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Updates to SNL Nuclear Criticality Safety Benchmark Suite for MCNP

Depriest, Kendall R.; Miller, John A.; Henderson, Shawn J.

Members of the Nuclear Criticality Safety (NCS) Program at Sandia National Laboratories (SNL) have updated the suite of benchmark problems developed to validate MCNP6 Version 2.0 for use in NCS applications. The updated NCS benchmark suite adds approximately 600 new benchmarks and includes peer review of all input files by two different NCS engineers (or one NCS engineer and one candidate NCS engineer). As with the originally released benchmark suite, the updated suite covers a broad range of fissile material types, material forms, moderators, reflectors, and neutron energy spectra. The benchmark suite provides a basis to establish a bias and bias uncertainty for use in NCS analyses at SNL.

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TYPE SAND Report YEAR 2022
DOIOSTI

Metal Hydride Compressor for High-Pressure (875 bar) Hydrogen Delivery

Johnson, Terry; Bays, Nathan R.; Bowman, Robert C.; Smith, D.B.; Anovitz, Lawrence M.; Jensen, Craig M.

Metal hydride hydrogen compression utilizes a reversible heat-driven interaction of a hydride-forming metal alloy with hydrogen gas. This paper reports on the development of a laboratory scale two-stage Metal Hydride Compressor (MHC) system with a feed pressure of 150 bar delivering high purity H2 gas at outlet pressures up to 875 bar. Stage 1 and stage 2 AB2 metal hydrides are identified based on experimental characterization of the pressure-composition-temperature (PCT) behavior of candidate materials. The selected metal hydrides are each combined with expanded natural graphite, increasing the thermal conductivity of the composites by an order of magnitude. These composites are integrated in two compressor beds with internal heat exchangers that alternate between hydrogenation and dehydrogenation cycles by thermally cycling between 20 °C and 150 °C. The prototype compressor achieved compression of hydrogen from 150 bar to 700 bar with an average flow rate of 33.6 g/hr.

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TYPE SAND Report YEAR 2022
DOIOSTI

Machine Learning Solutions for a Stable Grid Recovery

Verzi, Stephen J.; Guttromson, Ross; Sorensen, Asael H.

Grid operating security studies are typically employed to establish operating boundaries, ensuring secure and stable operation for a range of operation under NERC guidelines. However, if these boundaries are severely violated, existing system security margins will be largely unknown, as would be a secure incremental dispatch path to higher security margins while continuing to serve load. As an alternative to the use of complex optimizations over dynamic conditions, this work employs the use of machine learning to identify a sequence of secure state transitions which place the grid in a higher degree of operating security with greater static and dynamic stability margins. Several reinforcement learning solution methods were developed using deep learning neural networks, including Deep Q-learning, Mu-Zero, and the continuous algorithms Proximal Reinforcement Learning, and Advantage Actor Critic Learning. The work is demonstrated on a power grid with three control dimensions but can be scaled in size and dimensionality, which is the subject of ongoing research.

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TYPE SAND Report YEAR 2022
DOIOSTI

Identifying process-structure-property correlations related to the development of stress in metal thin films by high-throughput characterization and simulation-based methods

Kalaswad, Matias; Shrivastava, Ankit; Desai, Saaketh; Custer, Joyce O.; Khan, Ryan M.; Addamane, Sadhvikas J.; Monti, Joseph M.; Fowler, James E.; Rodriguez, Mark A.; Delrio, Frank W.; Kotula, Paul G.; D'Elia, Marta; Najm, Habib N.; Dingreville, Remi P.M.; Boyce, Brad L.; Adams, David P.

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TYPE Conference Poster YEAR 2022
DOIOSTI

Substrate-Independent Technique of III-V Heterogeneous Integration of Focal Plane Arrays and Lasers

Wood, Michael G.; Bahr, Matthew; Gutierrez, Jordan E.; Anderson, Evan; Finnegan, Patrick S.; Weatherred, Scott E.; Martinez, William M.; Bays, Nathan R.; Reyna, Robert; Arterburn, Shawn C.; Friedmann, Thomas A.; Hawkins, Samuel D.; Patel, Victor J.; Hendrickson, Alex T.; Klem, John F.; Long, Christopher M.; Olesberg, Jonathon; Shank, Josh; Chumney, Daniel R.; Looker, Quinn M.

Abstract not provided.

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TYPE Conference Proceeding YEAR 2022
DOIOSTI
Results 6276–6300 of 100,993
Results 6276–6300 of 100,993
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