Materials designed for nuclear reactors undergo microstructural changes resulting from a combination of several environmental factors, including neutron irradiation damage, gas accumulation and elevated temperatures. Typical ion beam irradiation experiments designed for simulating a neutron irradiation environment involve irradiating the sample with a single ion beam and subsequent characterization of the resulting microstructure, often by transmission electron microscopy (TEM). This method does not allow for examination of microstructural effects due to simultaneous gas accumulation and displacement cascade damage, which occurs in a reactor. Sandia's in situ ion irradiation TEM (I3TEM) offers the unique ability to observe microstructural changes due to irradiation damage caused by concurrent multi-beam ion irradiation in real time. This allows for time-dependent microstructure analysis. A plethora of additional in situ stages can be coupled with these experiments, e.g.; for more accurately simulating defect kinetics at elevated reactor temperatures. This work outlines experiments showing synergistic effects in Au using in situion irradiation with various combinations of helium, deuterium and Au ions, as well as some initial work on materials utilized in tritium-producing burnable absorber rods (TPBARs): zirconium alloys and LiAlO2.
We have demonstrated a scalable dual-band tunable infrared filter based on gating of a continuous graphene layer. The measured and modeled response of the device are in good agreement.
With the increased scale expected on future leadership-class systems, detailed information about the resource usage and performance of MPI message matching provides important insights into how to maintain application performance on next-generation systems. However, obtaining MPI message matching performance data is often not possible without significant effort. A common approach is to instrument an MPI implementation to collect relevant statistics. While this approach can provide important data, collecting matching data at runtime perturbs the application’s execution, including its matching performance, and is highly dependent on the MPI library’s matchlist implementation. In this paper, we introduce a trace-based simulation approach to obtain detailed MPI message matching performance data for MPI applications without perturbing their execution. Using a number of key parallel workloads, we demonstrate that this simulator approach can rapidly and accurately characterize matching behavior. Specifically, we use our simulator to collect several important statistics about the operation of the MPI posted and unexpected queues. For example, we present data about search lengths and the duration that messages spend in the queues waiting to be matched. Data gathered using this simulation-based approach have significant potential to aid hardware designers in determining resource allocation for MPI matching functions and provide application and middleware developers with insight into the scalability issues associated with MPI message matching.
While large-scale simulations have been the hallmark of the High Performance Computing (HPC) community for decades, Large Scale Data Analytics (LSDA) workloads are gaining attention within the scientific community not only as a processing component to large HPC simulations, but also as standalone scientific tools for knowledge discovery. With the path towards Exascale, new HPC runtime systems are also emerging in a way that differs from classical distributed computing models. However, system software for such capabilities on the latest extreme-scale DOE supercomputing needs to be enhanced to more appropriately support these types of emerging software ecosystems.In this paper, we propose the use of Virtual Clusters on advanced supercomputing resources to enable systems to support not only HPC workloads, but also emerging big data stacks. Specifically, we have deployed the KVM hypervisor within Cray's Compute Node Linux on a XC-series supercomputer testbed. We also use libvirt and QEMU to manage and provision VMs directly on compute nodes, leveraging Ethernet-over-Aries network emulation. To our knowledge, this is the first known use of KVM on a true MPP supercomputer. We investigate the overhead our solution using HPC benchmarks, both evaluating single-node performance as well as weak scaling of a 32-node virtual cluster. Overall, we find single node performance of our solution using KVM on a Cray is very efficient with near-native performance. However overhead increases by up to 20% as virtual cluster size increases, due to limitations of the Ethernet-over-Aries bridged network. Furthermore, we deploy Apache Spark with large data analysis workloads in a Virtual Cluster, effectively demonstrating how diverse software ecosystems can be supported by High Performance Virtual Clusters.
Qi, Zhenyi; Wu, X.; Ward, Daniel R.; Prance, J.R.; Kim, Dohun; Laros, James H.; Mohr, R.T.; Shi, Zhan; Savage, D.E.; Lagally, M.G.; Eriksson, M.A.; Coppersmith, S.N.; Friesen, Mark; Vavilov, M.G.
