The International Conference for High Performance Computing, Networking, Storage, and Analysis
The International Conference for High Performance Computing, Networking, Storage, and Analysis
Workshop: 1st Workshop on Enabling Predictive Science with Optimization and Uncertainty Quantification in HPC
Authors: Robert Carson (Lawrence Livermore National Laboratory), Matt Rolchigo and John Coleman (Oak Ridge National Laboratory), Mikhail Titov (Brookhaven National Laboratory), Jim Belak (Lawrence Livermore National Laboratory), and Matt Bement (Oak Ridge National Laboratory)
Abstract: Metal additive manufacturing is a disruptive manufacturing technology that opens the design space for parts outside those possible from traditional manufacturing methods. In order to accelerate industry and R&D needs to certify AM parts, the ExaAM project has developed a suite of exascale-ready computational tools to model the process-to-structure-to-properties relationship for additively manufactured metal components. One tool is a UQ pipeline to quantify the effect uncertainty in processing conditions has on local mechanical responses. We present an overview of this pipeline and its codes. Using ORNL’s exascale computer, Frontier, we utilize this pipeline to cross multiple length and time scales to predict local mechanical response of a location within a complex AM bridge part, AMB2018-01 produced by NIST as part of their 2018 AM-Bench test series. Our results are then compared to experimental mechanical tests of parts from the NIST build to quantify the error in the ExaAM UQ workflow.