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 & Thin film. The organization has 21501 authors who have published 46724 publications receiving 1484388 citations. The organization is also known as: SNL & Sandia National Labs.

Wind tunnel performance data for two- and three-bucket Savonius rotors

Abstract: Fifteen configurations of a Savonius rotor wind turbine were tested in the Vought Corporation Systems Division 4.9- x 6.1-m Low Speed Wind Tunnel to determine aerodynamic performance. The range of values of the varied parameters was as follows: number of buckets, 2 and 3; nominal freestream velocity, 7 and 14 m/s; Reynolds number per meter, 4.32 x 10/sup 5/ and 8.67 x 10/sup 5/; rotor height, 1 and 1.5 m; rotor diameter (nominal), 1 m; bucket overlap, 0.0 to 0.1 m. The measured test variables were torque, rotational speed, and tunnel conditions. It is concluded that increasing Reynolds number and/or aspect ratio improves performance. The recommended configuration consists of two sets of two-bucket rotors, rotated 90 deg apart, with each rotor having a dimensionless gap width of 0.1 to 0.15.

Master equation methods in gas phase chemical kinetics.

Abstract: In this article, we discuss the application of master equation methods to problems in gas phase chemical kinetics. The focus is on reactions that take place over multiple, interconnected potential wells and on the dissociation of weakly bound free radicals. These problems are of paramount importance in combustion chemistry. To illustrate specific points, we draw on our experience with reactions we have studied previously.

Exploring Topic Coherence over Many Models and Many Topics

Abstract: We apply two new automated semantic evaluations to three distinct latent topic models. Both metrics have been shown to align with human evaluations and provide a balance between internal measures of information gain and comparisons to human ratings of coherent topics. We improve upon the measures by introducing new aggregate measures that allows for comparing complete topic models. We further compare the automated measures to other metrics for topic models, comparison to manually crafted semantic tests and document classification. Our experiments reveal that LDA and LSA each have different strengths; LDA best learns descriptive topics while LSA is best at creating a compact semantic representation of documents and words in a corpus.