• Hallberg, Håkan
• Mellbin, Y.
• Ristinmaa, M.

Abstract

<jats:title>SUMMARY</jats:title><jats:p>Crystal plasticity models are often used to model the deformation behavior of polycrystalline materials. One major drawback with such models is that they are computationally very demanding. Adopting the common Taylor assumption requires calculation of the response of several hundreds of individual grains to obtain the stress in a single integration point in the overlying FEM structure. However, a large part of the operations can be executed in parallel to reduce the computation time. One emerging technology for running massively parallel computations without having to rely on the availability of large computer clusters is to port the parallel parts of the calculations to a graphical processing unit (GPU). GPUs are designed to handle vast numbers of floating point operations in parallel. In the present work, different strategies for the numerical implementation of crystal plasticity are investigated as well as a number of approaches to parallelization of the program execution. It is identified that a major concern is the limited amount of memory available on the GPU. However, significant reductions in computational time‐up to 100 times speedup‐are achieved in the present study, and possible also on a standard desktop computer equipped with a GPU. Copyright © 2014 John Wiley &amp; Sons, Ltd.</jats:p>

Topics

• impedance spectroscopy
• cluster
• grain
• simulation
• plasticity
• crystal plasticity