Mg-doped LiNi0.5Mn1.5O4 spinel for cathode materials

Lithium-ion batteries are becoming more and more important not only for portable electronic devices, but also in prevision of high power electric vehicles. In such an optic, deep studies regarding all the components of a secondary battery are in development. In this study, high voltage cathode materials have been selected. Crystals with spinel structure have a 3D vacancy pathway suitable for Li-ions transport. The material under study was LiNi_0.5Mn_1.5O₄ doped with magnesium replacing the nickel. Various samples were synthesized via three different routes: a solid-state method, a modified sol–gel method and a xerogel method. The structure and morphology of the powders were analyzed with HRTEM and XRD. Electrochemical tests were also performed. A wide range of particle sizes (from micro to nanosize) was the result of the different synthesis routes. Unfortunately pure materials were not always obtained. The electrochemical tests showed improvement of the material's cyclability, by reducing the particle size. The electrochemical tests further confirmed the existence of a Li_1+dMn_2−dO₄ impurity. The results are quite promising, however, further improvement of the purity of the electrode composition are needed.     

A hybrid evolutionary approach to the registration area planning problem

The last two decades have witnessed a phenomenal growth in the number of cellular wireless network users which in turn stressed the need to utilize the limited network bandwidth in an efficient manner. The network bandwidth is consumed not only by user traffic, but also by control traffic needed for ensuring the mobility of users. As we don’t have any control over the volume of user traffic, all attempts to efficiently use bandwidth are based on frequency reuse and minimizing the control traffic. The registration area planning (RAP) problem seeks a partition of the cells of the network into contiguous areas called registration areas so that the bandwidth consumed by control signals is minimized. RAP problem in an \(\mathcal {NP}\) -Hard problem. In this paper, we present a steady-state grouping genetic algorithm with local search to solve this problem. We have compared our approach with the state-of-the-art approaches reported in the literature. Computational results show the effectiveness of our approach.     

Bubble size effect on low liquid input drift-flux parameters

We investigated the effect of bubble size on the drift-flux parameters at low liquid flow conditions by measuring the radial profiles of void fraction and phase velocities in a vertical bubbly pipe flow of diameter and height . To study the effect of the bubble size we used two different types of bubble inlets. We measured the local bubble fraction and velocity U_g by using single and four-point-optical fibre probes, and we used Laser Doppler Anemometry to determine the liquid velocity U_l. The distribution parameter C₀ and the weighted mean drift velocity |U_drift| were directly computed from the local measurements at a height on our experimental set-up. Both parameters were influenced by the bubble size. Provided no liquid flow reversal occurred at the near wall region, the distribution parameter reached a below unity minimum plateau value of C₀=0.95 for wall peaking void fraction profiles. At low liquid input conditions both the liquid input and bubble size had an influence on the distribution parameter. Extreme values such as C₀>2 were measured. From these measurements we developed models for the drift-flux parameters to take into account the effect of bubble size and input-flow conditions for our intermediate pipe diameter value. These models were tested and validated with separately collected experimental data.     

Analytical study of slightly eccentric core–annular flow

An analytical study was conducted on the flow of a rigid core surrounded by an annular liquid layer through a horizontal tube. Special emphasis was placed on the question how the buoyancy force on the core, caused by a possible density difference between the core and the annular layer, is counterbalanced. The analysis was carried out under the following restrictions: Stokes flow (no inertia), gap between the core and tube wall small compared with the tube radius, and eccentricity small compared with the gap between the core and the tube wall. A perturbation calculation was carried out with the amplitude of the wave at the core–annular interface as small parameter. The restoring (levitation) force is due to the viscous force exerted by the azimuthal velocity components on the core.     

Atomic Friction Investigations on Ordered Superstructures

We review recent friction measurements on ordered superstructures performed by atomic force microscopy. In particular, we consider ultrathin KBr films on NaCl(001) and Cu(001) surfaces, single and bilayer graphene on SiC(0001), and the herringbone reconstruction of Au(111). Atomically resolved friction images of these systems show periodic features spanning across several unit cells. Although the physical mechanisms responsible for the formation of these superstructures are quite different, the experimental results can be interpreted within the same phenomenological framework. A comparison between experiments and modeling shows that, in the cases of KBr films on NaCl(001) and of graphene films, the tip-surface interaction is well described by a potential with the periodicity of the substrate which is modulated or, respectively, superimposed with a potential with the symmetry of the superstructure.     

Nonlinear predictive control for a NNARX hydro plant model

A neural network (NN)-based nonlinear predictive control (NPC) is described for control of turbine power with variation in gate position. The studied plant includes the tunnel, surge tank and penstock effect dynamics. Multilayer perceptron neural network is chosen to represent a neural network nonlinear autoregressive with exogenous signal model of hydro power plant. With the said NN model configuration, quasi-Newton and Levenberg–Marquardt iterative optimization algorithms are applied in order to determine optimal predictive control parameters. The controlled response is simulated on different amplitude step function and trapezoidal shape reference signal. The study also discusses comparison with an approximate predictive control approach, being linearized around operating points. It is shown that NPC strategy gives impressive results in comparison to the approximated one.     

SOFT COMPUTING TECHNIQUES FOR PREDICTION OF BOILING HEAT TRANSFER COEFFICIENT OF LIQUIDS ON COPPER-COATED TUBES

In this article pool boiling heat transfer coefficient (HTC) of liquids (isopropanol, methanol, and distilled water) on copper-coated heating tubes over a wide range of pressure conditions is computed experimentally. The objective is to find the applicability of soft computing techniques, swarm-intelligence based neural network, and adaptive fuzzy models in the prediction of boiling HTC. The results are compared with those computed experimentally. The performance of models for prediction of HTC is analyzed in terms of root mean square of prediction error. The minimum/maximum value obtained by zero-order fuzzy model with six membership function is 0.0023/3.4383 among all the liquids considered. The model is found to predict HTC with a maximum error of ±0.5% for boiling of liquids over all the coated tubes with pressure varying from atmospheric to subatmospheric levels. The study shows an excellent agreement between the experimental and predicted data.