Publications

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Temperature measurements are very important in shock and ramp type dynamic materials experiments. In particular, accurate temperature measurements can provide stringent additional constraints on determining the equation of state for materials at high pressure. The key to providing these constraints is to develop diagnostic techniques that can determine the temperature with sufficient accuracy. To enable such measurements, we are working to improve our diagnostic capability with three separate techniques, each of which has specific applicability in a particular temperature range. To improve our capability at low temperatures (< 1 eV) we are working on a technique that takes advantage of the change in reflectivity of Au as the temperature is increased. This is most applicable to ramp type experiments. In the intermediate range (~1 eV < T< 5-10 eV) we are improving our optical pyrometry diagnostic by adding the capability of doing an absolute calibration as part of the diagnostic procedure for the shock or shock ramp dynamic materials experiment. This will enable more accurate temperature measurements for shock and shock ramp type experiments. For higher temperatures that occur in very high-pressure shock experiments, above 10 eV, we are developing the capability of doing x-ray Thomson scattering measurements. Such measurements will enable us to characterize strongly shocked or warm dense matter materials. Work on these diagnostic approaches is summarized in this report.

This report presents a specification for the Portals 4 network programming interface. Portals 4 is intended to allow scalable, high-performance network communication between nodes of a parallel computing system. Portals 4 is well suited to massively parallel processing and embedded systems. Portals 4 represents an adaption of the data movement layer developed for massively parallel processing platforms, such as the 4500-node Intel TeraFLOPS machine. Sandia's Cplant cluster project motivated the development of Version 3.0, which was later extended to Version 3.3 as part of the Cray Red Storm machine and XT line. Version 4 is targeted to the next generation of machines employing advanced network interface architectures that support enhanced offload capabilities.

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The ThermalDiffusion class was created to simulate one-dimensional thermal diffusion across one or more material layers. Each layer is assumed to have constant conductivity K and diffusivity κ . Interface conductance between layers may be specified. Internal heating as a function of position and time is also supported. The ThermalDiffusion class is included in the SMASH package [1] as part of the PDE (Partial Differential Equation) subpackage.

This report describes the preliminary implementation of the sodium thermophysical properties and the design documentation for the sodium models of CONTAIN-LMR to be implemented into MELCOR 2.1. In the past year, the implementation included two separate sodium properties from two different sources. The first source is based on the previous work done by Idaho National Laboratory by modifying MELCOR to include liquid lithium equation of state as a working fluid to model the nuclear fusion safety research. To minimize the impact to MELCOR, the implementation of the fusion safety database (FSD) was done by utilizing the detection of the data input file as a way to invoking the FSD. The FSD methodology has been adapted currently for this work, but it may subject modification as the project continues. The second source uses properties generated for the SIMMER code. Preliminary testing and results from this implementation of sodium properties are given. In this year, the design document for the CONTAIN-LMR sodium models, such as the two condensable option, sodium spray fire, and sodium pool fire is being developed. This design document is intended to serve as a guide for the MELCOR implementation. In addition, CONTAIN-LMR code used was based on the earlier version of CONTAIN code. Many physical models that were developed since this early version of CONTAIN may not be captured by the code. Although CONTAIN 2, which represents the latest development of CONTAIN, contains some sodium specific models, which are not complete, the utilizing CONTAIN 2 with all sodium models implemented from CONTAIN-LMR as a comparison code for MELCOR should be done. This implementation should be completed in early next year, while sodium models from CONTAIN-LMR are being integrated into MELCOR. For testing, CONTAIN decks have been developed for verification and validation use.

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Recent advances in nanotechnology have enabled researchers to control individual quantum mechanical objects with unprecedented accuracy, opening the door for both quantum and extreme- scale conventional computation applications. As these devices become more complex, designing for facility of control becomes a daunting and computationally infeasible task. Here, motivated by ideas from compressed sensing, we introduce a protocol for the Compressed Optimization of Device Architectures (CODA). It leads naturally to a metric for benchmarking and optimizing device designs, as well as an automatic device control protocol that reduces the operational complexity required to achieve a particular output. Because this protocol is both experimentally and computationally efficient, it is readily extensible to large systems. For this paper, we demonstrate both the bench- marking and device control protocol components of CODA through examples of realistic simulations of electrostatic quantum dot devices, which are currently being developed experimentally for quantum computation.

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Streak cameras are important for high-speed data acquisition in single event experiments, where the total recorded information (I) is shared between the number of measurements (M) and the number of samples (S). Topics of this meeting included: streak camera use at the national laboratories; current streak camera production; new tube developments and alternative technologies; and future planning. Each topic is summarized in the following sections.

As computer systems grow in both size and complexity, the need for applications and run-time systems to adjust to their dynamic environment also grows. The goal of the RAAMP LDRD was to combine static architecture information and real-time system state with algorithms to conserve power, reduce communication costs, and avoid network contention. We devel- oped new data collection and aggregation tools to extract static hardware information (e.g., node/core hierarchy, network routing) as well as real-time performance data (e.g., CPU uti- lization, power consumption, memory bandwidth saturation, percentage of used bandwidth, number of network stalls). We created application interfaces that allowed this data to be used easily by algorithms. Finally, we demonstrated the benefit of integrating system and application information for two use cases. The first used real-time power consumption and memory bandwidth saturation data to throttle concurrency to save power without increasing application execution time. The second used static or real-time network traffic information to reduce or avoid network congestion by remapping MPI tasks to allocated processors. Results from our work are summarized in this report; more details are available in our publications [2, 6, 14, 16, 22, 29, 38, 44, 51, 54].