Robust airfoil optimization based on improved particle swarm optimization method

A robust airfoil optimization platform is constructed based on the modified particle swarm optimization method (i.e., the second-order oscillating particle swarm method), which consists of an efficient optimization algorithm, a precise aerodynamic analysis program, a high accuracy surrogate model, and a classical airfoil parametric method. There are two improvements for the modified particle swarm method compared with the standard particle swarm method. First, the particle velocity is represented by the combination of the particle position and the variation of position, which makes the particle swarm algorithm a second-order precision method with respect to the particle position. Second, for the sake of adding diversity to the swarm and enlarging the parameter searching domain to improve the global convergence performance of the algorithm, an oscillating term is introduced to the update formula of the particle velocity. At last, taking two airfoils as examples, the aerodynamic shapes are optimized on this optimization platform. It is shown from the optimization results that the aerodynamic characteristic of the airfoils is greatly improved in a broad design range.     

基于Kriging代理模型的迭代更新高效反演方法

为提高混凝土坝等大体积结构参数反演效率和精度,减少由于应用有限元进行大量正分析而产生的计算机时,建立了一种结合Kriging代理模型和粒子群优化(PSO)算法的迭代更新反演方法。通过拉丁超立方抽样(LHS)方法确定初始样本点的空间分布,并使用有限元正分析获取对应的响应值,构建粗糙的初始代理模型,结合具有全局寻优能力的PSO算法,反演大体积结构的分区弹性模量,随之再代入有限元模型中,计算获取新的位移响应,并将其作为新样本加入到样本集中,通过迭代更新获得局部更高精度的代理模型。工程实际算例表明,该方法对混凝土坝等大体积结构参数反演精度较高和适用性好,且能大幅减少传统有限元模型反演方法所需消耗的正分析机时,提高反演效率。     

APPLICATION OF SURROGATE BASED PARTICLE SWARM OPTIMIZATION TO THE RELIABILITY-BASED ROBUST DESIGN OF COMPOSITE PRESSURE VESSELS

A surrogate based particle swarm optimization （SBPSO） algorithm which combines the surrogate modeling technique and particle swarm optimization is applied to the reliability- based robust design （RBRD） of composite pressure vessels. The algorithm and efficiency of SBPSO are displayed through numerical examples. A model for filament-wound composite pressure vessels with metallic liner is then studied by netting analysis and its responses are analyzed by using Finite element method （performed by software ANSYS）. An optimization problem for maximizing the performance factor is formulated by choosing the winding orientation of the helical plies in the cylindrical portion, the thickness of metal liner and the drop off region size as the design variables. Strength constraints for composite layers and the metal liner are constructed by using Tsai-Wu failure criterion and Mises failure criterion respectively. Numerical examples show that the method proposed can effectively solve the RBRD problem, and the optimal results of the proposed model can satisfy certain reliability requirement and have the robustness to the fluctuation of design variables.     

Thermal-economic multi-objective optimization of shell and tube heat exchanger using particle swarm optimization (PSO)

Many studies are performed by researchers about shell and tube heat exchanger (STHE) but the multi-objective particle swarm optimization (PSO) technique has never been used in such studies. This paper presents application of thermal-economic multi-objective optimization of STHE using PSO. For optimal design of a STHE, it was first thermally modeled using e-number of transfer units method while Bell–Delaware procedure was applied to estimate its shell side heat transfer coefficient and pressure drop. Multi objective PSO (MOPSO) method was applied to obtain the maximum effectiveness (heat recovery) and the minimum total cost as two objective functions. The results of optimal designs were a set of multiple optimum solutions, called ‘Pareto optimal solutions’. In order to show the accuracy of the algorithm, a comparison is made with the non-dominated sorting genetic algorithm (NSGA-II) and MOPSO which are developed for the same problem.     

