强磁体结构的磁场和应力同步分析方法

在强磁体结构中，电磁力是主要载荷。由于磁场计算和结构分析属不同学科，这两部分研究一般都是孤立地、分别进行的。本文指出了这样研究的弊病，提出了磁场和应力同步分析的方法。并以中国ＨＬ－１托卡马克磁体为例，采用有限元应力分析和三维磁场计算同步进行的方法，得到了托卡马克环向场线圈的磁体力、应力和变形分布。     

积分方程计算舰船感应磁场

积分方程法是取得舰船应磁场的有效方法，且简便易行，以某型舰船为算例，介绍了积分方程法在舰船感应磁场计算中的应用，给出了剖分方法，算例结果与工程实际测量结果相吻合，可应用于工程实际。     

基于舰船磁场模型的离线磁定位研究

目前，海上舰船磁场测量试验获得的数据一般为时间通过曲线，不能直接用于舰船磁场建模。为了解决上述问题，利用遗传算法良好的全局搜索能力，在目标舰船运动．参数未知、磁探头存在姿态角的情况下,实现了一种基于单旋转椭球体磁场模型的离线磁定位算法．计算结果表明，该方法效果良好。     

面向磁场有限元分析的舰船模块化几何建模技术研究

运用模块化思想，完成了舰船结构模块和功能模块的划分，并通过对功能模块的变参，实现了自动生成船舶各个组成部件几何模型的建模系统。最后，以某舰几何模型为基础对该舰磁场分布进行有限元分析计算。结果表明，该方法计算精度较高，能够较好反映舰船空间磁场的分布。     

舰船磁场信号的Lipschitz指数与船速的关系

用小波变换的方法对舰船磁场信号进行奇异性分析，找出船速与奇异性指数之间的关系，并通过人工神经网络来判断船速。     

舰船固定磁场分量分离的一种新方法

为了准确了解舰船的固定磁场分布，进而对舰船磁性防护作出指导，用等效磁偶板子的方法对舰船固定磁场分量分离的方法进行了研究，通过对固定磁场垂直分量总和的曲线进行分析，根据磁偶板子组合产生磁场规律的先验知识，选择最佳的磁偶板子的排列方式，然后对磁偶板子磁矩范围进行估计，最后利用遗传算法这种全局优化算法求出磁源的参数，进而分离出固定磁场各个分量。仿真计算结果表明，该方法能够比较好地搜索到磁偶板子的最佳数目、位置和磁矩，可以用于舰船固定磁场的分离。     

HDDR工艺各向异性Nd（Fe,Co）B粘结磁体的磁性与取向磁场的关系

研究了取向磁场对ＨＤＤＲ工艺制备的各向异性Ｎｄ（Ｆｅ,Ｃｏ）Ｂ粘结磁体永磁性能的影响,实验指出采用ＨＤＤＲ工艺制取的含Ｇａ的Ｎｄ（Ｆｅ,Ｃｏ）Ｂ磁粉具有较强的各向异性。在用这种磁粉制备粘结磁体时,随着施回报以向磁场强度的逐渐增强,磁体的剩磁,矫顽力和磁能积不同程度地增大,其永磁性能明显提高。     

基于PSO的人工神经网络学习算法的研究

PSO算法由于具有独特的信息共享机制而得到广泛应用。介绍人工神经网络的基本原理以及网络学习及泛化的方法,以此为基础将POS算法作为学习算法用于人工神经网络训练,给出基于PSO的神经网络学习算法的设计方法,并通过实验,验证PSO算法在训练集错误率方面的优越性。     

舰船大空间舱室火灾模拟及防火设计

为了研究舰船内部大空间舱室这一典型结构的火灾及舰船火灾安全设计，对大空间舱室内火灾发生后的蔓延情况进行了模拟计算，得出其火灾蔓延规律，总结出针对该类型结构的防火设计应该注意的地方．     

