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: Sonar & Radar. The organization has 2855 authors who have published 3697 publications receiving 83518 citations. The organization is also known as: NSWC.

Joint transform correlator that uses wavelet transforms

Abstract: A joint transform correlation system based on wavelet transforms is introduced. The selection of wavelets and the optical wavelet transform of images enables this optical correlator to identify the specific features and distinguish similar characters. Preliminary experimental results are given.

All-Speed Time-Accurate Underwater Projectile Calculations Using a Preconditioning Algorithm

Abstract: An algorithm based on the combination of time-derivative preconditioning strategies with low-diffusion upwinding methods is developed and applied to multiphase, compressible flows characteristic of underwater projectile motion Multiphase compressible flows are assumed to be in kinematic and thermodynamic equilibrium and are modeled using a homogeneous mixture formulation Compressibility effects in liquid-phase water are modeled using a temperature-adjusted Tait equation, and gaseous phases (water vapor and air) are treated as an ideal gas The algorithm is applied to subsonic and supersonic projectiles in water general multiphase shock tubes, and a high-speed water entry problem Low-speed solutions are presented and compared to experimental results for validation Solutions for high-subsonic and transonic projectile flows are compared to experimental imaging results and theoretical results Results are also presented for several multiphase shock tube calculations Finally, calculations are presented for a high-speed axisymmetric supercavitating projectile during the important water entry phase of flight

Experimental maintenance of chaos

Abstract: We present a method for the anticontrol or maintenance of chaos designed for easy application to physical and biological systems. The method is based on the return map of the experimental data and requires only small, very infrequently applied time-dependent perturbations of a single system parameter and does not require any model equations for or a priori knowledge of the system dynamics. The method is shown to be able to reliably sustain chaos in a magnetomechanical ribbon experiment.

Helicopter Rotor System Fault Detection Using Physics-Based Model and Neural Networks

Abstract: A comprehensive physics-based model of the helicopter rotor in forward flight is used to analyze the impact of selected faults on rotor system behavior. The rotor model is based on finite elements in space and time. The helicopter rotor model is used to develop a neural network-based damage detection methodology. Simulated data from the rotor system are contaminated with noise and used to train a feedforward neural network using backpropogation learning. Cases considered for training and testing the neural network include both single and multiple faults on the damaged blade. Results show that the neural network can detect and quantify both single and multiple faults on the blade from noise-contaminated simulated vibration and blade response test data. For accurate estimation of type and extent of damages, it is important to train the neural networks with noise-contaminated response data.

Prestressed Composite Girders. I: Experimental Study for Negative Moment

Abstract: Limited experimental results were reported in the literature on the behavior of prestressed composite girders subjected to negative bending moment. This paper examines experimentally the behavior o...