The removal of NOx from a lean exhaust gas using storage and reduction on H3PW12O40·6H2O

NO_x sorption and reduction capacities of 12-tungstophosphoric acid hexahydrate (H₃PW₁₂O₄₀·6H₂O, HPW) were measured under representative alternating conditions of lean and rich exhaust-type gas mixture. Under lean conditions, the sorption of NO_x is large and is equivalent to 37 mg of NO_x/g_HPW. Although a part of these NO_x remains unreduced, HPW is able to reduce some of the NO_x to produce N₂ by a reaction between the sorbed NO₂ and hydrocarbon (HC), but this process is slow. The addition of 1% Pt affects strongly the chemical behaviour occurring during the course of a rich operation. The NO desorption observed at the beginning of the rich phase is strongly accelerated. The direct correlation between NO₂ consumption and CO₂ production shows that the principal pathway is the reaction CO+NO₂→CO₂+NO. In a mixture of reducing gas (CO, HC, H₂), the competition is strongly in favour of CO though in its absence the reaction observed was the hydrogenation of propene to propane.     

An enriched FEM technique for modeling hydraulically driven cohesive fracture propagation in impermeable media with frictional natural faults: Numerical and experimental investigations

In this paper, an enriched finite element technique is presented to simulate the mechanism of interaction between the hydraulic fracturing and frictional natural fault in impermeable media. The technique allows modeling the discontinuities independent of the finite element mesh by introducing additional DOFs. The coupled equilibrium and flow continuity equations are solved using a staggered Newton solution strategy, and an algorithm is proposed on the basis of fixed‐point iteration concept to impose the flow condition at the hydro‐fracture mouth. The cohesive crack model is employed to introduce the nonlinear fracturing process occurring ahead of the hydro‐fracture tip. Frictional contact is modeled along the natural fault using the penalty method within the framework of plasticity theory of friction. Moreover, an experimental investigation is carried out to perform the hydraulic fracturing experimental test in fractured media under plane strain condition. The results of several numerical and experimental simulations are presented to verify the accuracy and robustness of the proposed computational algorithm as well as to investigate the mechanisms of interaction between the hydraulically driven fracture and frictional natural fault. Copyright © 2015 John Wiley & Sons, Ltd.     

Thermal Stability of <Emphasis Type="Italic">α</Emphasis> Phase of Titanium by Using X-Ray Diffraction

At room temperature and ambient pressure, crystalline titanium has a hexagonal-close-packed (hcp) lattice and at high temperature appears as a body-centered-cubic (bcc) structure. In fact, the phase transitions of titanium have been investigated under various pressures and temperatures. However, the phase transitions of titanium have been mostly reported at high pressure, while less attention has been paid to various ranges of high temperature. Therefore, in this study, we have considered the thermal stability of α phase of titanium by using X-ray diffraction (XRD) at high temperature. The observed experimental results of the diffraction show that the stability range of α phase varies between room temperature to around 923 K (650 °C).     

Two-stage Robust Network Design with Exponential Scenarios

We study two-stage robust variants of combinatorial optimization problems on undirected graphs, like Steiner tree, Steiner forest, and uncapacitated facility location. Robust optimization problems, previously studied by Dhamdhere et al. (Proc. of 46th Annual IEEE Symposium on Foundations of Computer Science (FOCS’05), pp. 367–378, 2005), Golovin et al. (Proc. of the 23rd Annual Symposium on Theoretical Aspects of Computer Science (STACS), 2006), and Feige et al. (Proc. of the 12th International Integer Programming and Combinatorial Optimization Conference, pp. 439–453, 2007), are two-stage planning problems in which the requirements are revealed after some decisions are taken in Stage 1. One has to then complete the solution, at a higher cost, to meet the given requirements. In the robust k-Steiner tree problem, for example, one buys some edges in Stage 1. Then k terminals are revealed in Stage 2 and one has to buy more edges, at a higher cost, to complete the Stage 1 solution to build a Steiner tree on these terminals. The objective is to minimize the total cost under the worst-case scenario. In this paper, we focus on the case of exponentially many scenarios given implicitly. A scenario consists of any subset of k terminals (for k-Steiner tree), or any subset of k terminal-pairs (for k-Steiner forest), or any subset of k clients (for facility location). Feige et al. (Proc. of the 12th International Integer Programming and Combinatorial Optimization Conference, pp. 439–453, 2007) give an LP-based general framework for approximation algorithms for a class of two stage robust problems. Their framework cannot be used for network design problems like k-Steiner tree (see later elaboration). Their framework can be used for the robust facility location problem, but gives only a logarithmic approximation. We present the first constant-factor approximation algorithms for the robust k-Steiner tree (with exponential number of scenarios) and robust uncapacitated facility location problems. Our algorithms are combinatorial and are based on guessing the optimum cost and clustering to aggregate nearby vertices. For the robust k-Steiner forest problem on trees and with uniform multiplicative increase factor for Stage 2 (also known as inflation), we present a constant approximation. We show APX-hardness of the robust min-cut problem (even with singleton-set scenarios), resolving an open question of (Dhamdhere et al. in Proc. of 46th Annual IEEE Symposium on Foundations of Computer Science (FOCS’05), pp. 367–378, 2005) and (Golovin et al. in Proc. of the 23rd Annual Symposium on Theoretical Aspects of Computer Science (STACS), 2006).     

