Damage assessment in plate-like structures using a two-stage method based on modal strain energy change and Jaya algorithm

The paper presents an effective two-stage approach based on modal strain energy change and Jaya algorithm for damage assessment in plate-like structures. In the first stage, a newly developed damage indicator, named as normalized modal strain energy-based damage index (nMSEBI), is proposed to help locate potential damage elements more effectively. After eliminating most of the healthy elements, the actual damaged sites and its extent in plate structure are determined in the second stage by minimizing an objective function, which is solved using Jaya algorithm. For finding a suitable objective function used in optimization process, two different objective functions are considered to examine their effects on the performance of the utilized optimization algorithm. The efficiency and accuracy of the proposed two-stage damage detection method are investigated by two numerical examples comprising a concrete plate and a four-layer (0°/90°/90°/0°) laminated composite plate with multiple damage locations. All of the obtained results indicate that even under measurement noise, the proposed method can identify the actual damage sites and estimate the extent of damage with high precision. In addition, the numerical results also show that the computational cost of the optimization process using the objective function based on modal flexibility change is much lower than that using the objective function based on mode shapes change.     

Fast simulation-driven design of microwave structures using improved variable-fidelity optimization technique

An improved variable-fidelity optimization algorithm for the simulation-driven design of microwave structures is presented. It exploits a set of electromagnetic-based models of increasing discretization density. These models are sequentially optimized with the optimum of the ‘coarser’ model being the initial design for the ‘finer’ one. The found optimum is further refined using a response surface approximation model constructed from the coarse-discretization simulation data. In this work, the computational efficiency of the variable-fidelity algorithm is enhanced by employing a novel algorithm for optimizing the coarse-discretization models. This allows reduction of the overall design time by up to 50% compared to the previous version. The presented technique is particularly suitable for problems where simulation-driven design is the only option, for example, ultra wideband and dielectric resonator antennas. Operation of the presented approach is demonstrated using two examples of antennas and a microstrip filter. In all cases, the optimal design is obtained at a low computational cost corresponding to a few high-fidelity simulations of the structure.     

A scaled boundary finite element based explicit topology optimization approach for three-dimensional structures

This article proposes an efficient approach for solving three-dimensional (3D) topology optimization problem. In this approach, the number of design variables in optimization as well as the number of degrees of freedom in structural response analysis can be reduced significantly. This is accomplished through the use of scaled boundary finite element method (SBFEM) for structural analysis under the moving morphable component (MMC)-based topology optimization framework. In the proposed method, accurate response analysis in the boundary region dictates the accuracy of the entire analysis. In this regard, an adaptive refinement scheme is developed where the refined mesh is only used in the boundary region while relating coarse mesh is used away from the boundary. Numerical examples demonstrate that the computational efficiency of 3D topology optimization can be improved effectively by the proposed approach.     

Topology optimization of structures under variable loading using a damage superposition approach

We present an original algorithm and accompanying mathematical formulation for topology optimization of structures that can sustain material damage and are subject to multiple load cases with varying configurations. Damage accumulation is simulated using a coupled, non‐linear brittle damage model. The structures are optimized for minimum mass subject to stiffness constraints defined as the compliance evaluated at the end of each loading sequence. To achieve robustness of the optimized structures, the respective damage fields caused by each individual load case are computed and combined using superposition to simulate a worst‐case damage field. All load cases are then run a second time using the worst‐case damage distribution as a starting point. In this way, one effectively accounts for the spectrum of possible load sequences to which the structure may be subjected. Results from this method are compared with an exhaustive, brute‐force approach in which all non‐repeating load sequences are analyzed individually. For each method, the corresponding sensitivities are derived and implemented analytically using a path‐dependent adjoint method. The two approaches are implemented on a series of numerical examples, which demonstrate that the superposition method produces structures that are as robust as those obtained using the exhaustive method but require significantly less computational effort. Copyright © 2014 John Wiley & Sons, Ltd.     

Topology optimization using bi-directional evolutionary structural optimization based on the element-free Galerkin method

In this article, the bi-directional evolutionary structural optimization (BESO) method based on the element-free Galerkin (EFG) method is presented for topology optimization of continuum structures. The mathematical formulation of the topology optimization is developed considering the nodal strain energy as the design variable and the minimization of compliance as the objective function. The EFG method is used to derive the shape functions using the moving least squares approximation. The essential boundary conditions are enforced by the method of Lagrange multipliers. Several topology optimization problems are presented to show the effectiveness of the proposed method. Many issues related to topology optimization of continuum structures, such as chequerboard patterns and mesh dependency, are studied in the examples.     

