Expedited simulation‐driven design optimization of UWB antennas by means of response features

In this work, a method for fast design optimization of broadband antennas is considered. The approach is based on a feature‐based optimization (FBO) concept where reflection characteristics of the structure at hand are formulated in terms of suitably defined feature points. Redefinition of the design problem allows for reducing the design optimization cost, because the dependence of feature point coordinates on antenna dimensions is less nonlinear than for the original frequency characteristics (here, S‐parameters). This results in faster convergence of the optimization algorithm. The cost of the design process is further reduced using variable‐fidelity electromagnetic (EM) simulation models. In case of UWB antennas, the feature points are defined, among others, as the levels of the reflection characteristic at its local in‐band maxima, as well as location of the frequency point which corresponds to acceptable reflection around the lower corner frequency within the UWB band. Also, the number of characteristic points depends on antenna topology and its dimensions. Performance of FBO‐based design optimization is demonstrated using two examples of planar UWB antennas. Moreover, the computational cost of the approach is compared with conventional optimization driven by a pattern search algorithm. Experimental validation of the numerical results is also provided.     

Robustness,generality and efficiency of optimization algorithms for practical applications

The design of most engineering systems is a complex and time-consuming process. In addition, the need to optimize such systems where multidisciplinary analysis and design procedures are required can cost additional human and computational resources if proper software and numerical algorithms are not used. Several computational aspects of optimization algorithms and the associated software must be considered while making comparative studies and selecting a suitable algorithm for practical applications. Several parameters, such asaccuracy, generality, robustness, efficiency and ease of use, must be considered while deciding the superiority of an optimization approach. Approximate algorithms without sound mathematical basis can be sometimes more efficient for a specific problem, but fail to satisfy other requirements. They are, therefore, not suitable for general applications. An objective of the paper is to emphasize the critical importance of the above-mentioned parameters in large scalestructural optimization and other applications. Theoretical foundations of two promising approaches, thesequential quadratic programming (SQP) andoptimality criteria (OC), are presented and analysed. Recent numerical experiments and experiences with the SQP algorithm satisfying these requirements are described by solving a variety of structural design problems. An important conclusion of the paper is that the SQP method with a potential constraint strategy is a better choice as compared to the currently prevalent mathematical programming (MP) and OC approaches.     

Topology optimization of cellular materials with periodic microstructure under stress constraints

Material design is a critical development area for industries dealing with lightweight construction. Trying to respond to these industrial needs topology optimization has been extended from structural optimization to the design of material microstructures to improve overall structural performance. Traditional formulations based on compliance and volume control result in stiffness-oriented optimal designs. However, strength-oriented designs are crucial in engineering practice. Topology optimization with stress control has been applied mainly to (macro) structures, but here it is applied to material microstructure design. Here, in the context of density-based topology optimization, well-established techniques and analyses are used to address known difficulties of stress control in optimization problems. A convergence analysis is performed and a density filtering technique is used to minimize the risk of results inaccuracy due to coarser finite element meshes associated with highly non-linear stress behavior. A stress-constraint relaxation technique (qp-approach) is applied to overcome the singularity phenomenon. Parallel computing is used to minimize the impact of the local nature of the stress constraints and the finite difference design sensitivities on the overall computational cost of the problem. Finally, several examples test the developed model showing its inherent difficulties.

     

Recent Advances on Topology Optimization of Multiscale Nonlinear Structures

Research on topology optimization mainly deals with the design of monoscale structures, which are usually made of homogeneous materials. Recent advances of multiscale structural modeling enables the consideration of microscale material heterogeneities and constituent nonlinearities when assessing the macroscale structural performance. However, due to the modeling complexity and the expensive computing requirement of multiscale modeling, there has been very limited research on topology optimization of multiscale nonlinear structures. This paper reviews firstly recent advances made by the authors on topology optimization of multiscale nonlinear structures, in particular techniques regarding to nonlinear topology optimization and computational homogenization (also known as FE²) are summarized. Then the conventional concurrent material and structure topology optimization design approaches are reviewed and compared with a recently proposed FE²-based design approach, which treats the microscale topology optimization process integrally as a generalized nonlinear constitutive behavior. In addition, discussions on the use of model reduction techniques is provided in regard to the prohibitive computational cost.     