基于多轮升维策略与改进量子行为粒子群优化算法的热导率函数估计方法

将改进的量子行为粒子群优化算法应用于材料热导率函数估计问题中,并提出了一种多轮升维策略对算法的搜索过程进行优化,形成了一种鲁棒性强且高效的反演方法。通过数值实验测试了该方法在测量误差以及系统误差下的表现,并对不同粒子群优化算法的性能进行了比较研究。结果表明,采用的反演方法能够在较大的搜索范围与反演维度下稳定收敛,对测量误差的敏感度较低;提出的多轮升维策略能够使各类粒子群优化算法在热导率函数估计问题中的搜索效率得到提升。     

Retrieval of aerosol size distribution using improved quantum-behaved particle swarm optimization on spectral extinction measurements

An improved quantum-behaved particle swarm optimization (IQPSO) algorithm is employed to determine aerosol size distribution (ASD). The direct problem is solved using the anomalous diffraction approximation and Lambert–Beer's Law. Compared with the standard particle swarm optimization algorithm, the stochastic particle size optimization algorithm and the original QPSO, our IQPSO has faster convergence speed and higher accuracy within a smaller number of generations. Optimization parameters for the IQPSO were also evaluated; we recommend using four measurement wavelengths and 50 particles. Size distributions of various aerosol types were estimated using the IQPSO under dependent and independent models. Finally, experimental ASDs at different locations in Harbin were recovered using the IQPSO. All our results confirm that the IQPSO algorithm is an effective and reliable technique for estimating ASD.     

基于POD-RBF代理模型的迭代更新反演方法

针对常规的水工大坝等大型工程结构参数反演需要耗费大量有限元正分析机时的问题,建立了具有较好反演精度和泛化性能的POD-RBF代理模型和快速迭代更新反演算法。基于有限元分析获得足量数据样本,利用POD提取本征向量,并使用RBF方法进行插值得到有限元模型的代理模型;同时结合粒子群算法的全局寻优能力和高斯-牛顿法的快速局部收敛优势,建立了一种新的高效率迭代反演方法,并应用于混凝土大坝分区弹性模量反演。结果表明,该方法适用于大坝等大体积混凝土结构的力学参数反演。同时,相较于传统的单一反演方法,该方法在反演效率和反演精度两方面均显示出优势。     

Parameter identification for cracks in elastic plate structures based on remote strain fields

A method for the detection of cracks in plate structures is presented. In contrast to most of the common monitoring concepts taking advantage of the reflection of elastic waves at crack faces, the presented approach is based on the strain measured at different locations on the surface of the structure. This allows both the identification of crack position parameters, such as length, location and angles with respect to a reference coordinate system and the calculation of stress intensity factors (SIF). The solution of the direct problem is performed on the basis of the BFM (body force method). The inverse problem is solved applying the particle swarm optimization (PSO) algorithm. The BFM is based on the principle of linear superposition which allows the calculation of the strain field in a cracked body. The strain at an arbitrary point in the structure is replaced by the strain provided by body force doublets in the uncracked structure. The doublets as well as external loads are parameters which have to be determined solving the inverse problem by minimizing a fitness function, which is defined by a square sum of residuals between measured strain distributions and computed ones for an assumed crack. The PSO algorithm applied to the fitness function operates on the basis of a swarm of candidate solutions. Once knowing loading and crack parameters, the SIF can be determined.     

Particle swarm optimization with fractional-order velocity

This paper proposes a novel method for controlling the convergence rate of a particle swarm optimization algorithm using fractional calculus (FC) concepts. The optimization is tested for several well-known functions and the relationship between the fractional order velocity and the convergence of the algorithm is observed. The FC demonstrates a potential for interpreting evolution of the algorithm and to control its convergence.     

求解非线性方程组的混沌粒子群算法及应用

针对非线性方程组的求解在工程上具有广泛的实际意义,经典的数值算法如牛顿法存在其收敛性依赖于初值而实际计算中初值难确定的问题,提出以混沌粒子群算法求解非线性方程。它通过将混沌搜索机制有机地引入粒子群算法,使每个粒子从混沌搜索机制与粒子群算法搜索机制中获得适当的搜索方向,以混沌变量的遍历性增强粒子的搜索性能与更全面地应用目标函数的信息,并反映到逐代更新的个体极值和群体极值中,可更有效地调整粒子的移向并最终获得最优解。测试结果表明这一尝试的有效性。最后将所提的方法用于建立复合材料结构的疲劳寿命与应力、温度、湿度的关系模型。     

Automated critical point identification for PIV data using multimodal particle swarm optimization

