An improved differential evolution based on roulette wheel selection for shape and size optimization of truss structures with frequency constraints

Structural optimization with frequency constraints is well known as a highly nonlinear and complex optimization problem with many local optimum solutions. Therefore, to solve such problems effectively, designers need to use adequate optimization methods which can make a good balance between the computational cost and the quality of solutions. In this work, a novel differential evolution (DE) is proposed to solve the shape and size optimization problems for truss structures with frequency constraints. The proposed method, called ReDE, is a new version of the DE algorithm with two improvements. Firstly, the roulette wheel selection is employed to choose members for the mutation phase instead of random selection as in the conventional DE. Secondly, an elitist selection technique is applied to the selection phase instead of basic selection to improve the convergence speed of the method. The efficiency and reliability of the proposed method are demonstrated through five numerical examples. Numerical results reveal that the proposed algorithm outperforms many optimization methods in the literature.

     

Structural application of a shape optimization method based on a genetic algorithm

This paper presents a structural application of a shape optimization method based on a Genetic Algorithm (GA). The method produces a sequence of fixed-distance step-wise movements of the boundary nodes of a finite element model to derive optimal shapes from an arbitrary initial design space. The GA is used to find the optimal or near-optimal combination of boundary nodes to be moved for a given step movement. The GA uses both basic and advanced operators. For illustrative purposes, the method has been applied to structural shape-optimization. The shape-optimization methodology presented allows local optimization, where only crucial parts of a structure are optimized as well as global shape-optimization which involves finding the optimal shape of the structure as a whole for a given environment as described by its loading and freedom conditions. Material can be removed or added to reach the optimal shape. Two examples of structural shape optimization are included showing local and global optimization through material removal and addition. Received October 14, 1999     

Efficient and accurate derivatives for a software process chain in airfoil shape optimization

When using a Newton-based numerical algorithm to optimize the shape of an airfoil with respect to certain design parameters, a crucial ingredient is the derivative of the objective function with respect to the design parameters. In large-scale aerodynamics, this objective function is an output of a computational fluid dynamics program written in a high-level programming language such as Fortran or C. Numerical differentiation is commonly used to approximate derivatives but is subject to truncation and subtractive cancellation errors. For a particular two-dimensional airfoil, we instead apply automatic differentiation to compute accurate derivatives of the lift and drag coefficients with respect to geometric shape parameters. In automatic differentiation, a given program is transformed into another program capable of computing the original function together with its derivatives. In the problem at hand, the objective function consists of a sequence of programs: a MATLAB program followed by two Fortran 77 programs. It is shown how automatic differentiation is applied to a sequence of programs while keeping the computational complexity within reasonable limits. The derivatives computed by automatic differentiation are compared with approximations based on divided differences.     

汽驱生产过程蒸汽干度的软测量和优化操作

本文针对汽驱工业过程设计了一套优化监控系统。本系统建立了基于RBF神经网络的蒸汽干度软测量模型,并应用遗传算法对整个汽驱过程进行优化计算。系统具有良好的人机界面,各部分之间的数据通讯是基于OPC进行的。目前该监控系统已成功运行于辽河油田,实践证明,本系统具有较好的可靠性和稳定性。     

An efficient shape optimization technique of a two-dimensional body based on the boundary element method

An efficient approximation method to determine the optimum shape of the minimum weight of a body subjected to stress and displacement constraints is suggested by using the boundary element method. The objective function of weight is approximated to an expansion of a second-order Taylor series and the stress and displacement constraints to expansions of a first-order Taylor series, based on the boundary element sensitivity analysis at the current design point. The approximated subproblem is then solved by a linear complementary pivot method. Design variable reduction techniques of isoparametric interpolation and trigonometric series interpolation for the design boundary shape are also adopted for reducing the degrees of freedom of the design problems.     

