Flow frictional resistance in pneumatic conveying of solid particles through inclined lines

The frictional behaviour of fully suspended dilute flow of granular solid particles in air through the transport lines of various angularities, alpha, with the horizontal plane in the range of 0degreesless than or equal to alpha 30degrees is presented in this paper. The friction factor determination is based on the measurements of local static pressure gradients. The experiments were mainly directed to determine the effects of transport line inclination, particle physical characteristics, the ratio of mass flow rate of particles to that of air, and the flow Reynolds number, Re, on the two-phase friction factor. A variety of solid particles whose average diameter, d(p), and apparent density, p(p), being in die respective ranges of 75.5 mumless than or equal tod(p)less than or equal to275 mum and 467.95 kg/m(3) less than or equal to p(p)less than or equal to824.17 kg/m(3) were used, with 5%less than or equal toM(p)/M(a)less than or equal to30%, at 50 000less than or equal toReless than or equal to100 000, for this purpose. The results were expressed in terms of two-phase friction factor, f(p+a), correlations proposed under the light of the state of art. The experimental data was also evaluated via solids friction factor, f(s), correlated previously by Ozbelge [Int. J. Multiphase Flow 10 (1984) 459] based on a theoretical analysis of vertical upward flow fields [Int. J. Multiphase Flow 9 (1983) 437] as well as to confirm the validity of the method in non-vertical upward flows

Genetically programmable optical random neural networks

Today, machine learning tools, particularly artificial neural networks, have become crucial for diverse applications. However, current digital computing tools to train and deploy artificial neural networks often struggle with massive data sizes and high power consumptions. Optical computing provides inherent parallelism and perform fundamental operations with passive optical components. However, most of the optical computing platforms suffer from relatively low accuracies for machine learning tasks due to fixed connections while avoiding complex and sensitive techniques. Here, we demonstrate a genetically programmable yet simple optical neural network to achieve high performances with optical random projection. By genetically programming the orientation of the scattering medium which acts as a random projection kernel and only using 1% of the search space, our novel technique finds an optimum kernel and improves its initial test accuracies 7-22% for various machine learning tasks. Our optical computing method presents a promising approach to achieve high performance in optical neural networks with a simple and scalable design