![]() While mean grain area after 1 mm of build is found to be sensitive to N0 and S0, particularly at small N0 and large S0 (despite some convergence toward similar values), the resulting grain shapes and overall textures develop in a reasonably similar manner. In addition to “random” uncertainty (due to random number generation in the orientations and locations of grains present), the heterogeneous nucleation density N0 and the mean substrate grain spacing S0 are varied to examine their impact of grain area development as a function of build height in the simulated microstructure. The OpenFOAM model for process conditions, the ExaCA model for as-solidified grain structure, and the ExaConstit model for constitutive mechanical properties are used as part of the ExaAM modeling framework to examine a few of the various sources of uncertainty in the modeling workflow. Future work will include improving the strong scaling of ExaCA on GPUs by reducing load imbalance associated with the locality of the problem, and continuing performance optimization across exascale hardware.Ĭoupled process–microstructure–property modeling, and understanding the sources of uncertainty and their propagation toward error in part property prediction, are key steps toward full utilization of additive manufacturing (AM) for predictable quality part development. The improved performance of CA through GPU utilization and the performance portable nature of ExaCA will enable accurate part-scale modeling by harnessing the power of current and future generations of high performance computing resources. Testing showed comparable CPU performance to the MPI-only CA code and a 5-20x speedup when running AM-based test problems using GPUs. Performance testing of ExaCA on Summit (a pre-exascale machine at Oak Ridge National Laboratory) was used to quantify CPU–GPU speedup comparing with equal numbers of nodes. We detail the steps taken to transform a baseline, MPI-based CA code into one that is performant on CPUs and GPUs. The CA-based code is parallelized using MPI and the Kokkos programming model, the latter enabling simulation on both CPUs and GPUs within a single-source implementation. As part of the ExaAM project, an initiative within the Exascale Computing Project (ECP) to develop, test, and optimize an exascale-capable coupled and self-consistent model of AM parts, we developed ExaCA () for the liquid–solid phase transformation in the wake of AM melt pools. While cellular automata (CA)-based models have proven able to predict aspects of microstructure for several alloys and AM process conditions, long run times and large resource sets required limit the utility and the problem size to which existing CA models can be applied. Good agreement is obtained between the experimental and numerical results.Modeling the as-solidified grain structures that form during alloy processing is a critical component in understanding process-property relationships, particularly for additive manufacturing (AM) where grain structure is very sensitive to processing conditions. ![]() Based on the relationship, the CA model can be used to continuously predict the corrosion behaviors of WS in the long-term atmospheric corrosion process. Finally, the parameter analysis is performed and the relationship between CA model and atmospheric exposure test is established to achieve the model application. ![]() ![]() Results show that the basic hypotheses and evolution rules are reasonable and the model is reliable, and thus can be used to describe and to identify the corrosion kinetics, corrosion morphology, and distribution characteristics of pits for WS in atmosphere. Secondly, the results obtained from the CA model are compared with the basic corrosion laws verified by several experiments. The evolution rules of atmospheric corrosion are then proposed and the software program is developed. Basic assumptions are made with the purpose of simplifying the complicated corrosion process. Firstly, the atmospheric corrosion mechanism of WS is described. In order to reproduce the atmospheric corrosion process of weathering steel (WS), a three-dimensional (3D) cellular automata (CA) based method is proposed.
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