We study the dynamics of a pulse-gated semiconductor double-quantum-dot qubit. In our experiments, the qubit coherence times are relatively long, but the visibility of the quantum oscillations is low. We show that these observations are consistent with a theory that incorporates decoherence arising from charge noise that gives rise to detuning fluctuations of the double dot. Because effects from charge noise are largest near the singlet-triplet avoided level crossing, the visibility of the oscillations is low when the singlet-triplet avoided level crossing occurs in the vicinity of the charge degeneracy point crossed during the manipulation, but there is only modest dephasing at the large detuning value at which the quantum phase accumulates. This theory agrees well with experimental data and predicts that the visibility can be increased greatly by appropriate tuning of the interdot tunneling rate.
A systematic approach is presented for increasing the concentration of redox-active species in electrolytes for nonaqueous redox flow batteries (RFBs). Starting with an ionic liquid consisting of a metal coordination cation (MetIL), ferrocene-containing ligands and iodide anions are substituted incrementally into the structure. While chemical structures can be drawn for molecules with 10 m redox-active electrons (RAE), practical limitations such as melting point and phase stability constrain the structures to 4.2 m RAE, a 2.3× improvement over the original MetIL. Dubbed “MetILs3,” these ionic liquids possess redox activity in the cation core, ligands, and anions. Throughout all compositions, infrared spectroscopy shows the ethanolamine-based ligands primarily coordinate to the Fe2+ core via hydroxyl groups. Calorimetry conveys a profound change in thermophysical properties, not only in melting temperature but also in suppression of a cold crystallization only observed in the original MetIL. Square wave voltammetry reveals redox processes characteristic of each molecular location. Testing a laboratory-scale RFB demonstrates Coulombic efficiencies >95% and increased voltage efficiencies due to more facile redox kinetics, effectively increasing capacity 4×. Application of this strategy to other chemistries, optimizing melting point and conductivity, can yield >10 m RAE, making nonaqueous RFB a viable technology for grid scale storage.
This project investigated a recently patented Sandia technology known as visible light Laser Voltage Probing (LVP). In this effort we carefully prepared well understood and characterized samples for testing. These samples were then operated across a range of configurations to minimize the possibility of superposition of multiple photon carrier interactions as data was taken with conventional and visible light LVP systems. Data consisted of LVP waveforms and Laser Voltage Images (LVI). Visible light (633 nm) LVP data was compared against 1319 nm and 1064 nm conventional LVP data to better understand the similarities and differences in mechanisms for all wavelengths of light investigated. The full text can be obtained by reaching the project manager, Ed Cole or the Cyber IA lead, Justin Ford.
The overall goal of this work was to utilize the Advanced Power Management (APM) capabilities of the ATS-1 Trinity platform to understand the power usage behavior of ASC workloads running on Trinity and gain insight into the potential for utilizing power management techniques on future ASC platforms.
This report summarizes the work performed as part of a FY17 CSSE L2 milestone to in- vestigate the power usage behavior of ASC workloads running on the ATS-1 Trinity plat- form. Techniques were developed to instrument application code regions of interest using the Power API together with the Kokkos profiling interface and Caliper annotation library. Experiments were performed to understand the power usage behavior of mini-applications and the SNL/ATDM SPARC application running on ATS-1 Trinity Haswell and Knights Landing compute nodes. A taxonomy of power measurement approaches was identified and presented, providing a guide for application developers to follow. Controlled scaling study experiments were performed on up to 2048 nodes of Trinity along with smaller scale ex- periments on Trinity testbed systems. Additionally, power and energy system monitoring information from Trinity was collected and archived for post analysis of "in-the-wild" work- loads. Results were analyzed to assess the sensitivity of the workloads to ATS-1 compute node type (Haswell vs. Knights Landing), CPU frequency control, node-level power capping control, OpenMP configuration, Knights Landing on-package memory configuration, and algorithm/solver configuration. Overall, this milestone lays groundwork for addressing the long-term goal of determining how to best use and operate future ASC platforms to achieve the greatest benefit subject to a constrained power budget.
