Sandia National Laboratories

About: Sandia National Laboratories is a facility organization based out in Livermore, California, United States. It is known for research contribution in the topics: Laser & Combustion. The organization has 21501 authors who have published 46724 publications receiving 1484388 citations. The organization is also known as: SNL & Sandia National Labs.

Particle Velocity and Deposition Efficiency in the Cold Spray Process

Abstract: Copper powder was sprayed by the cold gas-dynamic method. In-flight particle velocities were measured with a laser two-focus system as a function of process parameters such as gas temperature, gas pressure, and powder feed rate. Mean particle velocities were uniform in a relatively large volume within the plume and agreed with theoretical predictions. The presence of a substrate was found to have no significant effect on in-flight particle velocities prior to impact. Cold-spray deposition efficiencies were measured on aluminum substrates as a function of particle velocity and incident angle of the plume. Deposition efficiencies of up to 95% were achieved. The critical velocity for deposition was determined to be about 640 m/s for the system studied.

Temporal Link Prediction using Matrix and Tensor Factorizations

Abstract: The data in many disciplines such as social networks, web analysis, etc. is link-based, and the link structure can be exploited for many different data mining tasks. In this paper, we consider the problem of temporal link prediction: Given link data for times 1 through T, can we predict the links at time T+1? If our data has underlying periodic structure, can we predict out even further in time, i.e., links at time T+2, T+3, etc.? In this paper, we consider bipartite graphs that evolve over time and consider matrix- and tensor-based methods for predicting future links. We present a weight-based method for collapsing multi-year data into a single matrix. We show how the well-known Katz method for link prediction can be extended to bipartite graphs and, moreover, approximated in a scalable way using a truncated singular value decomposition. Using a CANDECOMP/PARAFAC tensor decomposition of the data, we illustrate the usefulness of exploiting the natural three-dimensional structure of temporal link data. Through several numerical experiments, we demonstrate that both matrix- and tensor-based techniques are effective for temporal link prediction despite the inherent difficulty of the problem. Additionally, we show that tensor-based techniques are particularly effective for temporal data with varying periodic patterns.

Ignition and devolatilization of pulverized bituminous coal particles during oxygen/carbon dioxide coal combustion

Abstract: Oxygen/carbon dioxide recycle coal combustion is actively being investigated because of its potential to facilitate CO 2 sequestration and to achieve emission reductions. In the work reported here, the effect of enhanced oxygen levels and CO 2 bath gas is independently analyzed for their influence on single-particle pulverized coal ignition of a U.S. eastern bituminous coal. The experiments show that the presence of CO 2 and a lower O 2 concentration increase the ignition delay time but have no measurable effect on the time required to complete volatile combustion, once initiated. For the ignition process observed in the experiments, the CO 2 results are explained by its higher molar specific heat and the O 2 results are explained by the effect of O 2 concentration on the local mixture reactivity. Particle ignition and devolatilization properties in a mixture of 30% O 2 in CO 2 are very similar to those in air.