Discharge Characteristics of Polydisperse Powders through Conical Hoppers. Part 2: Predictions for Coarse, Granular, Free Flowing Powders

Discharge characteristics of coarse polydisperse granular powders through a conical hopper are experimentally investigated. The average discharge rates of four different powder types are systematically measured for seven different diameters of hopper orifice under gravity flow conditions. Each powder type is a mixture of some sub-sizes (smaller than 3 mm) of same powder material. Effects of orifice diameter of hopper and size distribution of polydisperse powders on discharge characteristics are experimentally determined. The measured discharge data are compared with discharge values predicted by using modified forms of the well-known Beverloo correlation. The volume-moment-mean diameter, d_VM, and the 50% weight fraction diameter, d₅₀, cited in available literature are both checked to characterize the coarse polydisperse powders and used throughout the predictions. Comparisons implied that both d_VM and d₅₀ diameters can be successfully used to characterize polydisperse test powders, and discharge rate predictions are in good agreement with experimental data, with mean deviations lower than ±4.04%.     

On three intelligent systems: dynamic neural,fuzzy, and wavelet networks for training trajectory

Intelligent systems cover a wide range of technologies related to hard sciences, such as modeling and control theory, and soft sciences, such as the artificial intelligence (AI). Intelligent systems, including neural networks (NNs), fuzzy logic (FL), and wavelet techniques, utilize the concepts of biological systems and human cognitive capabilities. These three systems have been recognized as a robust and attractive alternative to the some of the classical modeling and control methods. The application of classical NNs, FL, and wavelet technology to dynamic system modeling and control has been constrained by the non-dynamic nature of their popular architectures. The major drawbacks of these architectures are the curse of dimensionality, such as the requirement of too many parameters in NNs, the use of large rule bases in FL, the large number of wavelets, and the long training times, etc. These problems can be overcome with dynamic network structures, referred to as dynamic neural networks (DNNs), dynamic fuzzy networks (DFNs), and dynamic wavelet networks (DWNs), which have unconstrained connectivity and dynamic neural, fuzzy, and wavelet processing units, called neurons, feurons, and wavelons, respectively. The structure of dynamic networks are based on Hopfield networks. Here, we present a comparative study of DNNs, DFNs, and DWNs for non-linear dynamical system modeling. All three dynamic networks have a lag dynamic, an activation function, and interconnection weights. The network weights are adjusted using fast training (optimization) algorithms (quasi-Newton methods). Also, it has been shown that all dynamic networks can be effectively used in non-linear system modeling, and that DWNs result in the best capacity. But all networks have non-linearity properties in non-linear systems. In this study, all dynamic networks are considered as a non-linear optimization with dynamic equality constraints for non-linear system modeling. They encapsulate and generalize the target trajectories. The adjoint theory, whose computational complexity is significantly less than the direct method, has been used in the training of the networks. The updating of weights (identification of network parameters) is based on Broyden–Fletcher–Goldfarb–Shanno method. First, phase portrait examples are given. From this, it has been shown that they have oscillatory and chaotic properties. A dynamical system with discrete events is modeled using the above network structure. There is a localization property at discrete event instants for time and frequency in this example.     

Solubility and demixing of polyethylene in supercritical binary fluid mixtures: Carbon dioxide–cyclohexane,carbon dioxide–toluene,carbon dioxide–pentane

Solubility of polyethylene in three different supercritical binary solvent systems—carbon dioxide–cyclohexane, carbon dioxide–toluene, and carbon dioxide–pentane—have been studied. Solvent compositions that lead to complete dissolution at pressures below 70 MPa have been identified. Demixing pressures have been determined for a range of polymer concentrations at temperatures up to 200°C. It is shown that the behavior of the solutions depends strongly on the fluid composition. In the composition ranges studied, solutions in cyclohexane–carbon dioxide mixtures were found to show lower critical solution temperature (LCST) behavior. It is found that the behavior of solutions in carbon dioxide–toluene and carbon dioxide–pentane mixtures shifts from upper critical solution temperature (UCST) to LCST with increasing toluene or pentane content. © 1993 John Wiley & Sons, Inc.     

Effect of Si on austenite stabilization,martensite morphology,and magnetic properties in Fe-26%Ni-x%Si alloys

The effect of Si on the austenite stabilization, martensite morphology, and magnetic properties in Fe-26%Ni-x%Si (x=3.5, 5, and 6) alloys have been studied by means of transmission electron microscopy (TEM) and M?ssbauer spectroscopy techniques. TEM observations reveal that the martensite morphology is closely dependent on the Si content. The volume fraction changes of martensite and austenite phases, the hyperfine magnetic field, and isomer shift values have been determined by Mssbauer spectroscopy. The M...     

Evaluation of adhesion and fatigue of MoS2-Nb solid-lubricant films deposited by pulsed-dc magnetron sputtering

The thin film deposition for tribological applications becomes more and more widespread. The tribological performance of the overlay coatings correlates with coating-substrate adhesion. Hence, it is important to measure adhesive strength. The scratch adhesion test for thin films is extensively used. In this work, MoS₂-Nb coatings deposited by closed-field unbalanced magnetron sputtering (CFUBMS) have been scratch tested in two modes. A multi-mode operation was used as sliding-fatigue, like multi-pass scratching in the same track at different fractions of critical load (unidirectional sliding) and a standard mode using progressive load operation. Failure mechanisms are discussed according to examination of response of very dense microstructure and the adhesion value. The critical load to the first failure (L_C1) was 15 N but the final adhesion value from the film and substrate interface was 120 N (L_C2) as function of the coating thinning. The coefficient of friction (COF) from the multi-scratch for MoS₂-Nb started at a very high value of around 0.067, 0.073, and 0.093 under 5, 8, and 15 N loads respectively and then drops to 0.006, 0.035, and 0.065 at the end of the 1000 cycles. The most significant finding in the test is that when the multi-scratch passes reached to 1000 cycles, micro scale fatigue failures disappeared.     

Characterization of binding interactions between green tea flavanoids and milk proteins

Flavanoids are known to interact with proteins to form complexes whose properties depend on the structure of both the flavanoids and the protein. In this study, the fluorescent probe binding method (fluorimetry analysis) and isothermal titration calorimetry (ITC) analysis were used to characterize binding interactions of green tea (GT) flavanoids and milk proteins. The hydrophobicity of the surface sites of milk proteins was estimated using the reconstituted milk–green tea and casein–green tea systems. Reconstituted milk–green tea and casein–green tea samples were prepared with different solid-non-fat (SNF) and casein (Cn) concentrations, respectively. It was observed that the number of surface hydrophobic sites decreased in the presence of GT flavanoids for all SNF and Cn concentrations. The decrease in protein surface hydrophobicity was explained by the hydrophobic binding between milk proteins and GT flavanoids. The binding enthalpies obtained from ITC analysis implied that interaction was non-covalent between catechin and β-casein.