A hybrid sequential niche algorithm is used for the automated identification of critical points of velocity fields. This method combines an adaptive sequential niche technique with deterministic local optimization to detect critical points: focus, node and saddle points. A particle swarm algorithm performs a global search whereas vortex core identification functions compute the precise location as the extremum of the corresponding function. Once a critical point is found, a rectangular niche is constructed around the point. The particle swarm then proceeds to explore different regions of the velocity field. The process advances sequentially, avoiding areas near previously found critical points by blocking niches obtained from previous steps. The niche size is automatically adjusted each time a search enters inside an existing niche. Vortex core functions are used for critical point identification and calculating its precise location inside each niche. The procedure is validated on particle image velocimetry data obtained with two types of flows, an impinging jet flow and a flow downstream of a model building. The hybrid algorithm proved to be very efficient and robust for automated detection and identification of critical points. It can be used as a first step for studying the time‐dependent dynamic behavior of instantaneous velocity fields by tracking topological critical points. This is the first study that uses a multi‐modal particle swarm algorithm for critical point identification. Copyright © 2011 John Wiley & Sons, Ltd.     

基于修正PSO-UKF的SINS/GPS组合导航滤波算法

针对噪声时变特性引起滤波精度下降的问题,提出了一种基于修正粒子群技术( PSO)的自适应UKF算法.为了克服传统粒子群算法过早收敛,容易陷入局部最优的问题,基于粒子的适应值方差提出了一种惯性权值实时修正算法,有效改善了传统PSO算法.在使用新息序列对观测噪声进行实时跟踪的同时,通过构造合理的适应度函数将修正PSO算法和...     

基于萤火虫群优化的空间非合作目标相对导航粒子滤波算法

研究了空间非合作目标相对导航算法,针对标准粒子滤波的重采样过程导致的粒子贫化现象及其造成的相对导航精度下降问题,分析了萤火虫优化算法的运行机制,提出一种基于萤火虫智能优化算法的改进粒子滤波算法。改进算法通过优化粒子滤波的重采样过程,使粒子群智能的向高似然区域移动,同时在低似然区域也合理保留了部分粒子,保证了粒子的多样性,提高了样本的整体质量。仿真结果表明,改进算法导航精度较标准算法提高了39.35%,达到稳定精度所需粒子数较少,有效抑制了粒子贫化问题。     

Stacking sequence optimization of composite plates for maximum fundamental frequency using particle swarm optimization algorithm

The paper illustrates the application of the particle swarm optimization (PSO) algorithm to the lay-up design of symmetrically laminated composite plates for maximization of fundamental frequency. The design variables are the fiber orientation angles, edge conditions and plate length/width ratios. The formulation is based on the classical laminated plate theory (CLPT), and the method of analysis is the semi-analytical finite strip approach which has been developed on the basis of full energy methods. The performance of the PSO is also compared with the simple genetic algorithm and shows the good efficiency of the PSO algorithm. To check the validity, the obtained results are compared with those available in the literature and some other stacking sequences, wherever possible.     

Rational approximation for fractional-order system by particle swarm optimization

In this paper, a rational approximation me-thod is proposed for the fractional-order system using the particle swarm optimization (PSO). Firstly, the approximation method for the fractional-order operator is studied, because a fractional-order system consists of many fractional-order operators. The coefficients of the transfer function are calculated using PSO with a fitness function under the continued fraction expansion (CFE) framework in the frequency domain. The average velocity of the particle swarm is defined to reflect the real state of particle swarm. To improve the global optimization and achieve a more satisfactory fitting result, comparing with the linear PSO, the chaotic optimization is combined with PSO. The numerical examples of fractional-order systems demonstrate the effectiveness of this method.     

Multifunctional design of two-dimensional cellular materials with tailored mesostructure

Two-dimensional cellular materials (prismatic honeycombs) provide a range of properties that make them suitable for multifunctional applications involving heat dissipation and structural performance. In this paper we present two-scale homogenization-based finite element scheme for convective heat transfer and structural characterization of 2-D cellular metals with uniform and graded cell sizes of various topologies as well as with mixed cell-topologies. For convective heat transfer analysis, the cells are modeled implicitly as temperature-dependent sinks modeling the out-of-plane fluid convection through the cells; the sink strength is determined via a micromechanics problem of heat transfer in a cell. For structural analysis, the cellular material is represented as a micropolar continuum with linear elastic constitutive equations obtained via micromechanics solution of a representative unit cell. The analyses are then used in conjunction with an optimization algorithm to design cellular materials with functionally tailored mesostructures. The analysis and design framework enables tailoring cellular materials with graded cell structures of a given topology as well as with cell structures that combine multiple topologies.     