A local linear radial basis function neural network for financial time-series forecasting

In this paper a Local Linear Radial Basis Function Neural Network (LLRBFN) is presented. The difference between the proposed neural network and the conventional Radial Basis Function Neural Network (RBFN) is connection weights between the hidden layer and the output layer which are replaced by a local linear model in the LLRBFN. A modified Particle Swarm Optimization (PSO) with hunter particles is introduced for training the LLRBFN. The proposed methods have been applied for prediction of financial time-series and the result shows the feasibility and effectiveness.     

Calculation of Nano-charged Particles Pulsation Frequency in Tokamak

One of the unknown problems that cause to perturbation of tokamak function is charged particles pulsation. Applying the toroidal magnetic field to the charged motive particles implicate plasma in the system. This plasma has a frequency that depends on the toroidal magnetic field. Another frequency exists in this system that is caused to the plasma self magnetism. This lateral pulsation is principal origin of perturbation in the system. In this paper, nano-charged particles pulsation is investigated and its frequency is calculated by Buckingham method. Knowing this frequency is necessary to control perturbation. The result of calculations shows that temperature increasing of the system cause to more intense perturbation.     

On the use of the RMR system for estimation of rock mass deformation modulus

Estimation of rock mass deformation modulus is the subject of many studies in rock engineering research work. Although numerous predictive models have been developed for the estimation of the deformation modulus, they cannot be generalized for other sites because of inadequate accuracy. Furthermore, it is very valuable that the predictive models involve some accessible input parameters. The rock mass rating (RMR) is a well-known geomechanical parameter, which is usually determined to describe the quality of rock mass in rock engineering projects. In this study, five parameter ratings of the RMR classification system are used to predict the deformation modulus of rock mass in the abutment of the Gotvand earth dam. Statistical analysis and an artificial neural network are employed to present two new predictive models. Finally, probabilistic analysis is used to predict the rock mass deformation modulus, which overcomes the low accuracy caused by the inherent uncertainty in prediction. The results indicated that the parameter ratings used in the RMR classification system can predict the rock mass deformation modulus with a satisfactory correlation. However, the parameters don’t have the same influence on the rock mass deformability with the joint condition and the groundwater as the major and minor influencing parameters, respectively.     

A fuzzy irregular cellular automata-based method for the vertex colouring problem

ABSTRACT

Vertex colouring is among the most important problems in graph theory which has been widely applied across different real-world problems. In vertex colouring problem (VCP), the goal is to assign a distinct colour to each vertex of the graph in such a way that no two adjacent vertices have the same colour. This paper presents a fuzzy irregular cellular automaton (FICA) for finding a near-optimal solution for the VCP. FICA is an extension fuzzy cellular automaton (FCA) in which the cells of the automaton can be arranged in an irregular structure. The aim of the proposed method is to reap the benefits of both FCA and irregular cellular automata while minimising their drawbacks. To evaluate the proposed method, various computer simulations have been conducted on a variety of graphs. The results suggest that the proposed method is able to achieve better results in terms of the minimum number of required colours and the execution time of the algorithm, compared to other peer algorithms.