被动免疫算法在模拟电路故障诊断中的应用研究

基于生物免疫系统的信息处理机理,介绍了被动免疫算法的实现过程,并将其应用到某装备电路板的故障诊断之中。该算法具有边检测边学习的动态调整功能,仿真和实验实例表明,该算法适合于电路板的故障诊断,有较高的故障诊断率。     

Design and simulation of equilateral triangular microstrip antenna using particle swarm optimization (PSO) and advanced particle swarm optimization (APSO)

In this paper a new design is proposed in microstrip antenna family. In this paper, a review design of microstrip antenna design using particle swarm optimization (PSO) and advanced particle swarm optimization (APSO) has been presented which optimizes the parameters and both results are compared. This technique helps antenna engineers to design, analyze, and simulate antenna efficiently and effectively. An advanced PSO driven antenna has been developed to calculate resonant frequency of slit-cut stacked equilateral triangular microstrip antenna. The paper presents simplicity, accuracy and comparison of result between PSO and APSO.     

Decomposition-based multi-objective differential evolution particle swarm optimization for the design of a tubular permanent magnet linear synchronous motor

This article proposes a decomposition-based multi-objective differential evolution particle swarm optimization (DMDEPSO) algorithm for the design of a tubular permanent magnet linear synchronous motor (TPMLSM) which takes into account multiple conflicting objectives. In the optimization process, the objectives are evaluated by an artificial neural network response surface (ANNRS), which is trained by the samples of the TPMSLM whose performances are calculated by finite element analysis (FEA). DMDEPSO which hybridizes differential evolution (DE) and particle swarm optimization (PSO) together, first decomposes the multi-objective optimization problem into a number of single-objective optimization subproblems, each of which is associated with a Pareto optimal solution, and then optimizes these subproblems simultaneously. PSO updates the position of each particle (solution) according to the best information about itself and its neighbourhood. If any particle stagnates continuously, DE relocates its position by using two different particles randomly selected from the whole swarm. Finally, based on the DMDEPSO, optimization is gradually carried out to maximize the thrust of TPMLSM and minimize the ripple, permanent magnet volume, and winding volume simultaneously. The result shows that the optimized TPMLSM meets or exceeds the performance requirements. In addition, comparisons with chosen algorithms illustrate the effectiveness of DMDEPSO to find the Pareto optimal solutions for the TPMLSM optimization problem.     

Application of particle simulation methods to composite materials: a review

Particle simulation methods represent deformation of an object by motion of particles, and their Lagrangian and discrete nature is suitable for explicit modeling of the microstructure of composite materials. They also facilitate handling of large deformation, separation, contact, and coalescence. Mesh-free particle methods will thus be appropriate for a part of issues throughout the lifecycle of composite materials despite their high calculation cost. This study focuses on three particle simulation methods, namely, smoothed particle hydrodynamics, moving particle semi-implicit method, and discrete element method, and reviews approaches for modeling composite materials through these methods. Applicability of each method as well as advantages and drawbacks will be discussed from the viewpoint of engineering of composite materials. This reviewing study suggests capability of particle simulation methods to handle multiphysics and to predict various complex phenomena that necessitate explicit modeling of the material’s microstructure consisting of reinforcements (inclusions), matrix, and voids.     

Parallel asynchronous particle swarm optimization

The high computational cost of complex engineering optimization problems has motivated the development of parallel optimization algorithms. A recent example is the parallel particle swarm optimization (PSO) algorithm, which is valuable due to its global search capabilities. Unfortunately, because existing parallel implementations are synchronous (PSPSO), they do not make efficient use of computational resources when a load imbalance exists. In this study, we introduce a parallel asynchronous PSO (PAPSO) algorithm to enhance computational efficiency. The performance of the PAPSO algorithm was compared to that of a PSPSO algorithm in homogeneous and heterogeneous computing environments for small- to medium-scale analytical test problems and a medium-scale biomechanical test problem. For all problems, the robustness and convergence rate of PAPSO were comparable to those of PSPSO. However, the parallel performance of PAPSO was significantly better than that of PSPSO for heterogeneous computing environments or heterogeneous computational tasks. For example, PAPSO was 3.5 times faster than was PSPSO for the biomechanical test problem executed on a heterogeneous cluster with 20 processors. Overall, PAPSO exhibits excellent parallel performance when a large number of processors (more than about 15) is utilized and either (1) heterogeneity exists in the computational task or environment, or (2) the computation-to-communication time ratio is relatively small.     

