The interaction of a probability screen with spherical particles was simulated in a computer model. Repeated runs were made using randomly selected, slightly differing initial conditions, from which the penetration probability was obtained at a particular particle size under given screen operating conditions. The calculations were then repeated with differing particle sizes and operating conditions, and the results were found to compare favourably with experimental data obtained in a previous study. It was shown that the stochastic model correctly predicts the effects of the principal operating variables (screen angle of inclination and aperture size, frequency and amplitude of vibration). Because of excessive computer time requirements of this model a second, semi-empirical non-stochastic model was developed whose results closely match those of the earlier model. The second model is suitable for incorporation into a plant simulation computer package. 相似文献
Scanning electron micrograph of a representative microstructure from a sintered (1‐x)BaTiO3 ‐ xBi(M)O3 ceramic, overlaid with a dielectric response typical of this family of materials. The apparent simplicity of the microstructure belies the nanoscale complexity that results in the frequency dispersion and temperature‐ and field‐stability of permittivity that is characteristic of these dielectrics. See review article by Michaela A. Beuerlein et al.
One of the most important challenges in increasing the performance, reliability and lifetime of fuel cells is the mechanical load effects that occur on real applications. Therefore, the vibration model of fuel cell that predicts the behavior of various fuel cell layouts is very useful. The fuel cell is made up of different adjacent layers that may have semi opposite mechanical properties. This special structure leads to occurrence of non‐linear behavior of fuel cell under dynamic mechanical vibrations and so, a black box method is selected for modeling of its vibration behavior. In this study, the mechanical load experiments in various shape and axes were applied on five layouts of proposed fuel cell and the vibration of its body measure by some accelerometers. The NNARXM neural network is created and trained with the experimental data of three layouts of the fuel cell. Then, the prediction error of this neural network, validated with the two other experimental data of fuel cell layouts, by correlation coefficients and histogram of prediction errors. Neural network validation shows the well prediction of both untrained layout and suitable estimation for any desired layout. 相似文献
