Publications

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This communication is the final report for the project Utilizing Highly Scattered Light for Intelligence through Aerosols funded by the Laboratory Directed Research and Development (LDRD) program at Sandia National Laboratories and lasting six months in 2019. Aerosols like fog reduce visibility and cause down-time that for critical systems or operations are unacceptable. Information is lost due to the random scattering and absorption of light by tiny particles. Computational diffuse optical imaging methods show promise for interpreting the light transmitted through fog, enabling sensing and imaging to improve situational awareness at depths 10 times greater than current methods. Developing this capability first requires verification and validation of diffusion models of light propagation in fog. For this reason, analytical models were developed and compared to experimental data captured at the Sandia National Laboratory Fog Chamber facility. A methodology was developed to incorporate the propagation of scattered light through the imaging optics to a pixel array. The diffusion approximation to the radiative transfer equation was found to predict light propagation in fog under the appropriate conditions.

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In the summer of 2020, the National Aeronautics and Space Administration (NASA) plans to launch a spacecraft as part of the Mars 2020 mission. The rover on the proposed spacecraft will use a Multi-Mission Radioisotope Thermoelectric Generator (MMRTG) to provide continuous electrical and thermal power for the mission. The MMRTG uses radioactive plutonium dioxide. NASA is preparing a Supplemental Environmental Impact Statement (SEIS) for the mission in accordance with the National Environmental Policy Act. This Nuclear Risk Assessment addresses the responses of the MMRTG option to potential accident and abort conditions during the launch opportunity for the Mars 2020 mission and the associated consequences. This information provides the technical basis for the radiological risks discussed in the SEIS.

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Sandia's Z Pulsed Power Facility is able to dynamically compress matter to extreme states with exceptional uniformity, duration, and size, which are ideal for investigations of fundamental material properties of high energy density conditions. X-ray diffraction (XRD) is a key atomic scale probe since it provides direct observation of the compression and strain of the crystal lattice, and is used to detect, identify, and quantify phase transitions. Because of the destructive nature of Z-Dynamic Materials Properties (DMP) experiments and low signal vs background emission levels of XRD, it is very challenging to detect the XRD pattern close to the Z-DMP load and to recover the data. We developed a new Spherical Crystal Diffraction Imager (SCDI) diagnostic to relay and image the diffracted x-ray pattern away from the load debris field. The SCDI diagnostic utilizes the Z-Beamlet laser to generate 6.2-keV Mn-He c , x-rays to probe a shock-compressed sample on the Z-DMP load. A spherically bent crystal composed of highly oriented pyrolytic graphite is used to collect and focus the diffracted x-rays into a 1-inch thick tungsten housing, where an image plate is used to record the data. We performed experiments to implement the SCDI diagnostic on Z to measure the XRD pattern of shock compressed beryllium samples at pressures of 1.8-2.2 Mbar.

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In this investigation a series of small-scale tests were conducted, which were sponsored by the Nuclear Regulatory Commission (NRC) Office of Nuclear Regulatory Research (RES) and performed at Sandia National Laboratories (SNL). These tests were designed to better understand localized particle dispersion phenomena resulting from electrical arcing faults. The purpose of these tests was to better characterize aluminum particle size distribution, rates of production, and morphology (agglomeration) of electrical arc faults. More specifically, this effort characterized ejected particles and high-energy dispersion, where this work characterized HEAF electrical characteristics, particle movement/distributions, and morphology near the arc. The results and measurements techniques from this investigation will be used to inform an energy balance model to predict additional energy from aluminum involvement in the arc fault. The experimental setup was developed based on prior work by KEMA and SNL for phase-to-ground and phase-to-phase electrical circuit faults. The small-scale tests results should not be expected to be scale-able to the hazards associated with full-scale HEAF events. Here, the test voltages will consist of four different levels: 480V, 4160V, 6900V and 10kV, based on those realized in nuclear power plant (NPP) HEAF events.

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We present a new method for reducing parallel applications’ communication time by mapping their MPI tasks to processors in a way that lowers the distance messages travel and the amount of congestion in the network. Assuming geometric proximity among the tasks is a good approximation of their communication interdependence, we use a geometric partitioning algorithm to order both the tasks and the processors, assigning task parts to the corresponding processor parts. In this way, interdependent tasks are assigned to “nearby” cores in the network. We also present a number of algorithmic optimizations that exploit specific features of the network or application to further improve the quality of the mapping. We specifically address the case of sparse node allocation, where the nodes assigned to a job are not necessarily located in a contiguous block nor within close proximity to each other in the network. However, our methods generalize to contiguous allocations as well, and results are shown for both contiguous and non-contiguous allocations. We show that, for the structured finite difference mini-application MiniGhost, our mapping methods reduced communication time up to 75 percent relative to MiniGhost’s default mapping on 128K cores of a Cray XK7 with sparse allocation. For the atmospheric modeling code E3SM/HOMME, our methods reduced communication time up to 31% on 16K cores of an IBM BlueGene/Q with contiguous allocation.