基于云计算的框架结构参数并行辨识算法

基于时域振动响应的结构损伤检测方法因其便于实现在线监测受到了越来越多的关注。该文回顾了两种利用时域响应相关函数建立的结构特征向量(即内积向量及互相关函数幅值向量)及其对应的损伤检测方法。为了从时域响应相关函数中提取更多的结构健康信息,通过利用不同的结构响应组合,将上述两种结构特征向量扩展到了多种结构特征向量,并进一步采用数据融合理论,提出了检测精度更高的结构损伤检测方法。针对8层框架结构损伤检测的试验研究结果表明,该文方法可以对框架结构上的微小损伤进行定位。     

Enhancing particle swarm optimization algorithm using two new strategies for optimizing design of truss structures

This work develops an augmented particle swarm optimization (AugPSO) algorithm using two new strategies,: boundary-shifting and particle-position-resetting. The purpose of the algorithm is to optimize the design of truss structures. Inspired by a heuristic, the boundary-shifting approach forces particles to move to the boundary between feasible and infeasible regions in order to increase the convergence rate in searching. The purpose of the particle-position-resetting approach, motivated by mutation scheme in genetic algorithms (GAs), is to increase the diversity of particles and to prevent the solution of particles from falling into local minima. The performance of the AugPSO algorithm was tested on four benchmark truss design problems involving 10, 25, 72 and 120 bars. The convergence rates and final solutions achieved were compared among the simple PSO, the PSO with passive congregation (PSOPC) and the AugPSO algorithms. The numerical results indicate that the new AugPSO algorithm outperforms the simple PSO and PSOPC algorithms. The AugPSO achieved a new and superior optimal solution to the 120-bar truss design problem. Numerical analyses showed that the AugPSO algorithm is more robust than the PSO and PSOPC algorithms.     

Topology optimization of multiscale elastoviscoplastic structures

This paper extends current concepts of topology optimization to the design of structures made of nonlinear microheterogeneous materials. The objective is to maximize the macroscopic structural stiffness for a prescribed material volume usage while accounting for the nonlinearity and the microstructure of the material. The resulting design problem considers two scales: the macroscopic scale at which the optimization is performed and the microscopic scale at which the material heterogeneities and the nonlinearities are observed. The topology optimization at the macroscopic scale is performed by means of the bi‐directional evolutionary structural optimization method. The solution of the macroscopic boundary value problem requires as inputs the effective constitutive response with full consideration of the microstructure. While computational homogenization methods such as the FE² method could be used to solve the nonlinear multiscale problem, the associated numerical expense (CPU time and memory) is highly unacceptable. In order to regain the computational feasibility of the computational scale transition, a recent model reduction technique of the authors is employed: the potential‐based reduced basis model order reduction with graphics processing unit acceleration. Numerical examples show the efficiency of the resulting nonlinear two‐scale designs. The impact of different load amplitudes on the design is examined. Copyright © 2015 John Wiley & Sons, Ltd.     

A study on the application of material selection optimization approach for structural-acoustic optimization

Issues of application of the material selection optimization approach for structural-acoustic optimization is investigated herein. By introducing the stacking sequence hypothesis of metal material, the mechanical properties parameters and plies’ numbers of the metal material or composite material are defined as design variables; the mathematical formulation about material selection optimization approach is established. Finally, a hexahedral box structure is taken as an example, and the material selection optimization is conducted. By introducing genetic algorithm (GA), the optimization problem is solved. The numerical example shows the effectiveness of the proposed stacking sequence hypothesis of metal material.     

Process identification using a new component analysis model and particle swarm optimization

A novel component analysis model is proposed to identify the mixed process signals which are frequently encountered in the statistical process control (SPC) and engineering process control (EPC) practice. Based upon one of existing state-of-the-art evolutionary algorithms, called particle swarm optimization (PSO), the proposed model provides a solution (i.e., demixing matrix) by maximizing the determinant of the corresponding second-order moment (variance–covariance) matrix of the reconstructed signals. Then, the estimated demixing matrix is used to separate mixed signals arising from several original process signals. The process signals considered in this paper include inconsistent variance series, autoregressive (AR) series, step change, and Gaussian noises in the process data. In practice, most of industrial manufacturing processes can be well characterized by a mixture of these four types of data. By following the proposed model, the blind signal separation framework can be cast into a nonlinear constrained optimization problem, where only the demixing matrix appears as unknown. Several illustrative examples involving linear mixtures of the process signals with different statistical characteristics are demonstrated to justify the new component analysis model.     

