Given its good readability and extendibility, the script can be extended to the processing of more different problems in Abaqus. When a dataset is very large, we must decompose the model into several pieces, requiring parallel data capability to achieve full parallel visualization. The data partition scheme minimizes data redundancy and works efficiently. This level of parallel visualization uses parallel rendering and the corresponding parallel computing components of ParaView. To support full parallel visualization, the Python script achieves data partitioning by reorganizing all nodes, elements and the corresponding results on those nodes and elements. ParaView's parallel visualization makes it possible to visualize very big data. Such capabilities as volume rendering, tensor glyphs, superior animation and other filters allow ParaView to offer excellent visualizing manifestations.
#The paraview guide a parallel visualization application manual
Reference Manual ’s Section 1 to Section 12 provide details on various components in the UI and the scripting API. We convert Abaqus output into the XML-based VTK format by developing a Python script and then using ParaView to visualize the results. Welcome to ParaView Documentation This guide is split into three volumes: User’s Guide ’s Section 1 to Section 8 cover various aspects of data analysis and visualization with ParaView. Models from microtomography have extremely complicated structures, and datasets of Abaqus output are huge, requiring a visualization tool more powerful than Abaqus/CAE. However, the visualization capability of Abaqus using its CAE module is limited.
It delivers linear and nonlinear analyses of mechanical and fluid dynamics, includes multi-body system and multi-physics coupling. Abaqus® is a popular software suite for finite element analysis.
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