Radial basis functions and level set method for structural topology optimization

Level set methods have become an attractive design tool in shape and topology optimization for obtaining lighter and more efficient structures. In this paper, the popular radial basis functions (RBFs) in scattered data fitting and function approximation are incorporated into the conventional level set methods to construct a more efficient approach for structural topology optimization. RBF implicit modelling with multiquadric (MQ) splines is developed to define the implicit level set function with a high level of accuracy and smoothness. A RBF–level set optimization method is proposed to transform the Hamilton–Jacobi partial differential equation (PDE) into a system of ordinary differential equations (ODEs) over the entire design domain by using a collocation formulation of the method of lines. With the mathematical convenience, the original time dependent initial value problem is changed to an interpolation problem for the initial values of the generalized expansion coefficients. A physically meaningful and efficient extension velocity method is presented to avoid possible problems without reinitialization in the level set methods. The proposed method is implemented in the framework of minimum compliance design that has been extensively studied in topology optimization and its efficiency and accuracy over the conventional level set methods are highlighted. Numerical examples show the success of the present RBF–level set method in the accuracy, convergence speed and insensitivity to initial designs in topology optimization of two‐dimensional (2D) structures. It is suggested that the introduction of the radial basis functions to the level set methods can be promising in structural topology optimization. Copyright © 2005 John Wiley & Sons, Ltd.     

Adaptive improved response surface method for reliability-based design optimization

Reliability-based design optimization (RBDO) requires the evaluation of probabilistic constraints (or reliability), which can be very time consuming. Therefore, a practical solution for efficient reliability analysis is needed. The response surface method (RSM) and dimension reduction (DR) are two well-known approximation methods that construct the probabilistic limit state functions for reliability analysis. This article proposes a new RSM-based approximation approach, named the adaptive improved response surface method (AIRSM), which uses the moving least-squares method in conjunction with a new weight function. AIRSM is tested with two simplified designs of experiments: saturated design and central composite design. Its performance on reliability analysis is compared with DR in terms of efficiency and accuracy in multiple RBDO test problems.     

NEW DEVELOPMENTS IN STRUCTURAL OPTIMIZATION USING ADAPTIVE MESH REFINEMENT AND MULTIPOINT APPROXIMATIONS

Combining Shape Optimization (SO) with Adaptive Mesh Refinement (AMR) potentially offers a higher accuracy and higher computational efficiency, especially if the applied target error for AMR is reduced in the course of the optimization process. The disadvantage of that approach is that the rate of convergence of the corresponding optimization processes can be significantly lower as compared to processes which apply a fixed target error for AMR. In the present paper the so-called Multipoint Approximation Method (MAM) is used as a basis for SO in conjunction with AMR. Several techniques for improvement of the rates of convergence are presented and investigated. Firstly, alternative algorithms for determining the approximation functions using a weighted least squares method are investigated. The focus is on weights which depend on the discretization errors. Secondly, different strategies for moving and resizing the search sub-regions in the space of design variables are presented. The proposed methods are illustrated by means of several optimization problems in which the effect of AMR with changing discretization errors is modelled by artificially introduced numerical noise.     

A comparative study of the scalability of alternative metamodelling techniques

Metamodels, also known as surrogate models, can be used in place of computationally expensive simulation models to increase computational efficiency for the purposes of design optimization or design space exploration. The accuracy of these metamodels varies with the scale and complexity of the underlying model. In this article, three metamodelling methods are evaluated with respect to their capabilities for modelling high-dimensional, nonlinear, multimodal functions. Methods analyzed include kriging, radial basis functions, and support vector regression. Each metamodelling technique is used to model a set of single output functions with dimensionality ranging from fifteen to fifty independent variables and modality ranging from one to ten local maxima. The number of points used to train the models is increased until a predetermined error threshold is met. Results show that kriging metamodels perform most consistently across a variety of functions, although radial basis functions and support vector regression are very competitive for highly multimodal functions and functions with large local gradients, respectively. Support vector regression metamodels consistently offer the shortest build and prediction times when applied to large scale multimodal problems.     