As part of Sandia’s nuclear deterrence mission, the B61-12 Life Extension Program (LEP) aims to modernize the aging weapon system. Modernization requires requalification and Sandia is using high performance computing to perform advanced computational simulations to better understand, evaluate, and verify weapon system performance in conjunction with limited physical testing. The Nose Bomb Subassembly (NBSA) of the B61-12 is responsible for producing a fuzing signal upon ground impact. The fuzing signal is dependent upon electromechanical impact sensors producing valid electrical fuzing signals at impact. Computer generated models were used to assess the timing between the impact sensor’s response to the deceleration of impact and damage to major components and system subassemblies. The modeling and simulation team worked alongside the physical test team to design a large-scale reverse ballistic test to not only assess system performance, but to also validate their computational models. The reverse ballistic test conducted at Sandia’s sled test facility sent a rocket sled with a representative target into a stationary B61-12 (NBSA) to characterize the nose crush and functional response of NBSA components. Data obtained from data recorders and high-speed photometrics were integrated with previously generated computer models in order to refine and validate the model’s ability to reliably simulate real-world effects. Large-scale tests are impractical to conduct for every single impact scenario. By creating reliable computer models, we can perform simulations that identify trends and produce estimates of outcomes over the entire range of required impact conditions. Sandia’s HPCs enable geometric resolution that was unachievable before, allowing for more fidelity and detail, and creating simulations that can provide insight to support evaluation of requirements and performance margins. As computing resources continue to improve, researchers at Sandia are hoping to improve these simulations so they provide increasingly credible analysis of the system response and performance over the full range of conditions.
Hydrodynamic instability growth is a fundamentally limiting process in many applications. In High Energy Density Physics (HEDP) systems such as inertial confinement fusion implosions and stellar explosions, hydro instabilities can dominate the evolution of the object and largely determine the final state achievable. Of particular interest is the process by which instabilities cause perturbations at a density or material interface to grow nonlinearly, introducing vorticity and eventually causing the two species to mix across the interface. Although quantifying instabilities has been the subject of many investigations in planar geometry, few have been done in converging geometry. During FY17, the team executed six convergent geometry instability experiments. Based on earlier results, the platform was redesigned and improved with respect to load centering at installation making the installation reproducible and development of a new 7.2 keV, Co He-a backlighter system to better penetrate the liner. Together, the improvements yielded significantly improved experimental results. The results in FY17 demonstrate the viability of using experiments on Z to quantify instability growth in cylindrically convergent geometry. Going forward, we will continue the partnership with staff and management at LANL to analyze the past experiments, compare to hydrodynamics growth models, and design future experiments.
The heterogeneity in mechanical fields introduced by microstructure plays a critical role in the localization of deformation. To resolve this incipient stage of failure, it is therefore necessary to incorporate microstructure with sufficient resolution. On the other hand, computational limitations make it infeasible to represent the microstructure in the entire domain at the component scale. In this study, the authors demonstrate the use of concurrent multi- scale modeling to incorporate explicit, finely resolved microstructure in a critical region while resolving the smoother mechanical fields outside this region with a coarser discretization to limit computational cost. The microstructural physics is modeled with a high-fidelity model that incorporates anisotropic crystal elasticity and rate-dependent crystal plasticity to simulate the behavior of a stainless steel alloy. The component-scale material behavior is treated with a lower fidelity model incorporating isotropic linear elasticity and rate-independent J 2 plas- ticity. The microstructural and component scale subdomains are modeled concurrently, with coupling via the Schwarz alternating method, which solves boundary-value problems in each subdomain separately and transfers solution information between subdomains via Dirichlet boundary conditions. Beyond cases studies in concurrent multiscale, we explore progress in crystal plastic- ity through modular designs, solution methodologies, model verification, and extensions to Sierra/SM and manycore applications. Advances in conformal microstructures having both hexahedral and tetrahedral workflows in Sculpt and Cubit are highlighted. A structure-property case study in two-phase metallic composites applies the Materials Knowledge System to local metrics for void evolution. Discussion includes lessons learned, future work, and a summary of funded efforts and proposed work. Finally, an appendix illustrates the need for two-way coupling through a single degree of freedom.
VISAR measurements are typically analyzed in the time domain, where velocity is approximately proportional to fringe shift. Moving to the frequency domain clarifies the limitations of this approximation and suggests several improvements. For example, optical dispersion preserves high-frequency information, so a zero-dispersion (air delay) interferometer does not provide optimal time resolution. Combined VISAR measurements can also improve time resolution. With adequate bandwidth and reasonable noise levels, it is quite possible to achieve better resolution than the VISAR approximation allows.