Low-temperature constrained sintering of YSZ electrolyte with Bi2O3 sintering sacrificial layer for anode-supported solid oxide fuel cells

Solid oxide fuel cells (SOFCs) have strong potential for next-generation energy conversion systems. However, their high processing temperature due to multi-layer ceramic components has been a major challenge for commercialization. In particular, the constrained sintering effect due to the rigid substrate in the fabrication process is a main reason to increase the sintering temperature of ceramic electrolyte. Herein, we develop a bi-layer sintering method composed of a Bi₂O₃ sintering sacrificial layer and YSZ main electrolyte layer to effectively lower the sintering temperature of the YSZ electrolyte even under the constrained sintering conditions. The Bi₂O₃ sintering functional layer applied on the YSZ electrolyte is designed to facilitate the densification of YSZ electrolyte at the significantly lowered sintering temperature and is removed after the sintering process to prevent the detrimental effects of residual sintering aids. Subsequent sublimation of Bi₂O₃ was confirmed after the sintering process and a dense YSZ monolayer was formed as a result of the sintering functional layer-assisted sintering process. The sintering behavior of the Bi₂O₃/YSZ bi-layer system was systematically analyzed, and material properties including the microstructure, crystallinity, and ionic conductivity were analyzed. The developed bi-layer sintered YSZ electrolyte was employed to fabricate anode-supported SOFCs, and a cell performance comparable to a conventional high temperature sintered (1400 °C) YSZ electrolyte was successfully demonstrated with significantly reduced sintering temperature (<1200 °C).     

Constrained differential evolution with multiobjective sorting mutation operators for constrained optimization

Differential evolution (DE) is a simple and powerful evolutionary algorithm for global optimization. DE with constraint handling techniques, named constrained differential evolution (CDE), can be used to solve constrained optimization problems (COPs). In existing CDEs, the parents are randomly selected from the current population to produce trial vectors. However, individuals with fitness and diversity information should have more chances to be selected. This study proposes a new CDE framework that uses nondominated sorting mutation operator based on fitness and diversity information, named MS-CDE. In MS-CDE, firstly, the fitness of each individual in the population is calculated according to the current population situation. Secondly, individuals in the current population are ranked according to their fitness and diversity contribution. Lastly, parents in the mutation operators are selected in proportion to their rankings based on fitness and diversity. Thus, promising individuals with better fitness and diversity are more likely to be selected as parents. The MS-CDE framework can be applied to most CDE variants. In this study, the framework is applied to two popular representative CDE variants, (μ + λ)-CDE and ECHT-DE. Experiment results on 24 benchmark functions from CEC’2006 and 18 benchmark functions from CEC’2010 show that the proposed framework is an effective approach to enhance the performance of CDE algorithms.     

A new MM algorithm for constrained estimation in the proportional hazards model

The constrained estimation in Cox’s model for the right-censored survival data is studied and the asymptotic properties of the constrained estimators are derived by using the Lagrangian method based on Karush–Kuhn–Tucker conditions. A novel minorization–maximization (MM) algorithm is developed for calculating the maximum likelihood estimates of the regression coefficients subject to box or linear inequality restrictions in the proportional hazards model. The first M-step of the proposed MM algorithm is to construct a surrogate function with a diagonal Hessian matrix, which can be reached by utilizing the convexity of the exponential function and the negative logarithm function. The second M-step is to maximize the surrogate function with a diagonal Hessian matrix subject to box constraints, which is equivalent to separately maximizing several one-dimensional concave functions with a lower bound and an upper bound constraint, resulting in an explicit solution via a median function. The ascent property of the proposed MM algorithm under constraints is theoretically justified. Standard error estimation is also presented via a non-parametric bootstrap approach. Simulation studies are performed to compare the estimations with and without constraints. Two real data sets are used to illustrate the proposed methods.     

Weighted Superposition Attraction (WSA): A swarm intelligence algorithm for optimization problems – Part 2: Constrained optimization

This paper is the second one of the two papers entitled “Weighted Superposition Attraction (WSA) Algorithm”, which is about the performance evaluation of the WSA algorithm in solving the constrained global optimization problems. For this purpose, the well-known mechanical design optimization problems, design of a tension/compression coil spring, design of a pressure vessel, design of a welded beam and design of a speed reducer, are selected as test problems. Since all these problems were formulated as constrained global optimization problems, WSA algorithm requires a constraint handling method for tackling them. For this purpose we have selected 6 formerly developed constraint handling methods for adapting into WSA algorithm and analyze the effect of the used constraint handling method on the performance of the WSA algorithm. In other words, we have the aim of producing concluding remarks over the performance and robustness of the WSA algorithm through a set of computational study in solving the constrained global optimization problems. Computational study indicates the robustness and the effectiveness of the WSA in terms of obtained results, reached level of convergence and the capability of coping with the problems of premature convergence, trapping in a local optima and stagnation.     

