In this study, the effect of design parameters such as pipe diameter, pipe wall thickness, pipe material and the effect of fluid velocity on the natural frequency of fluid-structure interaction in straight pipe conveying fully developed turbulent flow were investigate numerically,analytically and experimentally. Also the effect of support conditions, simply-simply and clamped-clamped was investigated. Experimentally, pipe vibrations were characterized by accelerometer mounted on the pipe wall. The natural frequencies of vibration were analyzed by using Fast Fourier Transformer (FFT). Five test sections of two different pipe diameters of 76.2
mm and 50.8 mm with two pipe thicknesses of 3.7 mm and 2.4 mm and two pipe materials,stainless steel and polyvinyl chloride PVC in the range of Reynolds numbers from 4*104 to 5*105 were studied. Mathematically, the governing continuity and momentum equations were solved numerically by using the finite volume method to compute the characteristics of two dimensional turbulent flow. The dynamics of a pipe conveying fluid was described by the Transfer Matrix Method (TMM) which is provides a numerical technique for solving the equations of pipe vibrations for simply-simply and clamped supports. The results showed that,the natural frequencies increase with pipe diameter increase and the natural frequencies slightly increases with pipe wall thickness increase. Also, the natural frequencies in clamped-clamped supported pipe are higher than those in simply-simply supported pipe.
م. د. ولاء طارق حميد, Al. Qadisiya journal for the Sciences of Physical Education, 2017
In the lifetime process in some systems, most data cannot belong to one single population. In fact, it can represent several subpopulations. In such a case, the known distribution cannot be used to model data. Instead, a mixture of distribution is used to modulate the data and classify them into several subgroups. The mixture of Rayleigh distribution is best to be used with the lifetime process. This paper aims to infer model parameters by the expectation-maximization (EM) algorithm through the maximum likelihood function. The technique is applied to simulated data by following several scenarios. The accuracy of estimation has been examined by the average mean square error (AMSE) and the average classification success rate (ACSR). T
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