使用支持向量机的微处理器验证向量优化方法

为了解决微处理器仿真验证中随机验证向量质量不高的问题,提出了一种基于支持向量机(SVM)的验证向量优化方法。该方法将已仿真运行的验证向量及其覆盖率信息作为支持向量机的样本进行有监督学习,得到验证向量关于功能覆盖点的分类器。利用训练后的分类器对于新产生的验证向量进行预测,并丢弃预测中不能提高覆盖率的冗余验证向量。实验数据表明该方法能准确地过滤冗余验证向量,提高仿真运行的验证向量的质量。和完全随机的验证向量生成方法相比,该方法达到相同的功能覆盖率仅需要前者1/3的验证向量。     

An uncertain multidisciplinary design optimization method using interval convex models

This article proposes an uncertain multi-objective multidisciplinary design optimization methodology, which employs the interval model to represent the uncertainties of uncertain-but-bounded parameters. The interval number programming method is applied to transform each uncertain objective function into two deterministic objective functions, and a satisfaction degree of intervals is used to convert both the uncertain inequality and equality constraints to deterministic inequality constraints. In doing so, an unconstrained deterministic optimization problem will be constructed in association with the penalty function method. The design will be finally formulated as a nested three-loop optimization, a class of highly challenging problems in the area of engineering design optimization. An advanced hierarchical optimization scheme is developed to solve the proposed optimization problem based on the multidisciplinary feasible strategy, which is a well-studied method able to reduce the dimensions of multidisciplinary design optimization problems by using the design variables as independent optimization variables. In the hierarchical optimization system, the non-dominated sorting genetic algorithm II, sequential quadratic programming method and Gauss–Seidel iterative approach are applied to the outer, middle and inner loops of the optimization problem, respectively. Typical numerical examples are used to demonstrate the effectiveness of the proposed methodology.     

Environmental optimization of chromium recovery from tannery sludge using a life cycle assessment approach

Life cycle assessment (LCA) was used to evaluate the environmental impact of an oxidative chromium recovery method from tannery sludge, in comparison with the usual landfilling process. Three improvement options (water reduction, byproduct use and anaerobic sludge digestion) were considered. The results showed that the proposed chromium recovery process would be better environmentally than conventional landfilling in all the evaluated impact categories if the amount of chromium recovered was 43 kg per ton of sludge. This amount could be recovered if the chromium concentration was about 20 times higher than that considered in this study. Alternatively, a lower chromium concentration would produce a better result if the recovery method was optimized and implemented at industrial rather than laboratory scale, and if more accurate data were provided on environmental credits for avoiding the chromium production process. Thus, the recovery method is environmentally beneficial when tannery sludge contains a chromium concentration of about 100,000 ppm. According to the literature, such concentrations are not unusual. The results could serve as the basis for further environmental improvements in chromium recovery and tannery sludge management and should be used in decision-making processes, especially for end-of-pipe treatments.     

Spent potliner treatment process optimization using a MADS algorithm

In this paper, the general problem of chemical process optimization defined by a computer simulation is formulated. It is generally a nonlinear, non-convex, non-differentiable optimization problem over a disconnected set. A brief overview of popular optimization methods from the chemical engineering literature is presented. The recent mesh adaptive direct search (MADS) algorithm is detailed. It is a direct search algorithm, so it uses only function values and does not compute or approximate derivatives. This is useful when the functions are noisy, costly or undefined at some points, or when derivatives are unavailable or unusable. In this work, the MADS algorithm is used to optimize a spent potliners (toxic wastes from aluminum production) treatment process. In comparison with the best previously known objective function value, a 37% reduction is obtained even if the model failed to return a value 43% of the time.     