Analysis and optimum design of fibre-reinforced composite structures

The optimal design of a carbon-fibre-reinforced plastic (CFRP) sandwich-like structure with aluminium (Al) webs is addressed. The material parameters are determined using tensile tests, whereafter the results of an analytical model, a numerical model and an experimental setup are compared. The analytical and numerical approximations are then used to optimize the structure in a multi-algorithm approach for minimum cost and maximum stiffness. The selected algorithm and approximation are motivated by their accuracy and computational efficiency. The CFRP plates are optimized with respect to ply arrangement, while the complete sandwich-like structure is optimized with respect to the combination of manufacturing and material cost. Design constraints on maximum deflection of the total structure, buckling of the CFRP composite plates, buckling of the Al webs, stress in the composite plates and stress in the Al stiffeners are included in the formulation. For the different phases in the optimization process, we use the recently proposed particle swarm optimization algorithm, a dynamic search technique and a continuous-discrete optimization technique .     

一种基于子结构分析的基本块重排算法

基本块重排是一类通过重新排布基本块在存储中的位置,以减少转移开销和指令cache失效率的编译优化技术.介绍了一种基于子结构分析的基本块重排算法.该算法通过统计剖视信息中控制流图的边执行频率,基于处理器转移预测策略构建转移开销模型和基本块排布收益模型.算法采用局部子结构优化的策略,改善基本块在存储中的排列顺序,从而减少转移开销,并提高指令cache的使用率,改善程序的总体性能.在UniCore处理器平台上进行了实验.实验结果表明,与其他基本块重排算法相比,该基本块重排算法在更大程度上减少转移开销和指令cache失效率的同时,其时间复杂度保持为O(n×logn).     

Optimization of sonic crystal attenuation properties by <Emphasis Type="Italic">ev-MOGA</Emphasis> multiobjective evolutionary algorithm

This paper shows a promising method for acoustic barrier design using a new acoustic material called Sonic Crystals (SCs). The configuration of these SCs is set as a multiobjective optimization problem which is very difficult to solve with conventional optimization techniques. The paper presents a new parallel implementation of a Multiobjective Evolutionary Algorithm called ev-MOGA (also known as ) and its application in a complex design problem. ev-MOGA algorithm has been designed to converge towards a reduced, but well distributed, representation of the Pareto Front (solution of the multiobjective optimization problem). The algorithm is presented in detail and its most important properties are discussed. To reduce the ev-MOGA computational cost when objective functions are substantial, a basic parallelization has been implemented on a distributed platform. Partially supported by MEC (Spanish Government) and FEDER funds: projects DPI2005-07835, MAT2006-03097 and Generalitat Valenciana (Spain) projects GV06/026, GV/2007/191.     

Multiscale Annealing for Grouping and Unsupervised Texture Segmentation

We derive real-time global optimization methods for several clustering optimization problems commonly used in unsupervised texture segmentation. Speed is achieved by exploiting the image neighborhood relation of features to design a multiscale optimization technique, while accuracy and global optimization properties are gained using annealing techniques. Coarse grained cost functions are derived for central and histogram-based clustering as well as several sparse proximity-based clustering methods. For optimization deterministic annealing algorithms are applied. Annealing schedule, coarse-to-fine optimization and the estimated number of segments are tightly coupled by a statistical convergence criterion derived from computational learning theory. The notion of optimization scale parametrized by a computational temperature is thus unified with the scales defined by the image resolution and the model or segment complexity. The algorithms are benchmarked on Brodatz-like microtexture mixtures. Results are presented for an autonomous robotics application. Extensions are discussed in the context of prestructuring large image databases valuable for fast and reliable image retrieval.     

