Distributed FE Analysis of Multiphase Composites for Linear and Nonlinear Material Behaviour

Conference paper
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Abstract

Modern and efficient adaptive multiscale models can be applied for the prognosis of complex material behavior including deterioration and damage effects of heterogeneous materials. Therewith the physical effects of damage initiation and crack propagation can be captured on the appropriate spatial scales and parameter identification for the different material constituents can be performed much easier than by applying phenomenological material models for composite materials on macroscale. As a consequence of application of these models, the number of degrees of freedom and therewith the necessary computing effort increases substantially. This statement holds for the necessary main memory as well as for the computing power to solve the underlying linear and nonlinear equation systems. The project dcmamc have been investigating partition/substructuring methods and efficient parallel algorithms to solve very large linear and nonlinear equation systems. The algorithms were implemented, tested and adapted to the HPC computing framework at the HLRS Stuttgart, using several hundred CPU nodes. A memory-efficient iterative and parallelized equation solver combined with a special preconditioning technique for solving the underlying equation system was modified and adapted in order to be applied in a mixed CPU and GPU based hardware environment. Additionally a saw-tooth algorithm has been adapted to take into account nonlinear material behavior in a sequential linear manner. Therewith the material nonlinear problem is treated as a sequential solution of purely linear problems, avoiding all drawbacks with convergence problems in classical incremental-iterative solution techniques. In return a substantially increased number of linear solution steps has to be accepted.

Keywords

Smeared Crack Nonlinear Equation System Nonoverlapping Domain Decomposition Inelastic Region Softening Modulus 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Notes

Acknowledgements

This research has been supported by the German Research Foundation (DFG), which is gratefully acknowledged by the authors.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Institute of Structural MechanicsBauhaus-University WeimarWeimarGermany

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