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.
#3D CELLULAR AUTOMATON FULL#
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.
#3D CELLULAR AUTOMATON PORTABLE#
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.
#3D CELLULAR AUTOMATON CODE#
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. By manipulating the relations between components one can control the properties of individual objects, evaluate the results and, correspondingly, customize the solutions.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. This computational design process incorporates CA and complex adaptive system together to allow us to generate complex 3D shapes with formal and procedural flexibility. The cells of the CA represent 3D spatial units with programmatic characteristics (e.g., housing units, rooms, public spaces, circulation spaces, etc.), which results in functionally deterministic outputs. The complex adaptations are based on more complicated rules resulting in sophisticated interactions, thus producing abstract spatial configurations that can serve as a functional layout for the more detailed development of the architectural or urban form. As shown in the diagrams above, the status of cells can be applied with various types of adaptations, thus generating a more complex system that can bind the form of the cells with more functions. Architects’ interest in CA is typically motivated by the simplicity of CA mechanisms on one hand and the potential complexity of generated outcomes on the othe r. In bottom-up generative design, rules are iteratively applied to each generation which results in outcome forms that are initially difficult to predict.
With the same configurational rule, different initiations will generate diverse results.
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