CABC–CSA: a new chaotic hybrid algorithm for solving optimization problems

Recently a lot of methods have been presented for solving optimization problems. In this paper, we are trying to propose a new hybrid algorithm for solving these kinds of problem. The proposed algorithm is based on chaotic artificial bee colony and chaotic simulated annealing, CABC–CSA. The chaotic artificial bee colony finds new locations chaotically. Actually, the proposed algorithm provides a combination of local search accuracy of simulated annealing and the ability of global search of artificial bee colony. Furthermore, we used a different method for generating the initial population. The proposed algorithm is validated using 12 benchmark functions. The results are compared with those of the artificial bees’ algorithm, the hybrid algorithm of artificial bee colony and simulated annealing and particle swarm optimization. Simulation results show the efficiency of the proposed algorithm.     

粒子群优化算法在传递对准中的应用

给出了一种基于粒子群优化算法的捷联惯导传递对准算法。简单分析了传递对准任务要求和主子惯导惯性器件输出之间的关系,将传递对准问题作为参数优化问题进行求解,给出了基于粒子群优化算法进行传递对准的数学模型。定义了传递对准的优化目标函数,介绍了粒子群优化算法及其应用于传递对准的具体算法设置。用粒子群优化算法求解目标函数的最小值,可获得主子惯导之间的失准角,进行一次校正即可完成传递对准过程。通过计算机仿真对算法进行了验证分析,在仿真条件下（陀螺精度为0.1°/h）,能达到方位0.1°的精度。与其他对准算法一样,算法受载体机动条件的影响较大,一般需要姿态机动来提高陀螺的信噪比。     

A modified multi-objective particle swarm optimization approach and its application to the design of a deepwater composite riser

A modified multi-objective particle swarm optimization method is proposed for obtaining Pareto-optimal solutions effectively. Different from traditional multi-objective particle swarm optimization methods, Kriging meta-models and the trapezoid index are introduced and integrated with the traditional one. Kriging meta-models are built to match expensive or black-box functions. By applying Kriging meta-models, function evaluation numbers are decreased and the boundary Pareto-optimal solutions are identified rapidly. For bi-objective optimization problems, the trapezoid index is calculated as the sum of the trapezoid’s area formed by the Pareto-optimal solutions and one objective axis. It can serve as a measure whether the Pareto-optimal solutions converge to the Pareto front. Illustrative examples indicate that to obtain Pareto-optimal solutions, the method proposed needs fewer function evaluations than the traditional multi-objective particle swarm optimization method and the non-dominated sorting genetic algorithm II method, and both the accuracy and the computational efficiency are improved. The proposed method is also applied to the design of a deepwater composite riser example in which the structural performances are calculated by numerical analysis. The design aim was to enhance the tension strength and minimize the cost. Under the buckling constraint, the optimal trade-off of tensile strength and material volume is obtained. The results demonstrated that the proposed method can effectively deal with multi-objective optimizations with black-box functions.     

基于改进粒子群的薄壁变截面刚架临界载荷优化算法

针对大型变截面薄壁结构的稳定问题,以一类任意约束对称结构受非对称载荷的单跨刚架为研究对象,结构拆分为相关铁木辛柯（Timoshenko）梁,结合差分原理和最优化方法,以每段刚架的每个离散点挠度、临界载荷、轴力、剪力和梁端弯矩为设计变量,建立求解满足边界条件的非线性差分方程模型,提出基于优胜劣汰粒子更新的粒子群（IPSO）临界载荷优化算法。运用JAVA编程语言编制对应优化程序,分析典型算例并核实ABAQUS仿真结果。研究表明,本文提出的优化算法获得了有效的变形位型和高精度的临界载荷计算,能更好地描述刚架受力下位型和载荷的力学关系,进一步为工程设计与分析提供支持。