Simulation of a Bubble Column by Computational Fluid Dynamics and Population Balance Equation Using the Cell Average Method

An axisymmetric computational fluid dynamics (CFD) simulation coupled with a population balance equation (PBE) has been applied in simulating the gas‐liquid flow in a bubble column with an in‐house code. The novel feature of this simulation is the application of the cell average method in a CFD‐PBE coupled model for the first time. The predicted results by this method are compared with those by the traditional fixed pivot method and experimental data. For both methods, the simulated results are in reasonable agreement with the reported experimentally measured values. However, the bubble size distributions determined by the cell average method are slightly better than those found by means of the fixed pivot method, i.e., the latter provides a smaller peak value and a wider bubble size distribution, and the probability density function of large bubbles is higher.     

Modeling of a DC glow discharge in a neon–xenon gas mixture at low pressure and with metastable atom densities

The physical properties of Ne–Xe DC glow discharges at low pressure are reported for a gap length of 1 cm for the first time in the literature. The model deals specifically with the first three moments of Boltzmann's equation and includes the radiation processes and metastable atom densities. The spatio-temporal distributions of the electron and neon and xenon ion densities, the neon and xenon metastable atom densities, the electric potential and the electric field as well as the mean electron energy are presented at 1.5 Torr and 250 V. The current–voltage characteristic is shown at 3 Torr, and it is compared with previous work for pure neon gas. The model is validated theoretically and experimentally in the case of pure gas.     

Grammar-based generation of stochastic local search heuristics through automatic algorithm configuration tools

Several grammar-based genetic programming algorithms have been proposed in the literature to automatically generate heuristics for hard optimization problems. These approaches specify the algorithmic building blocks and the way in which they can be combined in a grammar; the best heuristic for the problem being tackled is found by an evolutionary algorithm that searches in the algorithm design space defined by the grammar.In this work, we propose a novel representation of the grammar by a sequence of categorical, integer, and real-valued parameters. We then use a tool for automatic algorithm configuration to search for the best algorithm for the problem at hand. Our experimental evaluation on the one-dimensional bin packing problem and the permutation flowshop problem with weighted tardiness objective shows that the proposed approach produces better algorithms than grammatical evolution, a well-established variant of grammar-based genetic programming. The reasons behind such improvement lie both in the representation proposed and in the method used to search the algorithm design space.     

A study of equivalent electrical circuit fitting to electrochemical impedance using a stochastic method

Modeling electrochemical impedance spectroscopy is usually done using equivalent electrical circuits. These circuits have parameters that need to be estimated properly in order to make possible the simulation of impedance data. Despite the fitting procedure is an optimization problem solved recurrently in the literature, rarely statistical significance of the estimated parameters is evaluated. In this work, the optimization process for the equivalent electrical circuit fitting to the impedance data is detailed. First, a mathematical development regarding the minimization of residual least squares is presented in order to obtain a statistically valid objective function of the complex nonlinear regression problem. Then, the optimization method used in this work is presented, the Differential Evolution, a global search stochastic method. Furthermore, it is shown how a population-based stochastic method like this can be used directly to obtain confidence regions to the estimated parameters. A sensitivity analysis was also conducted. Finally, the equivalent circuit fitting is done to model synthetic experimental data, in order to demonstrate the adopted procedure.     

一种复合型发泡剂ADC产品的研制

介绍一种有机固体为活化剂生产复合型发泡剂ADC产品方法,分析了复合型发泡剂ADC产品的生产技术,产品的发气量及分解温度与产品中活化剂成份比例关系等因素。     

Using differential evolution for fine tuning naïve Bayesian classifiers and its application for text classification