基于材料选型优化的金属-复合材料组合结构-声辐射优化

基于金属铺层假设,以结构材料属性和材料铺层数量为设计变量,建立了基于金属-复合材料的材料选型结构-声辐射优化设计模型。以钢-纤维增强复合材料组合结构为例,开展了多设计变量、多约束条件的结构-声辐射优化研究。最后,采用遗传算法进行求解,实现了结构材料的转换。数值算例表明,通过金属-复合材料组合结构的材料选型优化,可以有效降低钢-纤维增强复合材料结构的振动和声辐射。文中的研究方法为结构材料的分布优化提供了一种可行有效的思路。     

Reliability assessment of a tanker using the model correction factor method based on the IACS-CSR requirement for hull girder ultimate strength

The model correction factor method (MCFM) is adopted to assess the reliability of a Suezmax oil tanker considering the ultimate vertical bending moment capacity of the hull girder as a limit state. The approach uses the incremental iterative method proposed by the International Association of Classification Societies (IACS) Common Structural Rules (CSR) to evaluate the hull girder ultimate strength as a response model that is calibrated iteratively at the design points calculated by the First Order Reliability method (FORM) by means of advanced non-linear Finite Element Analyses (FEA). The considered loads are the still water and the wave-induced bending moments in a typical seagoing operational condition of the oil tanker in the full load and ballast load conditions. First, the predictions of the hull girder bending capacities calculated by the IACS-CSR method and by non-linear FEA are compared and then the efficiency of the MCFM for hull girder reliability problems is illustrated. It is shown that using semi-empirical response models, which include the important mechanical features with respect to the bending capacity of the ship hull girder, the reliability evaluation can be performed with a limited number of non-linear FEAs (less than 10) promoting the application of advanced response and reliability methods to complex structures.     

Predicting microdroplet force response using a multiscale modeling approach

An axisymmetric microscale finite element model of a microdroplet test specimen is developed where the structural response of the fiber–droplet interface is accounted for by surface-based cohesive behavior. In this study, the interface cohesive response is estimated using a nanoscale interface finite element model that explicitly includes the effects of fiber surface topography and the interphase region. The interphase behavior in the nanoscale interface model is calibrated using indirect experimental data. Once calibrated, the fiber surface topography in the nanoscale interface model is modified in order to estimate the parameters defining the surface-based cohesive behavior of similar fiber–matrix systems with different fiber topography. The effect of altering the fiber topography on the force response of the microdroplet test can then be predicted by the microdroplet FE model. Comparing the simulation results with experimental data from the literature shows that this multiscale modeling approach gives accurate predictions for the interfacial shear stress.     

Investigation of nickel(II) biosorption on Enteromorpha prolifera: optimization using response surface analysis

In this study, the biosorption of nickel(II) ions on Enteromorpha prolifera, a green algae, was investigated in a batch system. The single and combined effects of operating parameters such as initial pH, temperature, initial metal ion concentration and biosorbent concentration on the biosorption of nickel(II) ions on E. prolifera were analyzed using response surface methodology (RSM). The optimum biosorption conditions were determined as initial pH 4.3, temperature 27 degrees C, biosorbent concentration 1.2 g/L and initial nickel(II) ion concentration 100 mg/L. At optimum biosorption conditions, the biosorption capacity of E. prolifera for nickel(II) ions was found to be 36.8 mg/g after 120 min biosorption. The Langmuir and Freundlich isotherm models were applied to the equilibrium data and defined very well both isotherm models. The monolayer coverage capacity of E. prolifera for nickel(II) ions was found as 65.7 mg/g. In order to examine the rate limiting step of nickel(II) biosorption, such as the mass transfer and chemical reaction kinetics, the intraparticle diffusion model, external diffusion model and the pseudo second order kinetic model were tested with the experimental data. It was found that for both contributes to the actual biosorption process. The pseudo second order kinetic model described the nickel(II) biosorption process with a good fitting.     

A novel approach using Dynamic Social Impact Theory for optimization of impedance-Tongue (iTongue)

This paper presents a novel multiobjective wrapper approach using Dynamic Social Impact Theory based optimizer (SITO). A Fuzzy Inference System in conjunction with support vector machines classifier has been used for the optimization of an impedance-Tongue for the classification of samples collected from single batch production of Kangra orthodox black tea. Impedance spectra of the tea samples have been measured in the range of 20 Hz to 1 MHz using a two electrode setup employing platinum and gold electrodes. The proposed approach has been compared, for its robustness and validity using various intra and inter measures, against Genetic Algorithm and binary Particle Swarm Optimization. Feature subset selection methods based on the first and second order statistics have also been employed for comparisons. The proposed approach outperforms the Genetic Algorithm and binary Particle Swarm Optimization.