Tungsten Oxide Nanorods Array and Nanobundle Prepared by Using Chemical Vapor Deposition Technique

Tungsten oxide (WO₃) nanorods array prepared using chemical vapor deposition techniques was studied. The influence of oxygen gas concentration on the nanoscale tungsten oxide structure was observed; it was responsible for the stoichiometric and morphology variation from nanoscale particle to nanorods array. Experimental results also indicated that the deposition temperature was highly related to the morphology; the chemical structure, however, was stable. The evolution of the crystalline structure and surface morphology was analyzed by scanning electron microscopy, Raman spectra and X-ray diffraction approaches. The stoichiometric variation was indicated by energy dispersive X-ray spectroscopy and X-ray photoelectron spectroscopy.     

The Bradford distribution and the Gini index

It is pointed out that the so-called Bradford distribution derived by Leimkuhler is more properly viewed as the theoretical form of a variant of the Lorenz curve. The equation of this Leimkuhler curve allows an easy calculation of the Gini coefficient of concentration which can be compared with empirical values.     

Size and pressure effects on solid transition temperatures of ZrO2

Transition temperatures between tetragonal and monoclinic polymorphs of ZrO₂ nanoparticles, thin films and nanostructured materials are calculated by considering energetic contributions of surface (interface) energy and surface stress on total Gibbs free energy. The transition temperatures drop as the size of the nanocrystals decreases, which is consistent with available results.     

Sensitivity analysis based preform die shape design using the finite element method

This paper uses a finite element-based sensitivity analysis method to design the preform die shape for metal forming processes. The sensitivity analysis was developed using the rigid visco-plastic finite element method. The preform die shapes are represented by cubic B-spline curves. The control points or coefficients of the B-spline are used as the design variables. The optimization problem is to minimize the difference between the realized and the desired final forging shapes. The sensitivity analysis includes the sensitivities of the objective function, nodal coordinates, and nodal velocities with respect to the design variables. The remeshing procedure and the interpolation/transfer of the history/dependent parameters are considered. An adjustment of the volume loss resulting from the finite element analysis is used to make the workpiece volume consistent in each optimization iteration and improve the optimization convergence. In addition, a technique for dealing with fold-over defects during the forming simulation is employed in order to continue the optimization procedures of the preform die shape design. The method developed in this paper is used to design the preform die shape for both plane strain and axisymmetric deformations with shaped cavities. The analysis shows that satisfactory final forging shapes are obtained using the optimized preform die shapes.     

Banding formation and eutectic lamellar growth in directional solidified Ni50Al20Fe30 alloy

Banding formation and eutectic lamellar growth in a directionally solidified Ni₅₀Al₂₀Fe₃₀ alloy were investigated. It was found that the banding area consists of two layers. The first layer is a γ layer, while the subsequent one is a γ layer. The composition of various phases around the banding area changes with the solidification process. The banding is formed by two steps process and caused by factors such as the fractions during the sample growth process. It was found that the band was found at relatively low growth rate. Therefore. this study indicates that increasing the growth rate is an effective method to eliminate the band formation. Eutectic lamellae nucleate and grow again after the banding formation. During the initial transition lamellar growth, the relationship between the square lamellar spacing, γ₂, and the distance from, the banding, d, can be described by the following equation: γ₂ = K [1-exp(A.d)] where K and A are constant.     

Filtering Multi‐Layer Shadow Maps for Accurate Soft Shadows

In this paper, we introduce a novel technique for pre‐filtering multi‐layer shadow maps. The occluders in the scene are stored as variable‐length lists of fragments for each texel. We show how this representation can be filtered by progressively merging these lists. In contrast to previous pre‐filtering techniques, our method better captures the distribution of depth values, resulting in a much higher shadow quality for overlapping occluders and occluders with different depths. The pre‐filtered maps are generated and evaluated directly on the GPU, and provide efficient queries for shadow tests with arbitrary filter sizes. Accurate soft shadows are rendered in real‐time even for complex scenes and difficult setups. Our results demonstrate that our pre‐filtered maps are general and particularly scalable.     

Probability‐weighted nonlinear stochastic optimal control strategy of quasi‐integrable Hamiltonian systems with uncertain parameters

The nonlinear stochastic optimal control problem of quasi‐integrable Hamiltonian systems with uncertain parameters is investigated. The uncertain parameters are described by using a random vector with λ probability density function. First, the partially averaged Itô stochastic differential equations are derived by using the stochastic averaging method for quasi‐integrable Hamiltonian systems. Then, the dynamical programming equation is established based on stochastic dynamical programming principle. By minimizing the dynamical programming equation with respect to control forces, the optimal control forces can be derived, which are functions of the uncertain parameters. The final optimal control forces are then determined by probability‐weighted average of the obtained control forces with the probability density of the uncertain parameters as weighting function. The mean control effectiveness and mean control efficiency are used to evaluate the proposed control strategy. The robustness of the proposed control is measured by using the ratios of the variation coefficients of mean control effectiveness and mean control efficiency to the variation coefficients of uncertain parameters. Finally, two examples are given to illustrate the proposed control strategy and its effectiveness and robustness. Copyright © 2014 John Wiley & Sons, Ltd.     

Active disturbance rejection control: between the formulation in time and the understanding in frequency

With the rapid deployments of the active disturbance rejection control (ADRC) as a bonafide industrial technology in the background, this paper summarizes some recent results in the analysis of linear ADRC and offers explanations in the frequency response language with which practicing engineers are familiar. Critical to this endeavor is the concept of bandwidth, which has been used in a more general sense. It is this concept that can serve as the link between the otherwise opaque state space formulation of the ADRC and the command design considerations and concerns shared by practicing engineers. The remarkable characteristics of a simple linear ADRC was first shown in the frequency domain, followed by the corresponding analysis in time domain, where the relationship between the tracking error and the ADRC bandwidth is established. It is shown that such insight is only possible by using the method of solving linear differential equations, instead of the more traditional techniques such as the Lyapunov methods, which tend to be more conservative and difficult to grasp by engineers. The insight obtained from such analysis is further demonstrated in the simulation validation.