An engineering method for complex structural optimization involving both size and topology design variables

This work presents an engineering method for optimizing structures made of bars, beams, plates, or a combination of those components. Corresponding problems involve both continuous (size) and discrete (topology) variables. Using a branched multipoint approximate function, which involves such mixed variables, a series of sequential approximate problems are constructed to make the primal problem explicit. To solve the approximate problems, genetic algorithm (GA) is utilized to optimize discrete variables, and when calculating individual fitness values in GA, a second-level approximate problem only involving retained continuous variables is built to optimize continuous variables. The solution to the second-level approximate problem can be easily obtained with dual methods. Structural analyses are only needed before improving the branched approximate functions in the iteration cycles. The method aims at optimal design of discrete structures consisting of bars, beams, plates, or other components. Numerical examples are given to illustrate its effectiveness, including frame topology optimization, layout optimization of stiffeners modeled with beams or shells, concurrent layout optimization of beam and shell components, and an application in a microsatellite structure. Optimization results show that the number of structural analyses is dramatically decreased when compared with pure GA while even comparable to pure sizing optimization.     

Structural,magnetic and magnetostrictive properties of Co1-xLixFe2O4 synthesized by cathode materials of spent Li-ion batteries

In this study, we investigated the effects of substituting Li⁺ for Co²⁺ at the B sites of the spinel lattice on the structural, magnetic and magnetostrictive properties of cobalt ferrites. The Li⁺ substituted cobalt ferrites, Co_1-xLi_xFe₂O₄, with x varying from 0 to 0.7 in 0.1 increments, were synthesized with a sol-gel auto-combustion method using the cathode materials of spent Li-ion batteries. X-ray diffraction analysis revealed that all the Co_1-xLi_xFe₂O₄ nanopowders had a single-phase spinel structure and the lattice parameters decreased with increasing Li⁺ content, which can be proved by slight shifts towards higher diffraction angle values of the (311) peak. Field emission scanning electron microscopy was used to observe the fractured inner surface of the sintered cylindrical rods and the increased porosity resulted in a decreased magnetostriction. The oxidation states of Co and Fe in the cobalt ferrite samples were examined by X-ray photoelectron spectroscopy. High resolution transmission electron microscopy micrographs showed that most particles were roughly spherical and with sizes of 25–35?nm. Li⁺ substitution had a strong effect on the saturation magnetization and coercivity, which were characterized with a vibrating sample magnetometer. The Curie temperature was reduced due to the decrease in magnetic cations and the weakening of the exchange interactions. The magnetostrictive properties were influenced by the incorporation of Li⁺ at the B sites of the spinel structure and correlated with the changes in porosity, magnetocrystalline anisotropy and the cation distribution.     

An iterative method for suboptimal control of linear time-delayed systems

This article presents a new approach for solving the Optimal Control Problem (OCP) of linear time-delay systems with a quadratic cost functional. The proposed method can also be used for designing optimal control time-delay systems with disturbance. In this study, the Variational Iteration Method (VIM) is employed to convert the original Time-Delay Optimal Control Problem (TDOCP) into a sequence of nonhomogeneous linear two-point boundary value problems (TPBVPs). The optimal control law obtained consists of an accurate linear feedback term and a nonlinear compensation term which is the limit of an adjoint vector sequence. The feedback term is determined by solving Riccati matrix differential equation. By using the finite-step iteration of a nonlinear compensation sequence, we can obtain a suboptimal control law. Finally, Illustrative examples are included to demonstrate the validity and applicability of the technique.     

环境中污染源废气检测方法及影响因素分析

对污染源废气监测原理进行了简单分析,阐述了当前我国常用的环境中污染源废气检测方法,并对废气检测影响因素及质量控制进行了深入探究。     

Enhanced transduction coefficient in piezoelectric PZT ceramics by mixing powders calcined at different temperatures

Large transduction coefficient ($d_{33}\times g_{33}$) is difficult to obtain in piezoelectric ceramics because these two parameters show opposite trends with compositional modifications. Herein, the Pb(Zr_0.53Ti_0.47)O₃ ceramic powders were calcinated under different temperatures (A:830 °C, B:860 °C, and C:890 °C), and then mixed together according to different weight ratios (1A:1B:1C, 1A:2B:1C, 1A:2B:3C and 3A:2B:1C) for ceramics preparation. Both d₃₃ and g₃₃ are improved successfully, and the transduction coefficient with the weight ratio of 1A:2B:3C reaches up to 17,500 × 10⁻¹⁵ m²/N, which is 60 % higher than that with the powders calcinated under 830 °C, and at least twice those of commercial PZT-4, PZT-5A and PZT-8 ceramics. The improved transduction coefficient is owing to the enhanced piezoelectric constant and spontaneous polarization resulted from the increased grain size, relative density and the fraction of tetragonal phase. These results indicate that this is a simple but effective way to tailor the transduction coefficient in piezoelectric ceramics.     

宁夏宁东供水工程主机设备形式参数选择与国际招标

宁东供水工程供水水泵共有卧式和立式离心泵2种方案可供选择,也是工程论证时争论的焦点,经综合比较及主机设备的国际招标,最终确定了奥地利安德里兹公司的卧式双吸中开式离心泵为宁东供水工程的供水水泵。     

Model Selection for Unsupervised Learning of Visual Context

This study addresses the problem of choosing the most suitable probabilistic model selection criterion for unsupervised learning of visual context of a dynamic scene using mixture models. A rectified Bayesian Information Criterion (BICr) and a Completed Likelihood Akaike’s Information Criterion (CL-AIC) are formulated to estimate the optimal model order (complexity) for a given visual scene. Both criteria are designed to overcome poor model selection by existing popular criteria when the data sample size varies from small to large and the true mixture distribution kernel functions differ from the assumed ones. Extensive experiments on learning visual context for dynamic scene modelling are carried out to demonstrate the effectiveness of BICr and CL-AIC, compared to that of existing popular model selection criteria including BIC, AIC and Integrated Completed Likelihood (ICL). Our study suggests that for learning visual context using a mixture model, BICr is the most appropriate criterion given sparse data, while CL-AIC should be chosen given moderate or large data sample sizes.