A local approximation based multi-objective optimization algorithm with applications

Optimization techniques are useful tools to the design of complex systems. Especially in case of multiple conflicting performance indexes, the knowledge of the tradeoffs by means of Pareto optimality can help the designer to achieve the best solution. Due to the increasing power of the computing tools, more and more accurate and time consuming models are used. In this case, the Pareto set computation can be a hard task (the Pareto set can be nonconvex, nonlinearities and discontinuities can occur) and the efficiency and the accuracy become crucial features for an optimization algorithm. In this paper an optimization algorithm based on local approximation of the objective and constraints functions is presented and tested with some well known test functions. The optimal design of the suspension system of a ground vehicle is performed by the new algorithm in order to reach the best tradeoff by means of road holding, comfort, working space and cornering behavior. The numerical results show that the proposed algorithm has good accuracy and high efficiency if compared to some widely used methods. The results are explained providing some general observations on the efficiency of local approximation based algorithm an other well known algorithms.     

Interval-Based Uncertain Multi-Objective Optimization Design of Vehicle Crashworthiness

In this paper, an uncertain multi-objective optimization method is suggested to deal with crashworthiness design problem of vehicle, in which the uncertainties of the parameters are described by intervals. Considering both lightweight and safety performance, structural weight and peak acceleration are selected as objectives. The occupant distance is treated as constraint. Based on interval number programming method, the uncertain optimization problem is transformed into a deterministic optimization problem. The approximation models are constructed for objective functions and constraint based on Latin Hypercube Design (LHD). Thus, the interval number programming method is combined with the approximation model to solve the uncertain optimization problem of vehicle crashworthiness efficiently. The present method is applied to two practical full frontal impact (FFI) problems.     

结构可靠性优化求解的解耦融合策略

在工程结构的可靠性优化过程中,求解的效率和精度是优化方法的关键。该文提出一种针对解耦优化的融合策略。所提方法在优化迭代解耦所用的失效概率函数为前几次迭代设计点构建的局部失效概率函数的加权融合形式。在对原可靠性优化问题进行解耦后,结合序列近似优化方法进行迭代求解。相比于常规的仅使用当次局部建立的失效概率函数而言,所提融合策略最大限度利用了各次迭代中产生的信息用于优化解耦求解,能够提高失效概率函数的近似精度,从而间接达到减少迭代次数和计算量的目的。最后给出了屋架和十杆结构的可靠性优化算例,验证该文方法的正确性和可行性。     

A ‘best points’ interpolation method for efficient approximation of parametrized functions

We present an interpolation method for efficient approximation of parametrized functions. The method recognizes and exploits the low‐dimensional manifold structure of the parametrized functions to provide good approximation. Basic ingredients include a specific problem‐dependent basis set defining a low‐dimensional representation of the parametrized functions, and a set of ‘best interpolation points’ capturing the spatial‐parameter variation of the parametrized functions. The best interpolation points are defined as solution of a least‐squares minimization problem which can be solved efficiently using standard optimization algorithms. The approximation is then determined from the basis set and the best interpolation points through an inexpensive and stable interpolation procedure. In addition, an a posteriori error estimator is introduced to quantify the approximation error and requires little additional cost. Numerical results are presented to demonstrate the accuracy and efficiency of the method. Copyright © 2007 John Wiley & Sons, Ltd.     

Improving variable-fidelity modelling by exploring global design space and radial basis function networks for aerofoil design

The global variable-fidelity modelling (GVFM) method presented in this article extends the original variable-complexity modelling (VCM) algorithm that uses a low-fidelity and scaling function to approximate a high-fidelity function for efficiently solving design-optimization problems. GVFM uses the design of experiments to sample values of high- and low-fidelity functions to explore global design space and to initialize a scaling function using the radial basis function (RBF) network. This approach makes it possible to remove high-fidelity-gradient evaluation from the process, which makes GVFM more efficient than VCM for high-dimensional design problems. The proposed algorithm converges with 65% fewer high-fidelity function calls for a one-dimensional problem than VCM and approximately 80% fewer for a two-dimensional numerical problem. The GVFM method is applied for the design optimization of transonic and subsonic aerofoils. Both aerofoil design problems show design improvement with a reasonable number of high- and low-fidelity function evaluations.     

Reliability-based design optimization using a generalized subset simulation method and posterior approximation

The evaluation of the probabilistic constraints in reliability-based design optimization (RBDO) problems has always been significant and challenging work, which strongly affects the performance of RBDO methods. This article deals with RBDO problems using a recently developed generalized subset simulation (GSS) method and a posterior approximation approach. The posterior approximation approach is used to transform all the probabilistic constraints into ordinary constraints as in deterministic optimization. The assessment of multiple failure probabilities required by the posterior approximation approach is achieved by GSS in a single run at all supporting points, which are selected by a proper experimental design scheme combining Sobol’ sequences and Bucher’s design. Sequentially, the transformed deterministic design optimization problem can be solved by optimization algorithms, for example, the sequential quadratic programming method. Three optimization problems are used to demonstrate the efficiency and accuracy of the proposed method.     