Reference governors and maximal output admissible sets for linear periodic systems

ABSTRACT

In this paper, we consider the problem of constraint management in Linear Periodic (LP) systems using Reference Governors (RG). First, we introduce the periodic-invariant maximal output admissible sets for LP systems. We extend the earlier results in the literature to Lyapunov stable LP systems with output constraints, which arise in RG applications. We show that, while the invariant sets for these systems may not be finitely determined, a finitely-determined inner approximation, which is periodically invariant, can be obtained by constraint tightening. We then analyze the geometric and algebraic relationship between these sets and show that these sets are related via simple transformations, implying that it suffices to compute only one of them for real-time applications. This greatly reduces the memory burden of RG (or other similar constraint management strategies), at the expense of an increase in processing requirements. We present a thorough analysis of this trade-off. In the second part of this paper, we present two RG formulations, and discuss their properties and algorithms for their computation. Numerical simulations demonstrate the efficacy of the approach.     

Methodology for robust multi-parametric control in linear continuous-time systems

This paper presents an extension of the recent multi-parametric (mp-)NCO-tracking methodology by Sun et al. [Comput. Chem. Eng. 92 (2016) 64–77] for the design of robust multi-parametric controllers for constrained continuous-time linear systems in the presence of uncertainty. We propose a robust-counterpart formulation and solution of multi-parametric dynamic optimization (mp-DO), whereby the constraints are backed-off based on a worst-case propagation of the uncertainty using either interval analysis or ellipsoidal calculus and an ancillary linear state feedback. We address the case of additive uncertainty, and we discuss approaches to dealing with multiplicative uncertainty that retain tractability of the mp-NCO-tracking design problem, subject to extra conservativeness. In order to assist with the implementation of these controllers, we also investigate the use of data classifiers based on deep learning for approximating the critical regions in continuous-time mp-DO problems, and subsequently searching for a critical region during on-line execution. We illustrate these developments with the case studies of a fluid catalytic cracking (FCC) unit and a chemical reactor cascade.     

Microstructure evolution during the co-sintering of Ni/BaTiO3 multilayer ceramic capacitors modeled by discrete element simulations

Electrode discontinuities are a critical issue for manufacturing ultrathin multilayer ceramic capacitors (MLCCs). The Discrete Element Method is used to simulate, at the particle length scale, the microstructure evolution during the co-sintering of state-of-the-art nickel based-MLCCs. Electrode discontinuities are considered to originate from the heterogeneities in the initial powder packing and to grow because of the constraint imposed by adjacent dielectric layers. A parametric study demonstrates that: (i) fast heating rate leads to lower electrode discontinuity during heating, (ii) green density and thickness of the electrode should be optimized to improve the electrode connectivity, (iii) rearrangement of the nickel particles plays a significant role in electrode discontinuity, and (iv) the addition of non-sintering inclusions can improve the electrode connectivity. These findings can be generalized to other multilayer components.     

Mechanistic origins of multi-scale reinforcements in segmented polyurethane-clay nanocomposites

The objective of the present work is to get insights into the mechanistic origin of the reinforcement effects of nanoclay on a segmented polybutadiene polyurethane-urea system. To this end, a convergent analysis of the hard domain morphology and conformational state of soft segment in the nanocomposites was carried out by using a combination of complementary characterization techniques, namely, Fourier transform infrared spectroscopy, small angle neutron scattering, transmission electron microscopy, modulated differential scanning calorimetry and dynamic mechanical analysis. Analysis of small angle neutron scattering data by a combination of Percus–Yevick hard sphere and Zernike-Ornstein model coupled with direct visualization of the dispersed hard domain morphology from transmission electron microscopy provided insight on clay induced changes in the hard domain morphology. A monotonic decrease in the domain size as well as the average interdomain distance was observed with increasing nanoclay content in the polymer matrix. Analysis of the carbonyl stretching region from FTIR showed increased degree of hydrogen bonding for the urethane carbonyl groups of the nanocomposites compared to the neat matrix. A combination of calorimetric and dynamic mechanical analysis revealed the existence of a constrained amorphous region; quantified to be ≈ 16% at the highest clay content experimented. The manifestation of these morphological and conformational changes on the nano-, micro- and macro scale reinforcements in the nanocomposites was investigated by mechanical properties at these length scales using nanoindentation, DMA and tensile testing, respectively.     

卵形消化池内力分析及基于遗传算法的形状优化

本文以静水压力作用下卵形消化池壳体的薄膜内力为基础 ,建立以体表面积为目标函数 ,以容积、高度 ,应力等为约束条件的优化模型。使用遗传算法对以上模型进行优化 ,并引进动态罚函数和对约束条件不断逼近的方法处理约束条件。从而为初步设计阶段卵形消化池形状的设计提供了建议和参数。     

A Novel Ironing Process with Extra High Thickness Reduction: Constrained Ironing

A new ironing method entitled constrained ironing is proposed for producing thin-walled cans and components with uniform thickness. This method based on compressive stresses could reach a higher ironing limit ratio or thickness reduction ratio (TRR) without interruption for additional processing such as multi-stage ironing and annealing between different stages. When the constrained ironing starts, the punch pushes the constrained material to reduce the thickness from the outer surface of the cup. The state of the stresses is fully compressive in this new process while it is tensile in the conventional ironing method. The results showed that after constrained ironing process, the tensile strength and hardness increased to 204 MPa and 85 HV, respectively, from the initial values of 71 MPa and 25 HV. Thus, very high TRR is achievable in the constrained ironing process. Obtaining a higher TRR about 80% after only single stage ironing, removing the annealing stages, and obtaining higher strength and hardness of the ironed cup are several advantages of the proposed method. This novel and simple process could be very promising for the industrial applications to replace the conventional process and to reduce the final product cost.