Process-structure linkage is one of the most important topics in materials science due to the fact that virtually all information related to the materials, including manufacturing processes, lies in the microstructure itself. Therefore, to learn more about the process, one must start by thoroughly examining the microstructure. This gives rise to inverse problems in the context of process-structure linkages, which attempt to identify the processes that were used to manufacturing the given microstructure. In this work, we present an inverse problem for structure-process linkages which we solve using asynchronous parallel Bayesian optimization which exploits parallel computing resources. We demonstrate the effectiveness of the method using kinetic Monte Carlo model for grain growth simulation.
Software development for high-performance scientific computing continues to evolve in response to increased parallelism and the advent of on-node accelerators, in particular GPUs. While these hardware advancements have the potential to significantly reduce turnaround times, they also present implementation and design challenges for engineering codes. We investigate the use of two strategies to mitigate these challenges: the Kokkos library for performance portability across disparate architectures, and the DARMA/vt library for asynchronous many-task scheduling. We investigate the application of Kokkos within the NimbleSM finite element code and the LAMÉ constitutive model library. We explore the performance of DARMA/vt applied to NimbleSM contact mechanics algorithms. Software engineering strategies are discussed, followed by performance analyses of relevant solid mechanics simulations which demonstrate the promise of Kokkos and DARMA/vt for accelerated engineering simulators.
The US Department of Energy (DOE) Nuclear Energy Research Initiative funded the design and construction of the Seven Percent Critical Experiment (7uPCX) at Sandia National Laboratories. The start-up of the experiment facility and the execution of the experiments described here were funded by the DOE Nuclear Criticality Safety Program. The 7uPCX is designed to investigate critical systems with fuel for light water reactors in the enrichment range above 5% 235U. The 7uPCX assembly is a water-moderated and -reflected array of aluminum-clad square-pitched U(6.90%)O2 fuel rods. Other critical experiments performed in the 7uPCX assembly are documented in LEU-COMP-THERM-078, LEU-COMP-THERM-080, LEU-COMPTHERM- 096, LEU-COMP-THERM-097, and LEU-COMP-THERM-101. The twenty-seven critical experiments in this series were performed in 2020 in the SCX at the Sandia Pulsed Reactor Facility. The experiments are grouped by fuel rod pitch. Case 1 is a base case with a pitch of 0.8001 cm and no water holes in the array. Cases 2 through 6 have the same pitch as Case 1 but contain various configurations with water holes, providing slight variations in the fuel-to-water ratio. Similarly, Case 7 is a base case with a pitch of 0.854964 cm and no water holes in the array. Cases 8 through 11 have the same pitch as Case 7 but contain various configurations with water holes. Cases 12 through 15 have a pitch of 1.131512 cm and differ according to the number of water holes in the array, with Case 12 having no water holes. Cases 16 through 19 have a pitch of 1.209102 cm and differ according to number of water holes in the array, with Case 16 having no water holes. Cases 20 through 23 have a pitch of 1.6002 cm and differ according to number of water holes in the array, with Case 20 having no water holes. Cases 24 through 27 have a pitch of 1.709928 cm and differ according to number of water holes in the array, with Case 24 having no water holes. As the experiment case number increases, the fuel-to-water volume ratio decreases.
This document summarily provides brief descriptions of the MELCOR code enhancement made between code revision number 14959and 18019. Revision 14959 represents the previous official code release; therefore, the modeling features described within this document are provided to assist users that update to the newest official MELCOR code release, 18019. Along with the newly updated MELCOR Users Guide and Reference Manual, users are aware and able to assess the new capabilities for their modeling and analysis applications.
Producing and distributing COVID-19 vaccine during the pandemic is a major logistical challenge requiring careful planning and efficient execution. This report presents information on logistical, policy and technical issues relevant to rapidly fielding a COVID-19 vaccination program. For this study we (a) conducted literature review and subject matter expert elicitation to understand current vaccine manufacturing and distribution capabilities and vaccine allocation strategies, (b) designed a baseline vaccine distribution strategy and modeling strategy to provide insight into the potential for targeted distribution of limited initial vaccine supplies, and (c) developed parametric interfaces to enable vaccine distribution scenarios to be analyzed in depth with Sandias Adaptive Recovery Model that will allow us evaluate the additional sub- populations and alternative distribution scenarios from a public health benefit and associated economic disruption Principal issues, challenges, and complexities that complicate COVID-19 vaccine delivery identified in our literature and subject matter expert investigation include these items: The United States has not mounted an urgent nationwide vaccination campaign in recent history. The existing global manufacturing and distribution infrastructure are not able to produce enough vaccine for the population immediately. Vaccines, once available will be scarce resources. Prioritization for vaccine allocation will be built on existing distribution networks. Vaccine distribution may not have a universal impact on disease transmission and morbidity because of scarcity, priority population demographics, and underlying disease transmission rates. Considerations for designing a vaccine distribution strategy are discussed. A baseline distribution strategy is designed and tested using the Adaptive Recovery Model, which couples a deterministic compartmental epidemiological model and a stochastic network model. We show the impact of this vaccine distribution strategy on hospitalizations, mortality, and contact tracing requirements. This model can be used to quantitatively evaluate alternative distribution scenarios, guiding policy decisions as vaccine candidates are narrowed down.
The equation of state (EOS) and shock compression of bulk vanadium were investigated using canonical ab initio molecular dynamic simulations, with experimental validation to 865 GPa from shock data collected at Sandia's Z Pulsed Power Facility. In simulations the phase space was sampled along isotherms ranging from 3000 K to 50000 K, for densities between -ü=3 and 15g/cm3, with a focus on the liquid regime and the body-centered-cubic phase in the vicinity of the melting limit. The principal Hugoniot predicted from first principles is overall consistent with shock data, while it showed that current multiphase SESAME-type EOS for vanadium needed revision in the liquid regime. A more accurate SESAME EOS was developed using constraints from experiments and simulations. This work emphasizes the need to use a combined theoretical and experimental approach to develop high-fidelity EOS models for extreme conditions.
Additive Manufacturing (AM) techniques are increasingly being utilized for energetic material processes and research. Energetic material samples fabricated using these techniques can develop artifacts or defects during the manufacturing process. In this work, we use Physical Vapor Deposition (PVD) of explosive samples as a model system to investigate the effects of typical AM artifact or defect geometries on detonation propagation. PVD techniques allow for precise control of geometry to simulate typical AM artifacts or defects embedded into explosive samples. This experiment specifically investigates triangular and diamond-shaped artifacts that can result during direct-ink-writing (Robocasting). Samples were prepared with different sizes of voids embedded into the films. An ultra-high-speed framing camera and streak camera were used to view the samples under dynamic shock loading. It was determined that both geometry and size of the defects have a significant impact on the detonation front.
Arm processors have been explored in HPC for several years, however there has not yet been a demonstration of viability for supporting large-scale production workloads. In this paper, we offer a retrospective on the process of bringing up Astra, the first Petascale supercomputer based on 64-bit Arm processors, and validating its ability to run production HPC applications. Through this process several immature technology gaps were addressed, including software stack enablement, Linux bugs at scale, thermal management issues, power management capabilities, and advanced container support. From this experience, several lessons learned are formulated that contributed to the successful deployment of Astra. These insights can be helpful to accelerate deploying and maturing other first-seen HPC technologies. With Astra now supporting many users running a diverse set of production applications at multi-thousand node scales, we believe this constitutes strong supporting evidence that Arm is a viable technology for even the largest-scale supercomputer deployments.