Patran is the world's most widely used pre/post-processing software for Finite Element Analysis (FEA), providing solid modeling, meshing, analysis setup and post-processing for multiple solvers including MSC Nastran, Marc, Abaqus, LS-DYNA, ANSYS, and Pam-Crash. Patran provides a rich set of tools that streamline the creation of analysis ready models for linear, nonlinear, explicit dynamics, thermal, and other finite element solutions. From geometry cleanup tools that make it easy for engineers to deal with gaps and slivers in CAD, to solid modeling tools that enable creation of models from scratch, Patran makes it easy for anyone to create FE models. Meshes are easily created on surfaces and solids alike using fully automated meshing routines, manual methods that provide more control, or combinations of both. Finally, loads, boundary conditions, and analysis setup for most popular FE solvers is built in, minimizing the need to edit input decks. Patran's comprehensive and industry tested capabilities ensure that your virtual prototyping efforts provide results fast so that you can evaluate product performance against requirements and optimize your designs. In addition to geometry import through industry-standard exchange formats, Patran also supports direct CAD access and provides advanced set of geometry creation tools. With Patran you can quickly create and edit geometry and create a quality finite element mesh in a short time. The sophisticated feature recognition capabilities allow users to delete or edit holes, fillets and chamfers. All finite elements, loads, boundary conditions, and material properties can be associated to the geometry saving considerable time during model updates. Manual preparation of input decks to be used with various FE solvers is a tedious and time consuming task. Patran supports complete preprocessing and postprocessing for leading FE solvers MSC.Nastran, MD Nastran, Marc, Abaqus, LSDYNA and Pam-Crash, improving your productivity. With its capabilities to transfer data from one solver to another, users can avoid duplication of effort for common tasks across multiple solvers making multidisciplinary model set up more efficient.


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