptive basis function that satisfies the partition of unity property and remains non-negative throughout the computational domain. We develop a practical strategy for particle-grid transfers that leverages the recently introduced SPGrid data structure for storing sparse multi-layered grids. We demonstrate the robustness and efficiency of our method on the simulation of various elastic and plastic materials. We also compare key kernel components to uniform grid MPM solvers to highlight performance benefits of our method.
Author
MING GAO, University of Wisconsin Madison
ANDRE PRADHANA TAMPUBOLON, University of Pennsylvania
CHENFANFU JIANG, University of Pennsylvania
EFTYCHIOS SIFAKIS, University of Wisconsin Madison
Journal/Conference
ACM Trans. Graph. 36, 6, Article 223, (SIGGRAPH Asia 2017)
Summary
Abstract
We present an adaptive Generalized Interpolation Material Point (GIMP) method for simulating elastoplastic materials. Our approach allows adaptive refining and coarsening of different regions of the material, leading to an efficient MPM solver that concentrates most of the computation resources in specific regions of interest. We propose a C1 continuous adaptive basis function that satisfies the partition of unity property and remains non-negative throughout the computational domain. We develop a practical strategy for particle-grid transfers that leverages the recently introduced SPGrid data structure for storing sparse multi-layered grids. We demonstrate the robustness and efficiency of our method on the simulation of various elastic and plastic materials. We also compare key kernel components to uniform grid MPM solvers to highlight performance benefits of our method.
Author
Journal/Conference
ACM Trans. Graph. 36, 6, Article 223, (SIGGRAPH Asia 2017)
Link