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.

Nonlinear Kalman filtering techniques for terrain-aided navigation

Abstract: The application of nonlinear Kalman filtering techniques to the continuous updating of an inertial navigation system using individual radar terrain-clearance measurements has been investigated. During this investigation, three different approaches for handling the highly nonlinear terrain measurement function were developed and their performance was established. These were 1) a simple first-order extended Kalman filter using local derivatives of the terrain surface, 2) a modified stochastic linearization technique which adaptively fits a least squares plane to the terrain surface and treats the associated fit error as an additional noise source, 3) a parallel Kalman filter technique utilizing a bank of reduced-order filters that was especially important in applications with large initial position uncertainties. Theoretical and simulation results are presented.

Rapid reduction of nitrogen oxides in exhaust gas streams

Abstract: Nitrogen oxides (NOx) play a major role in the formation of photochemical smog and in acid rain production1. Some progress has been made in reducing NOx emissions through the use of combustion and exhaust control schemes, including three-way catalyst, staged combustion, and ammonia injection. Nevertheless, estimates indicate that the production of nitrogen oxides will continue to increase until the end of the century and beyond, if current trends continue2. We describe here a new chemical process capable of completely removing NOx from the products of combustion. The effectiveness of this process is established in flow tube experiments. Further, we demonstrate the practical feasibility of this method by eliminating NOx from a portion of the exhaust from a single-cylinder diesel engine. Based on these results we conclude that this process for rapid reduction of nitrogen oxides could play a major role in controlling NOx emissions from most combustion devices.

Multivariate Least-Squares Methods Applied to the Quantitative Spectral Analysis of Multicomponent Samples

Abstract: In an extension of earlier work, weighted multivariate least-squares methods of quantitative FT-IR analysis have been developed. A linear least-squares approximation to nonlinearities in the Beer-Lambert law is made by allowing the reference spectra to be a set of known mixtures. The incorporation of nonzero intercepts in the relation between absorbance and concentration further improves the approximation of nonlinearities while simultaneously accounting for nonzero spectral baselines. Pathlength variations are also accommodated in the analysis, and under certain conditions, unknown sample pathlengths can be determined. All spectral data are used to improve the precision and accuracy of the estimated concentrations. During the calibration phase of the analysis, pure component spectra are estimated from the standard mixture spectra. These can be compared with the measured pure component spectra to determine which vibrations experience nonlinear behavior. In the predictive phase of the analysis, the calculated spectra are used in our previous least-squares analysis to estimate sample component concentrations. These methods were applied to the analysis of the IR spectra of binary mixtures of esters. Even with severely overlapping spectral bands and nonlinearities in the Beer-Lambert law, the average relative error in the estimated concentrations was <1%.