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Institution

Naval Surface Warfare Center

FacilityWashington D.C., District of Columbia, United States
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: Sonar & Radar. The organization has 2855 authors who have published 3697 publications receiving 83518 citations. The organization is also known as: NSWC.


Papers
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Journal ArticleDOI
TL;DR: It was found that plume velocity exhibits a modified exponential temporal profile, where smaller particles are more susceptible to air drag than larger ones, especially for particles in the diffusion-controlled regime.
Abstract: The development of a laser-shock technique for dispersing Al metal fuel particles at velocities approaching those expected in a detonating explosive is discussed. The technique is described in detail by quantifying how air drag affects the temporal variation of the velocity of the dispersed particle plume. The effect of particle size is incorporated by examining various poly-dispersed commercial Al powders at different dispersion velocities (390-630 m/s). The technique is finally tested within a preliminary study of particle ignition delay and burn time, where the effect of velocity is highlighted for different particle sizes. It was found that plume velocity exhibits a modified exponential temporal profile, where smaller particles are more susceptible to air drag than larger ones. Moreover, larger particles exhibit longer ignition delays and burn times than smaller ones. The velocity of a particle was found to significantly affect its ignition delay, burn time, and combustion temperature, especially for particles in the diffusion-controlled regime. Shorter ignition delays and burn times and lower temperatures were observed at higher particle velocities. The utility of this technique as a combustion screening test for future, novel fuels is discussed.

19 citations

Proceedings ArticleDOI
15 Sep 2003
TL;DR: A significant improvement was made to the CAD/CAC processing string by utilizing a repeated application of the subset feature selection / LLRT classification blocks, it was shown that LLRT-based fusion algorithms outperform the logic based and the “M-out-of-N” ones.
Abstract: A novel sea mine computer-aided-detection / computer-aided-classification (CAD/CAC) processing string has been developed. The overall CAD/CAC processing string consists of pre-processing, adaptive clutter filtering (ACF), normalization, detection, feature extraction, feature orthogonalization, optimal subset feature selection, classification and fusion processing blocks. The range-dimension ACF is matched both to average highlight and shadow information, while also adaptively suppressing background clutter. For each detected object, features are extracted and processed through an orthogonalization transformation, enabling an efficient application of the optimal log-likelihood-ratio-test (LLRT) classification rule, in the orthogonal feature space domain. The classified objects of 4 distinct processing strings are fused using the classification confidence values as features and logic-based, “M-out-of-N”, or LLRT-based fusion rules. The utility of the overall processing strings and their fusion was demonstrated with new shallow water high-resolution sonar imagery data. The processing string detection and classification parameters were tuned and the string classification performance was optimized, by appropriately selecting a subset of the original feature set. A significant improvement was made to the CAD/CAC processing string by utilizing a repeated application of the subset feature selection / LLRT classification blocks. It was shown that LLRT-based fusion algorithms outperform the logic based and the “M-out-of-N” ones. The LLRT-based fusion of the CAD/CAC processing strings resulted in up to a nine-fold false alarm rate reduction, compared to the best single CAD/CAC processing string results, while maintaining a constant correct mine classification probability.

19 citations

Journal ArticleDOI
TL;DR: In this article, three 26650 LiFePO4 (LFP) cells were cycled using a 40 A pulsed charge/discharge profile to study their performance in high rate pulsed applications.

19 citations

Journal ArticleDOI
TL;DR: In this article, the authors show that the range of high dose measurements can be increased by an order of magnitude by increasing the concentration of dysprosium in CaSO 4 :Dy.

19 citations

Proceedings ArticleDOI
20 Aug 2007
TL;DR: In this article, a robust numerical approach based on previous research is developed to derive the state dependent parameterization of the system, which is solved numerically at the instantaneous values of the state vector.
Abstract: ‡Numerical s tate -dependent Riccati equation based integrated guidance -control formulation is developed for an internally actuated missile. The dynamic system under consideration is of tenth order , making it tedious to algebraically manipulate the equations of motion into the state -dependent coeffic ient form central to the design methodology . A robust numerical approach based on previous research is developed to derive the state dependent parameterization of the system. The approach works directly with an input -state numerical simulation model of the system . The state -dependent Riccati equation is solved numerically at the instantaneous values of the state vector . The approach provides a fully numerical methodology for deriving state -dependent Riccati equation control lers for arbitrarily complex dynam ic systems. The proposed approach is applied for th e design of integrated guidance -controller of an internally actuated missile. Closed -loop simulation results for three -dimensional target intercept ions are presented .

19 citations


Authors

Showing all 2860 results

NameH-indexPapersCitations
James A. Yorke10144544101
Edward Ott10166944649
Sokrates T. Pantelides9480637427
J. M. D. Coey8174836364
Celso Grebogi7648822450
David N. Seidman7459523715
Mingzhou Ding6925617098
C. L. Cocke513128185
Hairong Qi503279909
Kevin J. Hemker4923110236
William L. Ditto431937991
Carey E. Priebe434048499
Clifford George412355110
Judith L. Flippen-Anderson402056110
Mortimer J. Kamlet3910812071
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
20233
20227
202172
202071
201982
201884