• Transactions of the Korean Society of Mechanical Engineers
• /
• v.18 no.7
• /
• pp.1840-1850
• /
• 1994

Enhancement of numerical algorithms for low speed compressible flow will be considered. Contemporary time-marching algorithm has been widely accepted and applied as the method of choice for transonic, supersonic and hypersonic flows. In the low Mach number regime, time-marching algorithms do not fare as well. When the velocity is small, eigenvalues of the system of compressible equations differ widely so that the system becomes very stiff and the convergence becomes very slow. This characteristic can lead to difficulties in computations of many practical engineering problems. In the present approach, the time-derivative preconditioning method will be used to control the eigenvalue stiffness and to extend computational capabilities over a wide range of flow conditions (from very low Mach number to supersonic flow). Computational capabilities of the above algorithm will be demonstrated through computation of a variety of practical engineering problems.

• The Journal of the Acoustical Society of Korea
• /
• v.17 no.1E
• /
• pp.54-65
• /
• 1998

This study attempts to test the classifying performance of a neural network and thereby examine its applicability to the signals distorted in a shallow water environment. Linear frequency modulated(LFM) signals are simulated by using an acoustic model and also measured through sea experiment. The network is constructed to have three layers and trained on both data sets. To get normalized power spectra as feature vectors, the study considers the three transforms : shot-time Fourier transform (STFT), wavelet transform (WT) and pseudo Wigner-Ville distribution (PWVD). After trained on the simulated signals over water depth, the network gives over 95% performance with the signal to noise ratio (SNR) being up to-10 dB. Among the transforms, the PWVD presents the best performance particularly in a highly noisy condition. The network performs worse with the summer sound speed profile than with the winter profile. It is also expected to present much different performance by the variation of bottom property. When the network is trained on the measured signals, it gives a little better results than that trained on the simulated data. In conclusion, the simulated signals are successfully applied to training a network, and the trained network performs well in classifying the signals distorted by a surrounding environment and corrupted by noise.