School-based optimization for performance-based optimum seismic design of steel frames

The performance-based optimum seismic design of steel frames is one of the most complicated and computationally demanding structural optimization problems. Metaheuristic optimization methods have been successfully used for solving engineering design problems over the last three decades. A very recently developed metaheuristic method called school-based optimization (SBO) will be utilized in the performance-based optimum seismic design of steel frames for the first time in this study. The SBO actually is an improved/enhanced version of teaching–learning-based optimization (TLBO), which mimics the teaching and learning process in a class where learners interact with the teacher and between themselves. Ad hoc strategies are adopted in order to minimize the computational cost of SBO results. The objective of the optimization problem is to minimize the weight of steel frames under interstory drift and strength constraints. Three steel frames previously designed by different metaheuristic methods including particle swarm optimization, improved quantum particle swarm optimization, firefly and modified firefly algorithms, teaching–learning-based optimization, and JAYA algorithm are used as benchmark optimization examples to verify the efficiency and robustness of the present SBO algorithm. Optimization results are compared with those of other state-of-the-art metaheuristic algorithms in terms of minimum structural weight, convergence speed, and several statistical parameters. Remarkably, in all test problems, SBO finds lighter designs with less computational effort than the TLBO and other methods available in metaheuristic optimization literature.

     

Minimization of sound radiation from vibrating bi-material structures using topology optimization

Up to now, work on topological design optimization of vibrating structures against noise radiation has mainly addressed the maximization of eigenfrequencies and gaps between consecutive eigenfrequencies of free vibration, and minimization of the dynamic compliance subject to harmonic loading on the structure. In this paper, we deal with topology optimization problems formulated directly with the design objective of minimizing the sound power radiated from the structural surface(s) into a surrounding acoustic medium. Bi-material elastic continuum structures without material damping are considered. The structural vibrations are excited by time-harmonic external mechanical loading with prescribed frequency and amplitude. It is assumed that air is the acoustic medium and that a feedback coupling to the structure can be neglected. Certain conditions are assumed that imply that the sound power emission from the structural surface can be obtained in a simpler way than by solving Helmholz’ integral equation. Hereby, the computational cost of the structural-acoustical analysis is substantially reduced. Several numerical results are presented and discussed for plate- and pipe-like structures with different sets of boundary and loading conditions.     

Robust formulation for Reliability-based design optimization of structures

The reliability-based design optimization (RBDO) has been widely recognized as a powerful optimization tool under probabilistic constraints, through appropriate modeling of uncertainties. However, the drawback of RBDO is that it does not reflect the ability of the structure to comply with large data variations, unforeseen actions or deterioration mechanisms. On the other hand, the robust design optimization (RDO) reduces the variability of the structural performance, in addition to its mean level. However, RDO does not take direct advantage of the interaction between controllable (product design values) and noise variables (environmental random values), and the obtained results do not accurately indicate what parameter has the highest effect on the performance characteristics. The purpose of this paper is to propose a robust formulation for reliability-based design optimization (RRBDO) that combines the advantages of both optimization procedures and overcomes their weaknesses. The optimization model proposed overcomes the limitations of the existing models without compromising the reliability level, by considering a robust convex objective function and a performance variation constraint. The proposed formulation can consider the total cost of structures and can control structural parameter variations. It takes into account uncertainty and variability in the same mathematical formulation. A numerical solution procedure is also developed, for which results are analyzed and compared with RBDO for several examples of concrete and steel structures.     

A discrete post-processing method for structural optimization

This paper introduces a discrete variable post-processing method for structural design optimization. The motivation behind the method is to find a good discrete solution at manageable cost while the traditional discrete optimization algorithms are regarded as impractical for large-scale structural design problems. In this paper, the Design of Experiments (DOE) and Conservative Discrete Design (CDD) approaches have been proposed to deal with discrete variables with limited computational cost. Both methods work on the explicit approximate discreteproblem to explore the discrete design. These two approaches, together with engineering rounded-off methods, can be used to process discrete variables at any specified continuous design optimization cycle for structural design problems. Brief background and a theoretical discussion about these approaches are given in this paper. Finally, the methods that have been implemented in MSC.Nastran are demonstrated by academic and real engineering examples.     