Adaptive range particle swarm optimization

This paper proposes a new technique for particle swarm optimization called adaptive range particle swarm optimization (ARPSO). In this technique an active search domain range is determined by utilizing the mean and standard deviation of each design variable. In the initial search stage, the search domain is explored widely. Then the search domain is shrunk so that it is restricted to a small domain while the search continues. To achieve these search processes, new parameters to determine the active search domain range are introduced. These parameters gradually increase as the search continues. Through these processes, it is possible to shrink the active search domain range. Moreover, by using the proposed method, an optimum solution is attained with high accuracy and a small number of function evaluations. Through numerical examples, the effectiveness and validity of ARPSO are examined.     

模态响应识别的粒子群优化方法在倾转旋翼机上的应用

利用粒子群优化算法识别模态频率和阻尼比的方法无需测量激励信号,且不受邻近模态耦合的影响.阐述了简谐激励作用下利用粒子群优化方法对系统模态参数的识别过程,指出了在信号经过滤波处理后该方法不能精确识别信号模态相位的缺陷,并提出了改进方法.通过仿真计算以及应用改进的方法对倾转旋翼模型机翼端部的振动信号进行识别和分析,表明改进的方法可以精确识别出信号中各模态响应的相位值,能够有效地对系统的模态响应进行识别.     

Multi-objective feasibility enhanced particle swarm optimization

This article introduces a new method entitled multi-objective feasibility enhanced partical swarm optimization (MOFEPSO), to handle highly-constrained multi-objective optimization problems. MOFEPSO, which is based on the particle swarm optimization technique, employs repositories of non-dominated and feasible positions (or solutions) to guide feasible particle flight. Unlike its counterparts, MOFEPSO does not require any feasible solutions in the initialized swarm. Additionally, objective functions are not assessed for infeasible particles. Such particles can only fly along sensitive directions, and particles are not allowed to move to a position where any previously satisfied constraints become violated. These unique features help MOFEPSO gradually increase the overall feasibility of the swarm and to finally attain the optimal solution. In this study, multi-objective versions of a classical gear-train optimization problem are also described. For the given problems, the article comparatively evaluates the performance of MOFEPSO against several popular optimization algorithms found in the literature.     

Self-regulating and self-evolving particle swarm optimizer

In this article, a novel self-regulating and self-evolving particle swarm optimizer (SSPSO) is proposed. Learning from the idea of direction reversal, self-regulating behaviour is a modified position update rule for particles, according to which the algorithm improves the best position to accelerate convergence in situations where the traditional update rule does not work. Borrowing the idea of mutation from evolutionary computation, self-evolving behaviour acts on the current best particle in the swarm to prevent the algorithm from prematurely converging. The performance of SSPSO and four other improved particle swarm optimizers is numerically evaluated by unimodal, multimodal and rotated multimodal benchmark functions. The effectiveness of SSPSO in solving real-world problems is shown by the magnetic optimization of a Halbach-based permanent magnet machine. The results show that SSPSO has good convergence performance and high reliability, and is well matched to actual problems.     

改进的混合粒子群优化算法

针对粒子群算法后期收敛速度较慢,易陷入局部最优的缺点,提出了改进的混合粒子群算法.通过更改现有的速度更新公式,加入扰动项,以及引入交叉和变异算子等措施,改进了粒子群算法的性能.数值试验表明,改进后的粒子群算法在全局寻优和局部寻优能力上均得到提高,是一种有效的优化算法.