Naval Surface Warfare Center

About: Naval Surface Warfare Center is a facility organization based out in Washington D.C., District of Columbia, United States. It is known for research contribution in the topics: Radar & Sonar. The organization has 2855 authors who have published 3697 publications receiving 83518 citations. The organization is also known as: NSWC.

$X$ -Band Beacon-Receiver Array Evaporation Duct Height Estimation

Abstract: Recent experimental campaigns provided the opportunity to measure radio wave propagation and atmospheric conditions with the $X$ -band beacon-receiver (XBBR) array system. The system consists of vertical arrays of transmitters and receivers for measuring the $X$ -band propagation. Measurements near the sea surface can be used to obtain information regarding the refractivity profile of the lower atmosphere. Since ducted propagation acts as a leaky waveguide, the vertical array elements in various transmit and receive height combinations effectively observe differing combinations of the modal components propagating in the duct, the use of multiple combinations improves the estimation of duct properties. The aforementioned measurement campaigns occurred near the coast of southern California; the SoCal 2013 experiment and the Scripps Pier Campaign. During both campaigns, the propagation loss recorded at each of the receivers from each of the transmitters, standardized by the total received power, was compared to variable terrain radio parabolic equation model predictions in order to estimate the evaporation duct height (EDH). Point meteorological data were recorded and used with the Navy Atmospheric Vertical Surface Layer Model to obtain in situ measurements of the EDH. Comparisons show strong correlation between EDH values inferred from XBBR measurements and meteorological information.

Evaluation of a hybrid energy storage module for pulsed power applications

Abstract: Before pulsed power systems can be fielded in either mobile or small footprint stationary applications, the prime power source must be optimized for both size and operational efficiency. In large footprint laboratories, prime power supplies are connected to a local utility grid to charge intermediate storage systems. In mobile platforms, alternative energy sources, such as electrochemical batteries or supercapacitors, must be used to backup smaller fossil fuel generators. The prime power source used in a pulsed power system must store high energy, to maximize the number of shots stored, and be able to source high power to recharge the intermediate store as fast as possible. Finding a single electrochemical energy storage device that has the right energy and power density for most applications is nearly impossible. Therefore, usage of batteries, which possess high energy density, along with electrochemical capacitors, which offer high power density, in a hybrid energy storage module (HESM) configuration is a promising way of combining both of these features into a single supply. Usage of this topology reduces the stress on the batteries, thereby prolonging their life, and also increases the instantaneous power capabilities of the system. This paper presents the design and validation of an actively controlled HESM built using commercial off the shelf power electronics and simple control strategies.

Effect of particle size on the shock sensitivity of porous HE

Abstract: Literature data show that in most gap tests, coarse porous high explosives (HE) seem more shock sensitive than fine whereas in most wedge tests, the reverse is true. It is proposed that gap tests measure threshold for ignition, and that the reversal occurs because the time to ignition is shorter and the time of buildup of chemical reaction is longer for the coarse than the fine material. In other words, for any pair of fine and coarse HE in any specific experiment, there is a pressure (Pγ) at and above which ignition for the fine and coarse is simultaneous. At and above this pressure, the finer material appears more sensitive than the coarse.

Spectral embedding finds meaningful (relevant) structure in image and microarray data

Abstract: Accurate methods for extraction of meaningful patterns in high dimensional data have become increasingly important with the recent generation of data types containing measurements across thousands of variables. Principal components analysis (PCA) is a linear dimensionality reduction (DR) method that is unsupervised in that it relies only on the data; projections are calculated in Euclidean or a similar linear space and do not use tuning parameters for optimizing the fit to the data. However, relationships within sets of nonlinear data types, such as biological networks or images, are frequently mis-rendered into a low dimensional space by linear methods. Nonlinear methods, in contrast, attempt to model important aspects of the underlying data structure, often requiring parameter(s) fitting to the data type of interest. In many cases, the optimal parameter values vary when different classification algorithms are applied on the same rendered subspace, making the results of such methods highly dependent upon the type of classifier implemented. We present the results of applying the spectral method of Lafon, a nonlinear DR method based on the weighted graph Laplacian, that minimizes the requirements for such parameter optimization for two biological data types. We demonstrate that it is successful in determining implicit ordering of brain slice image data and in classifying separate species in microarray data, as compared to two conventional linear methods and three nonlinear methods (one of which is an alternative spectral method). This spectral implementation is shown to provide more meaningful information, by preserving important relationships, than the methods of DR presented for comparison. Tuning parameter fitting is simple and is a general, rather than data type or experiment specific approach, for the two datasets analyzed here. Tuning parameter optimization is minimized in the DR step to each subsequent classification method, enabling the possibility of valid cross-experiment comparisons. Results from the spectral method presented here exhibit the desirable properties of preserving meaningful nonlinear relationships in lower dimensional space and requiring minimal parameter fitting, providing a useful algorithm for purposes of visualization and classification across diverse datasets, a common challenge in systems biology.