On the prediction of topology and local properties for optimal trussed structures

A new formulation is presented for mathematical modelling to predict the distribution of material, material properties, and topology for the optimal design of trussed structures. The design problem is cast in a form to minimize a measure ofgeneralized compliance, which is calculated as a sum over the structure of weighted displacement. Member stiffnesses appear as design variables and, starting with a given ground structure, the solution predicts the optimal layout and distribution of stiffness. The isoperimetric constraint in the reformulated problem measures totalcost in generalized form, based on independently specified unit relative cost factors for each truss element. One or another form of optimal design is generated via a process where designated elements in the unit relative cost field are adjusted systematically at each cycle. The generalized cost feature provides as well for the introduction of certain technical constraints into the design problem, e.g. the facility to design around obstacles. Results for each cycle of an algorithm for computational treatment are identified as the solution to a properly posed optimization problem. Computational procedures are demonstrated by the prediction of optimal designs for a variety of truss problems in 2D.     

Automatic evaluation of move-limits in structural optimization

The two-point exponential approximation method was introduced by Fadelet al. (1990) and tested on structural optimization problems with stress and displacement constraints. It was subsequently tested on problems with frequency constraints (Sareenet al. 1991). The results reported in earlier papers showed reductions in the number of iterations needed to reach an optimum, and a smoother convergence towards that optimum. The method, which consists in correcting Taylor series approximations using previous design history, is used in the present paper to automatically determine move-limits. Move-limits are the allowable changes in design variables during the optimization of the approximate problem. The exponents, computed in the two-point exponential approximation by matching slopes between two design iterations, are used as a measure of non-linearity of the objective function and constraints with respect to each of the design variables. The relationships between the move-limits and the exponents are established and individual move-limits are computed and applied to each design variable. The method is applied to two classical structural optimization examples. It provides the engineer with more flexibility when choosing the move-limits and typically converges in less iterations.     

Structural shape optimization using Cartesian grids and automatic <Emphasis Type="Italic">h</Emphasis>-adaptive mesh projection

We present a novel approach to 3D structural shape optimization that leans on an Immersed Boundary Method. A boundary tracking strategy based on evaluating the intersections between a fixed Cartesian grid and the evolving geometry sorts elements as internal, external and intersected. The integration procedure used by the NURBS-Enhanced Finite Element Method accurately accounts for the nonconformity between the fixed embedding discretization and the evolving structural shape, avoiding the creation of a boundary-fitted mesh for each design iteration, yielding in very efficient mesh generation process. A Cartesian hierarchical data structure improves the efficiency of the analyzes, allowing for trivial data sharing between similar entities or for an optimal reordering of the matrices for the solution of the system of equations, among other benefits. Shape optimization requires the sufficiently accurate structural analysis of a large number of different designs, presenting the computational cost for each design as a critical issue. The information required to create 3D Cartesian h-adapted mesh for new geometries is projected from previously analyzed geometries using shape sensitivity results. Then, the refinement criterion permits one to directly build h-adapted mesh on the new designs with a specified and controlled error level. Several examples are presented to show how the techniques here proposed considerably improve the computational efficiency of the optimization process.     

Integration of design and manufacturing for structural shape optimization

This paper presents an integrated design and manufacturing approach that supports shape optimization of structural components. The approach starts from a primitive concept stage, where boundary and loading conditions of the structural component are given to the designer. Topology optimization is conducted for an initial structural layout. The discretized structural layout is smoothed using parametric B-Spline surfaces. The B-Spline surfaces are imported into a CAD system to construct parametric solid models for shape optimization. Virtual manufacturing (VM) techniques are employed to ensure that the optimized shape can be manufactured at a reasonable cost. The solid freeform fabrication (SFF) system fabricates physical prototypes of the structure for design verification. Finally, a computer numerical control (CNC) machine is employed to fabricate functional parts as well as mold or die for mass production of the structural component. The main contribution of the paper is incorporating manufacturing into the design process, where manufacturing cost is considered for design. In addition, the overall design process starts from a primitive stage and ends with functional parts. A 3D tracked vehicle roadarm is employed throughout this paper to illustrate the overall design process and various techniques involved.     

