Publications

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The Xyce XDM Netlist Translator is a software solution developed by Sandia National Laboratories for translating SPICE netlists into a format readable by Xyce, Sandia’s internally developed SPICE-compatible simulation tool.

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Reversible logic schemes using flux solitons (fluxons) on long Josephson junctions (LJJs) have recently been proposed. The attraction of the fluxon is that it propagates ballistically along an LJJ until it encounters a change in the character of the LJJ, often a designed circuit element. Logic gates involve fluxons interacting with circuit elements and with other fluxons. However, testing of ballistic fluxon circuits requires other circuits outside the logic family to direct and control fluxon motion. We discuss two such non-reversible fluxon control circuits. First, the polarity filter gate is a simple non-reversible gate that allows one polarity of fluxon to pass, while reflecting the other polarity. In the off state both polarities reflect. Second, the polarity separator generalizes on the polarity filter concept and allows separation of the two fluxon polarities into different LJJs. We discuss simulations of these structures and possible applications.

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Nuclear power plants (NPPs) are considering flexible plant operations to take advantage of excess thermal and electrical energy. One option for NPPs is to pursue hydrogen production through high temperature electrolysis as an alternate revenue stream to remain economically viable. The intent of this study is to investigate the risk of a hydrogen production facility in close proximity to an NPP. A 100 MW, 500 MW, and 1,000 MW facility are evaluated herein. Previous analyses have evaluated preliminary designs of a hydrogen production facility in a conservative manner to determine if it is feasible to co-locate the facility within 1 km of an NPP. This analysis specifically evaluates the risk components of different hydrogen production facility designs, including the likelihood of a leak within the system and the associated consequence to critical NPP targets. This analysis shows that although the likelihood of a leak in an HTEF is not negligible, the consequence to critical NPP targets is not expected to lead to a failure given adequate distance from the plant.

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This paper describes a summer internship project undertaken at Sandia National Labs (SNL), both current status and future work. The project was to explore various machine learning approaches for use on turbulent flow data. Specifically, unsupervised classification of turbulent flow data was explored. First, the usage of models in this field is discussed, and several issues in the common usage of the models are identified. Solutions to these issues are then proposed, in the form of a Bayesian filtering approach which probabilistically incorporates multiple sources of data to improve confidence in a result. Several types of sensors are suggested for this method, the incorporation of which range from semi-supervised learning approaches to fully unsupervised. These approaches are then tested on several turbulent flow cases.

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The growth of helium bubbles impacts structural integrity of materials in nuclear applications. Understanding helium bubble nucleation and growth mechanisms is critical for improved material applications and aging predictions. Systematic molecular dynamics simulations have been performed to study helium bubble nucleation and growth mechanisms in Fe70Ni11Cr19 stainless steels. First, helium cluster diffusivities are calculated at a variety of helium cluster sizes and temperatures for systems with and without dislocations. Second, the process of diffusion of helium atoms to join existing helium bubbles is not deterministic and is hence studied using ensemble simulations for systems with and without vacancies, interstitials, and dislocations. We find that bubble nucleation depends on diffusion of not only single helium atoms, but also small helium clusters. Defects such as vacancies and dislocations can significantly impact the diffusion kinetics due to the trapping effects. Vacancies always increase the time for helium atoms to join existing bubbles due to the short-range trapping effect. This promotes bubble nucleation as opposed to bubble growth. Interestingly, dislocations can create a long-range trapping effect that reduces the time for helium atoms to join existing bubbles. This can promote bubble growth within a certain region near dislocations.

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Photovoltaic systems may underperform expectations for several reasons, including inaccurate initial estimates, suboptimal operations and maintenance, or component degradation. Accurate assessment of these loss factors aids in addressing root causes of underperformance and in realizing accurate expectations and models. The performance loss rate (PLR) is a commonly cited high-level metric for the change in system output over time, but there is no precise, standard definition. Herein, an annualized definition of PLR that is inclusive of all loss factors and that can capture nonlinear changes to performance over time is proposed. The importance of distinguishing between recoverable and nonrecoverable losses which underly PLR is highlighted.