Particle Method

Implicit SPH in LS-DYNA for Automotive Water Wading Simulations

The explicit SPH solver implemented in LS-DYNA is well fitted for numerical simulations involving hypervelocity impacts, explosions and other transient events, but is unsuitable for slower fluid-flow simulations such as water wading. In this work, we introduce an implicit SPH formulation specifically developed for handling large-scale incompressible fluid simulations. The method is based on a traditional projection scheme: Intermediate velocities are first predicted based on external and viscosity forces contribution, and a Poisson equation is then solved to obtain pressure forces such that incompressibility is maintained up to a given tolerance. All the surfaces composing the structure are automatically sampled with SPH particles by LS-DYNA using a user-supplied maximum interparticle distance, and the fluid-structure interaction is embedded in the SPH solver directly. This aspect of the simulation does not require any contact card to be setup. As the simulation evolves, the initial domain decomposition performed by LS-DYNA can become inefficient, triggering increasing communications across processors and poor load balancing, resulting in an increasing CPU time per simulation cycle as the SPH fluid particles intermix. A new feature has been developed based on the full-deck restart capability of LS-DYNA. The objective is to re-decompose the domain across processors at regular intervals, based on the updated geometry of the problem. This results in a more constant simulation time and overall improved performance.

Investigation on Parameter Identification and Coarse Graining Models using Discrete Element Capability in LS-DYNA

Processes such as transportation, flowing and processing of powder materials can be seen in the manufacturing process of various industrial products and are important processes for manufacturing high quality products. Discrete Element Method (DEM)[1] is widely used as a simulation method to handle powder materials, and excellent DEM function is also implemented in LS-DYNA. The DEM model can be used intuitively, and there is an advantage that stable computation can be performed. On the other hand, the DEM model is a hypothetical model based on the spring-mass model, and in order to reproduce the real phenomenon with high accuracy, it includes many numerical parameters that the user must decide beforehand. In this paper, the simulation of a compression experiment of polymer pellets were performed and the result of the parameter identification using optimization software LS-OPT is reported. In addition, when DEM is applied to fine powder material, the number of particles becomes enormous, and in many cases it cannot be processed in a common computational environment. In such a case, a coarse graining model is used to reduce the number of particles and computational load. Various ideas have been proposed for the method of coarse graining so far, and in this paper several coarse graining models were tested to compare powder behavior in drum mixing problem.