Computationally efficient design closure of miniaturized impedance matching transformers using response features

In this article, we describe a procedure for reliable and computationally efficient design optimization of miniaturized impedance matching transformers. Our approach exploits a concept of feature‐based optimization (FBO). According to FBO, considerable reduction of the computational cost of the simulation‐driven design process can be achieved—compared to conventional methods—by reformulating given performance requirements (typically, minimization of reflection over a frequency range of interest) in terms of suitably defined response features. For impedance transformer circuits, the feature points are defined as local maxima of the reflection characteristic, as well as the points defining the ?20 dB bandwidth. As the feature point coordinates (i.e., their frequencies and levels) depend on the geometry parameters of the structure in less nonlinear manner than the original responses (S‐parameters versus frequency), the optimization algorithm exhibits faster convergence. Further reduction of the optimization cost is obtained by utilization of variable‐fidelity electromagnetic simulations. Our technique is demonstrated using two design cases of an example miniaturized three‐section 50‐to‐100 ohm microstrip transformer. © 2016 Wiley Periodicals, Inc. Int J RF and Microwave CAE 26:396–401, 2016.     

The parameterized level set method for structural topology optimization with shape sensitivity constraint factor

In recent years, the parameterized level set method (PLSM) has attracted widespread attention for its good stability, high efficiency and the smooth result of topology optimization compared with the conventional level set method. In the PLSM, the radial basis functions (RBFs) are often used to perform interpolation fitting for the conventional level set equation, thereby transforming the iteratively updating partial differential equation (PDE) into ordinary differential equations (ODEs). Hence, the RBFs play a key role in improving efficiency, accuracy and stability of the numerical computation in the PLSM for structural topology optimization, which can describe the structural topology and its change in the optimization process. In particular, the compactly supported radial basis function (CS-RBF) has been widely used in the PLSM for structural topology optimization because it enjoys considerable advantages. In this work, based on the CS-RBF, we propose a PLSM for structural topology optimization by adding the shape sensitivity constraint factor to control the step length in the iterations while updating the design variables with the method of moving asymptote (MMA). With the shape sensitivity constraint factor, the updating step length is changeable and controllable in the iterative process of MMA algorithm so as to increase the optimization speed. Therefore, the efficiency and stability of structural topology optimization can be improved by this method. The feasibility and effectiveness of this method are demonstrated by several typical numerical examples involving topology optimization of single-material and multi-material structures.

     

Algorithms for Marketing-Mix Optimization

Algorithms for determining quality/cost/price tradeoffs in saturated markets are considered. A product is modeled by d real-valued qualities whose sum determines the unit cost of producing the product. This leads to the following optimization problem: given a set of n customers, each of whom has certain minimum quality requirements and a maximum price they are willing to pay, design a new product and select a price for that product in order to maximize the resulting profit.     

Multi-disciplinary constrained optimization of wind turbines

We describe procedures for the multi-disciplinary design optimization of wind turbines, where design parameters are optimized by maximizing a merit function, subjected to constraints that translate all relevant design requirements. Evaluation of merit function and constraints is performed by running simulations with a parametric high-fidelity aero-servo-elastic model; a detailed cross-sectional structural model is used for the minimum weight constrained sizing of the rotor blade. To reduce the computational cost, the multi-disciplinary optimization is performed by a multi-stage process that first alternates between an aerodynamic shape optimization step and a structural blade optimization one, and then combines the two to yield the final optimum solution. A complete design loop can be performed using the proposed algorithm using standard desktop computing hardware in one-two days. The design procedures are implemented in a computer program and demonstrated on the optimization of multi-MW horizontal axis wind turbines and on the design of an aero-elastically scaled wind tunnel model.