Multiphase Field Simulation of Austenite-to-Ferrite Transformation Accelerated by GPU Computing

Abstract

The multiphase field (MPF) method is recognized as a powerful numerical method to simulate microstructural evolutions, such as solidification, grain growth, recrystallization, and phase transformation, in various materials. However, because we need to solve the time evolution equations for multiple-order parameters derived from the total Gibbs free energy, MPF simulations are very computationally expensive. In this paper, we use a graphics processing unit (GPU) to accelerate the two-dimensional MPF simulation of austenite-to-ferrite transformation in a Fe-C alloy. This is an important phenomenon for predicting the morphology of multiphase microstructures in steel. To perform the MPF simulation on an NVIDIA GPU, the program code is developed in CUDA Fortran. Using this code, the acceleration performance of the GPU implementation is evaluated, and our results demonstrate that the GPU computation can powerfully accelerate the MPF simulation by introducing an active parameter tracking (APT) method, which is used to reduce the computational load and memory consumption. The performance of the GPU computation with APT achieves a speedup factor of 5 compared with the GPU computation without APT and a speedup factor of 15 compared with the basic CPU computation without the APT method.

Recursive grain remapping scheme for phase‐field models of additive manufacturing

“…There have also been schemes proposed in the literature that employ sparse or truncated representations of the microstructure. One such approach is known as active parameter tracking (APT), in which the polynomial that represents the local free energy density is truncated at a fixed number of OPs 35‐39 . Rather than remapping whole grains, APT only retains the largest OPs above a preset tolerance at each point in the simulation mesh.…”

“…One such approach is known as active parameter tracking (APT), in which the polynomial that represents the local free energy density is truncated at a fixed number of OPs. [35][36][37][38][39] Rather than remapping whole grains, APT only retains the n largest OPs above a preset tolerance at each point in the simulation mesh. As time evolves, the algorithm constantly updates what grains are associated with the n largest OPs and does not necessarily solve the evolution equations for the remaining features.…”