An F-bar aided edge-based smoothed finite element methods with four-node tetrahedral elements (F-barES-FEM-T4) in large deformation explicit dynamics for nearly incompressible materials is proposed. Because F-barES-FEM-T4 is based on on the standard displacement-based variational formulations, it can be easily applied to dynamic analysis by adding the term of inertia to the static equilibrium equation. Some example analyses of explicit dynamics for simple and complex shapes reveal that the proposed method can suppress volumetric locking and pressure oscillation in dynamic explicit cases as well as in static implicit cases. Meanwhile, these analyses also reveal that the proposed method causes energy divergence in long-term analyses. As the speed of energy divergence can be suppressed by increasing the smoothing level, the proposed method with sufficient smoothing presents good results in relatively short-term analyses.
For large-scale numerical simulations on supercomputers, data transfer and storage present significant efficiency and productivity issues. Therefore, the jointed hierarchical precision compression number-data format (JHPCN-DF) technique was proposed for efficient visualization and analysis of plasma particle-in-cell simulation data. It is also available for lossless and lossy compression with user-defined errors. We implement a lossy compression method of JHPCN-DF in finite element code and evaluate the compression effectiveness and compression data accuracy in linear static and dynamic structural analyses. Our technique achieves the required accuracy, even for dynamic problems, and provides a significant increase in compression performance for variable datasets.
In order to educate teenager internet literacy on social network service, we have developed a Problem Solving Environment to evaluate the literacy-level of their messages on twitter for their teachers and them. We propose a method the system provides effective recognition for their risks. And we adapt the Naive Bayes classifier to evaluation for tweets on Twitter based on pattern-based classifier. In this result, the classification accuracy for word patterns increases from 39.6-57.6% to 68.0-79.9% using Naive Bayes classifier on a set of 3000 training data sets, and users obtain internet literacy skills base on this system.