A surrogate assisted evolutionary optimization method with application to the transonic airfoil design

A multi-layer perceptron neural network (NN) method is used for efficient estimation of the expensive objective functions in the evolutionary optimization with the genetic algorithm (GA). The estimation capability of the NN is improved by dynamic retraining using the data from successive generations. In addition, the normal distribution of the training data variables is used to determine well-trained parts of the design space for the NN approximation. The efficiency of the method is demonstrated by two transonic airfoil design problems considering inviscid and viscous flow solvers. Results are compared with those of the simple GA and an alternative surrogate method. The total number of flow solver calls is reduced by about 40% using this fitness approximation technique, which in turn reduces the total computational time without influencing the convergence rate of the optimization algorithm. The accuracy of the NN estimation is considerably improved using the normal distribution approach compared with the alternative method.     

基于物理规划的Stewart平台多目标优化

应用物理规划方法对500米口径球面射电望远镜(FAST)精调 Stewart 平台进行了多目标优化设计.根据 Stewart 平台的设计准则,将 Stewart 平台的运动精度、重量和工作效率作为目标函数,构造了相应的以目标函数为变量的偏好函数,建立了基于物理规划的多目标优化模型,采用遗传算法对该优化问题进行求解.结果表明,物理规划避免了基于权重的多目标优化方法中权系数难以确定的问题,有效地给出了优化问题的 Pareto 解.     

Adaptive gradient-assisted robust design optimization under interval uncertainty

Robust optimization techniques attempt to find a solution that is both optimum and relatively insensitive to input uncertainty. In general, these techniques are computationally more expensive than their deterministic counterparts. In this article two new robust optimization methods are presented. The first method is called gradient-assisted robust optimization (GARO). In GARO, a robust optimization problem is first converted to a deterministic one by using a gradient-based approximation technique. After solving this deterministic problem, the solution robustness and the accuracy of the approximation are checked. If the accuracy meets a threshold, a robust optimum solution is found; otherwise, the approximation is adaptively modified until the threshold is met and a solution, if it exists, is obtained. The second method is a faster version of GARO called quasi-concave gradient-assisted robust optimization (QC-GARO). QC-GARO is for problems with quasi-concave objective and constraint functions. The difference between GARO and QC-GARO is in the way that they check the approximation accuracy. Both GARO and QC-GARO methods are applied to a set of six engineering design test problems and the results are compared with a few related previous methods. It was found that, compared to the methods considered, GARO could solve all test problems but with a higher computational effort compared to QC-GARO. However, QC-GARO was computationally much faster when it was able to solve the problems.     

Efficient reliability-based design with second order approximations

Sequential optimization and reliability analysis (SORA) is an efficient approach to reliability-based design (RBD). It decouples the double-loop structure of RBD into a serial cycle of deterministic optimization and reliability analysis. The first order approximation is used in SORA for reliability analysis owing to its good balance between accuracy and efficiency. However, it may result in a large error when a constraint function is highly nonlinear. This study proposes a new numerical method so that second order approximations for the reliability analysis can be used for higher accuracy. To minimize the increased computational cost due to second order approximations, this study also develops an efficient algorithm for searching for an equivalent reliability index with the help of the saddlepoint approximation. The efficiency and accuracy of the proposed method are verified through numerical examples.     

Multi-objective system reliability-based optimization method for design of a fully parametric concept car body

This article investigates multi-objective optimization under reliability constraints with applications in vehicle structural design. To improve computational efficiency, an improved multi-objective system reliability-based design optimization (MOSRBDO) method is developed, and used to explore the lightweight and high-performance design of a concept car body under uncertainty. A parametric model knowledge base is established, followed by the construction of a fully parametric concept car body of a multi-purpose vehicle (FPCCB-MPV) based on the knowledge base. The structural shape, gauge and topology optimization are then designed on the basis of FPCCB-MPV. The numerical implementation of MOSRBDO employs the double-loop method with design optimization in the outer loop and system reliability analysis in the inner loop. Multi-objective particle swarm optimization is used as the outer loop optimization solver. An improved multi-modal radial-based importance sampling (MRBIS) method is utilized as the system reliability solver for multi-constraint analysis in the inner loop. The accuracy and efficiency of the MRBIS method are demonstrated on three widely used test problems. In conclusion, MOSRBDO has been successfully applied for the design of a full parametric concept car body. The results show that the improved MOSRBDO method is more effective and efficient than the traditional MOSRBDO while achieving the same accuracy, and that the optimized body-in-white structure signifies a noticeable improvement from the baseline model.     