Introducing a level-set based shape and topology optimization method for the wear of composite materials with geometric constraints

The wear of materials continues to be a limiting factor in the lifetime and performance of mechanical systems with sliding surfaces. As the demand for low wear materials grows so does the need for models and methods to systematically optimize tribological systems. Elastic foundation models offer a simplified framework to study the wear of multimaterial composites subject to abrasive sliding. Previously, the evolving wear profile has been shown to converge to a steady-state that is characterized by a time-independent elliptic equation. In this article, the steady-state formulation is generalized and integrated with shape optimization to improve the wear performance of bi-material composites. Both macroscopic structures and periodic material microstructures are considered. Several common tribological objectives for systems undergoing wear are identified and mathematically formalized with shape derivatives. These include (i) achieving a planar wear surface from multimaterial composites and (ii) minimizing the run-in volume of material lost before steady-state wear is achieved. A level-set based topology optimization algorithm that incorporates a novel constraint on the level-set function is presented. In particular, a new scheme is developed to update material interfaces; the scheme (i) conveniently enforces volume constraints at each iteration, (ii) controls the complexity of design features using perimeter penalization, and (iii) nucleates holes or inclusions with the topological gradient. The broad applicability of the proposed formulation for problems beyond wear is discussed, especially for problems where convenient control of the complexity of geometric features is desired.     

基于KNN的剩余油形态识别

对从实验中采集到的剩余油图像进行分析研究,可以为油藏后期开采提供理论依据。通过收集确定类型的剩余油特征数据作为样本集向量空间,对待分类剩余油特征数据进行归一化处理,之后求取欧氏距离。使用KNN(K近邻)分类方法近邻投票确定剩余油类别,可以较为快速准确地得到分类结果。     

Novel hybrid optimization algorithm using PSO and MADS for the trajectory estimation of a four track wheel skid-steered mobile robot

Several optimization algorithms, such as the particle swarm optimization (PSO), genetic algorithm (GA), and ant colony optimization, have previously been applied in order to reliably obtain more accurate trajectory estimation for mobile robot. However, these optimization algorithms can get easily trapped in local optima when solving a complex system, which has many local optima and many input variables. This paper proposes a novel hybrid optimization algorithm-based tuning of the extended Kalman filter, which involves the PSO and mesh adaptive direct search algorithms, prior to operation. As demonstrated by our experimental results, the advantages of the novel hybrid optimization algorithm resolve the limitations of other algorithms in the trajectory estimation of a four track wheel skid-steered mobile robot (4-TW SSMR).     

基于自适应差分进化的常压塔轻质油产量多目标优化

常压塔轻质油产量最大化是提高企业效益的重要途径之一.为了适应市场需求和价格变化,生产高需求与高价值的轻质油产品,提出一种基于自适应差分进化的常压塔轻质油产量多目标优化算法.该算法采用惩罚边界交叉法的分解方法,在种群变异阶段引入择优学习算子来改进传统变异算子随机选取个体或者单纯选取最好个体的随机性和盲目性,利用自适应策略逐渐改变交叉变异算子.将改进算法应用于3种测试函数和实际炼油厂常压塔轻质油产量优化,结果表明所提出的算法在测试函数上具有明显优势,并能有效提高常压塔轻质油产量,验证了所提算法的有效性.     

基于嵌入式Linux的车载终端的设计与实现

针对城市交通管理中车辆管理和调度困难的问题,提出了一种基于嵌入式Linux车辆监控管理系统的车载终端方案.方案采用ARM核心处理器、嵌入式Linux操作系统,并且利用GPRS无线数据传输技术及GPS卫星定位技术,实现了GPS定位信息的接收、处理及其GPRS网络数据传输等多种功能.文章重点介绍了车辆监控管理系统的工作原理及车载终端的硬件设计和软件实现.本方案终端具有安装简单、功能强大、极具实用性等特点.样机测试表明,本方案终端可以满足车辆监控管理系统的要求.     

Development of the Kansa method for solving seepage problems using a new algorithm for the shape parameter optimization

In this research, the Kansa or Multiquadric method (MQ) has been developed for solving the seepage problems in 2D and 3D arbitrary domains. This research is the first application of this method for seepage analysis in both confined and unconfined porous media. The domain decomposition approach has been employed for applying MQ method easily in inhomogeneous and irregular complex geometries and decreasing the computational costs. For determining the optimum shape parameter that affects strongly the accuracy of MQ and other RFB methods, a new scheme that decreases drastically the computational time is introduced. The efficiency of the proposed algorithm has been examined under various radial basis functions, variations of number of interpolating points and points distribution, through a numerical example with analytical solution. Eventually, three examples including different boundary conditions are presented. Comparing results of the examples with other numerical methods indicates that the present approach has high capability and accuracy in solving seepage problems.     