The Naive Bayes (NB) learning algorithm is simple and effective in many domains including text classification. However, its performance depends on the accuracy of the estimated conditional probability terms. Sometimes these terms are hard to be accurately estimated especially when the training data is scarce. This work transforms the probability estimation problem into an optimization problem, and exploits three metaheuristic approaches to solve it. These approaches are Genetic Algorithms (GA), Simulated Annealing (SA), and Differential Evolution (DE). We also propose a novel DE algorithm that uses multi-parent mutation and crossover operations (MPDE) and three different methods to select the final solution. We create an initial population by manipulating the solution generated by a method used for fine tuning the NB. We evaluate the proposed methods by using their resulted solutions to build NB classifiers and compare their results with the results of obtained from classical NB and Fine-Tuning Naïve Bayesian (FTNB) algorithm, using 53 UCI benchmark data sets. We name these obtained classifiers NBGA, NBSA, NBDE, and NB-MPDE respectively. We also evaluate the performance NB-MPDE for text-classification using 18 text-classification data sets, and compare its results with the results of obtained from FTNB, BNB, and MNB. The experimental results show that using DE in general and the proposed MPDE algorithm in particular are more convenient for fine-tuning NB than all other methods, including the other two metaheuristic methods (GA, and SA). They also indicate that NB-MPDE achieves superiority over classical NB, FTNB, NBDE, NBGA, NBSA, MNB, and BNB.     

Interfacial reaction during fabricating of full Cu3Sn joints in microelectronic packaging

Two copper substrates electroplated with Sn, both consisted of Cu/Sn?+?Sn/Cu structures, but they were bonded over different times in order to investigate the interfacial reaction. The growth morphologies of Cu₆Sn₅ and Cu₃Sn were analysed, respectively. The growth mechanisms for Cu₆Sn₅ and Cu₃Sn were investigated. The results show that the growth of Cu₆Sn₅ is primarily controlled by grain boundary-diffusion. However, the growth Cu₃Sn is controlled by the reaction of Cu-Cu₆Sn₅ at the beginning of the reaction, and then controlled by volume-diffusion as the thickness of the Cu₃Sn layer increases.     

A high accurate static contact angle algorithm based on Hough transformation

The static contact angle for blurry drop images is more intricate to obtain. To improve the accuracy of the Hough transformation for the static contact angle calculation, the water drop images with different volumes are generated by the Laplace equation, and the influence of the volume on the accuracy of the Hough transformation is analyzed. The results reveal that the circle Hough transformation is particularly well suited to the cases with small drop volume. At the same time, the critical water drop volume value corresponding to a contact angle error of 3° is given, a modified Hough transformation algorithm in conjunction with the critical water drop volume is proposed, and at the same time, the accuracy of the static contact angle calculation for blurry water drop images is significantly improved. The proposed algorithm is a powerful approach to estimate the static contact angle for blurry water drop images.     

A homotopy analysis method for the nonlinear partial differential equations arising in engineering

In this article, we have established the homotopy analysis method (HAM) for solving a few partial differential equations arising in engineering. This technique provides the solutions in rapid convergence series with computable terms for the problems with high degree of nonlinear terms appearing in the governing differential equations. The convergence analysis of the proposed method is also discussed. Finally, we have given some illustrative examples to demonstrate the validity and applicability of the proposed method.     

Event-triggered control for semi-global stabilisation of systems with actuator saturation

This paper investigates the problem of event-triggered control for semi-global stabilisation of null controllable systems subject to actuator saturation. First, for a continuous-time system, novel event-triggered low-gain control algorithms based on Riccati equations are proposed to achieve semi-global stabilisation. The algebraic Riccati equation with a low-gain parameter is utilised to design both the event-triggering condition and the linear controller; a minimum inter-event time based on the Riccati ordinary differential equation is set a priori to exclude the Zeno behaviour. In addition, the high–low gain techniques are utilised to extend the semi-global results to event-based global stabilisation. Furthermore, for a discrete-time system, novel low-gain and high–low-gain control algorithms are proposed to achieve event-triggered stabilisation. Numerical examples are provided to illustrate the theoretical results.