基于差距映射的变可信度近似模型构建方法

为缓解复杂工程产品设计优化中计算复杂度和计算精度之间的矛盾,结合最小二乘支持向量回归（least squares support vector regression，LSSVR）模型，提出一种基于差距映射的变可信度近似模型构建方法，即最小二乘支持向量回归差距映射（LSSVR with difference mapping framework，DMF-LSSVR）方法，以实现小样本条件下高精度近似模型的构建，并通过工程实例验证该方法的有效性。工程实例结果显示所提出的方法具有较高的预测精度,可为复杂工程产品的设计优化提供理论基础。     

Implementation of Discrete Capability into the Enhanced Multipoint Approximation Method for Solving Mixed Integer-Continuous Optimization Problems

Multipoint approximation method (MAM) focuses on the development of metamodels for the objective and constraint functions in solving a mid-range optimization problem within a trust region. To develop an optimization technique applicable to mixed integer-continuous design optimization problems in which the objective and constraint functions are computationally expensive and could be impossible to evaluate at some combinations of design variables, a simple and efficient algorithm, coordinate search, is implemented in the MAM. This discrete optimization capability is examined by the well established benchmark problem and its effectiveness is also evaluated as the discreteness interval for discrete design variables is increased from 0.2 to 1. Furthermore, an application to the optimization of a lattice composite fuselage structure where one of design variables (number of helical ribs) is integer is also presented to demonstrate the efficiency of this capability.     

水下圆柱壳声学代理模型优化设计

为了提高水下结构声学计算的效率,采用有限元软件进行水下圆柱壳结构声辐射分析,建立声学分析近似代理模型,给出壳体结构几何尺寸与水下声辐射特性的显式解析表达式,简化声学计算,建立高效的结构声学优化设计模型。利用拉丁超立方取样方法进行样本点的选取,分别采用多项式响应面法、Kriging函数和径向基函数法构造水下双层圆柱壳结构的声辐射代理模型。通过比较三种代理模型的拟合精度,选择三种代理模型建立双层圆柱壳结构水下声辐射优化设计模型,并采用Matlab进行尺寸优化,减轻了结构质量。该研究为水下结构声辐射预报和声学设计提供参考。     

一种自适应抽样的代理模型构建及其在复材结构优化中的应用

提出了基于一种自适应抽样和增强径向基插值的自适应代理模型方法，这种自适应抽样方法以确定适量的样本点数量和提高代理模型自适应能力为目的，使新增样本点位于设计空间的稀疏区域并确保所有的样本点均匀分布于设计空间以提高代理模型精度。标准误差用来判断代理模型的精度大小并决定是否对代理模型进行更新。一种条件随机抽样被用来对比本文的自适应抽样方法。经过对比验证发现，采用自适应抽样方法的代理模型精度比条件随机抽样方法的代理模型精度高。这种自适应代理模型结合多岛遗传算法被用来优化旋翼臂的碳纤维增强环氧树脂复合材料铺层角度使得旋翼臂的一阶模态频率最大。优化结果表明，不同的碳纤维增强环氧树脂复合材料铺层角度对旋翼臂的一阶模态频率值影响较大，优化结果获取了最优铺层角度，旋翼臂的一阶模态频率值被提高以远离激励频率而避免旋翼飞机的共振。     

Free vibration analysis of non-axisymmetric and axisymmetric structures by the dual reciprocity BEM

The dual reciprocity boundary element method (DR/BEM) is employed for the free vibration analysis of three-dimensional non-axisymmetric and axisymmetric elastic solids. The method uses the elastostatic fundamental solution in the integral formulation of elastodynamics and as a result of that, an inertial volume integral is created in addition to the boundary ones. This volume integral is transformed into a surface integral by invoking the reciprocal theorem and expanding of the displacement field into a series involving seven different approximation functions. The approximation functions used are local radial basis functions (RBFs) and are applied in combination (or not) with global basis functions (augmentation). All these functions are compared in terms of the accuracy they provide. The axisymmetric case is efficiently treated with the aid of the fast Fourier transform (FFT) algorithm in order to provide even non-axisymmetric vibration modes. Two representative numerical examples involving the determination of natural frequencies and modal shapes of an elastic cube and an elastic cylinder serve to investigate in detail the potentiality of each of the seven approximation functions tested to provide results of high accuracy and to reach useful practical conclusions.