An integrated optimization for laminate design and manufacturing of a CFRP wheel hub based on structural performance

An integrated optimization that comprehensively considers design and manufacturing factors such as the geometric appearance, laminate constitutions, laminate distribution, laminate thickness and stacking sequence, is proposed for designing a carbon fiber reinforced polymer wheel hub of a racecar. First, the driving conditions of the racecar are analyzed to determine the performance requirements. Then, under the condition that the geometric design regions are partitioned and the constitutions of fiber plies with different directions are defined, laminate design and manufacturing model is established. A multi-objective optimization is then performed to achieve a lightweight, high-stiffness laminate structure in different design regions. Next, number of plies in each region is obtained from the thickness of laminate, and then, the stacking sequence is optimized to improve the stiffness of the laminate structure. Finally, laminate transitions for different regions are investigated. The results showed that laminate design and manufacturing optimization can reduce the weight of the wheel hub and improve the performance of the wheel hub under static, dynamic and impact conditions. The proposed optimization approach provides a feasible solution for a performance-based design of composite structures.     

A level set based shape and topology optimization method for maximizing the simple or repeated first eigenvalue of structure vibration

We present a level set based shape and topology optimization method for maximizing the simple or repeated first eigenvalue of structure vibration. Considering that a simple eigenvalue is Fréchet differentiable with respect to the boundary of a structure but a repeated eigenvalue is only Gateaux or directionally differentiable, we take different approaches to derive the boundary variation that maximizes the first eigenvalue. In the case of simple eigenvalue, material derivative is obtained via adjoint method, and variation of boundary shape is specified according to the steepest descent method. In the case of N-fold repeated eigenvalue, variation of boundary shape is obtained as a result of a N-dimensional algebraic eigenvalue problem. Constraint of a structure’s volume is dealt with via the augmented Lagrange multiplier method. Boundary variation is treated as an advection velocity in the Hamilton–Jacobi equation of the level set method for changing the shape and topology of a structure. The finite element analysis of eigenvalues of structure vibration is accomplished by using an Eulerian method that employs a fixed mesh and ersatz material. Application of the method is demonstrated by several numerical examples of optimizing 2D structures.     

Effects of carrying a backpack in a symmetrical manner on the shape of the feet

The aim of this study was to assess changes in the shape of the feet while carrying a backpack constituting 10% of a child's weight. It was an observational, cross-sectional study involving 118 primary school children aged 11–13 years. Selected parameters of foot shape were assessed in both a normal position and with a backpack using podoscopy and a CQ-ST examination device. The study revealed significant differences in the length and width of the right and left feet between assessments. Moreover, the longitudinal arch of the foot was significantly lowered and deformity of the great toe was reported. Carrying a backpack constituting 10% of a child's weight results in lowering of the longitudinal and traverse arches of the feet and advanced toe deformities.     

Application of the OBD method for optimization of neural state variable estimators of the two-mass drive system

This paper presents neural estimators of the mechanical state variables of the electrical drive system with elastic joints. The non-measurable state variables, as the torsional torque and the load machine speed are estimated using multilayer feed-forward neural networks. The main stages of the design methodology of these neural estimators are presented. The optimal brain damage method is implemented for the structure optimization of each neural network. Then signals estimated by neural estimators are tested in the electrical drive control structure with additional feedbacks from the estimated shaft torque and the difference between the motor and the load speeds. The simulation results show good accuracy of both presented neural estimators for the wide range of changes of the reference speed and the load torque. The simulation results are then verified by laboratory experiments.     

基于单目视觉的车辆前方障碍物测距方法

针对行车过程中的防碰撞预警问题,提出一种基于单目视觉车辆前方障碍物检测与测距方法。为解决传统车辆检测泛化性差且人工提取特征不准确问题,通过深度学习目标检测YOLOv4算法对车辆前方多种障碍物进行检测,获取障碍物的类别信息与位置信息。运用改进的边缘检测算法调整检测框的位置,提升检测算法目标定位的准确性。根据摄像机成像原理及几何关系,得到路面三维坐标与像平面二维坐标转换模型从而进行测距,对所得测量数据进行三次曲线拟合、对测距过程和算法进行优化提升测距精度。在50m范围内平均误差为0.54m,在80m范围内平均测距误差为0.78m。实验分析对比结果表明,所提方法能够实现较精准、高效率的单目视觉测距。