Particle swarm optimization approach for multi-objective composite box-beam design

This paper presents a multi-agent search technique to design an optimal composite box-beam helicopter rotor blade. The search technique is called particle swarm optimization (‘inspired by the choreography of a bird flock’). The continuous geometry parameters (cross-sectional dimensions) and discrete ply angles of the box-beams are considered as design variables. The objective of the design problem is to achieve (a) specified stiffness value and (b) maximum elastic coupling. The presence of maximum elastic coupling in the composite box-beam increases the aero-elastic stability of the helicopter rotor blade. The multi-objective design problem is formulated as a combinatorial optimization problem and solved collectively using particle swarm optimization technique. The optimal geometry and ply angles are obtained for a composite box-beam design with ply angle discretizations of 10°, 15° and 45°. The performance and computational efficiency of the proposed particle swarm optimization approach is compared with various genetic algorithm based design approaches. The simulation results clearly show that the particle swarm optimization algorithm provides better solutions in terms of performance and computational time than the genetic algorithm based approaches.     

Performance of Gradient-Based Optimizer for Optimum Wind Cube Design

Renewable energy is a safe and limitless energy source that can be utilized for heating, cooling, and other purposes. Wind energy is one of the most important renewable energy sources. Power fluctuation of wind turbines occurs due to variation of wind velocity. A wind cube is used to decrease power fluctuation and increase the wind turbine’s power. The optimum design for a wind cube is the main contribution of this work. The decisive design parameters used to optimize the wind cube are its inner and outer radius, the roughness factor, and the height of the wind turbine hub. A Gradient-Based Optimizer (GBO) is used as a new metaheuristic algorithm in this problem. The objective function of this research includes two parts: the first part is to minimize the probability of generated energy loss, and the second is to minimize the cost of the wind turbine and wind cube. The Gradient-Based Optimizer (GBO) is applied to optimize the variables of two wind turbine types and the design of the wind cube. The metrological data of the Red Sea governorate of Egypt is used as a case study for this analysis. Based on the results, the optimum design of a wind cube is achieved, and an improvement in energy produced from the wind turbine with a wind cube will be compared with energy generated without a wind cube. The energy generated from a wind turbine with the optimized cube is more than 20 times that of a wind turbine without a wind cube for all cases studied.     

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.     

Particle swarm-based structural optimization of laminated composite hydrokinetic turbine blades

Composite blade manufacturing for hydrokinetic turbine application is quite complex and requires extensive optimization studies in terms of material selection, number of layers, stacking sequence, ply thickness and orientation. To avoid a repetitive trial-and-error method process, hydrokinetic turbine blade structural optimization using particle swarm optimization was proposed to perform detailed composite lay-up optimization. Layer numbers, ply thickness and ply orientations were optimized using standard particle swarm optimization to minimize the weight of the composite blade while satisfying failure evaluation. To address the discrete combinatorial optimization problem of blade stacking sequence, a novel permutation discrete particle swarm optimization model was also developed to maximize the out-of-plane load-carrying capability of the composite blade. A composite blade design with significant material saving and satisfactory performance was presented. The proposed methodology offers an alternative and efficient design solution to composite structural optimization which involves complex loading and multiple discrete and combinatorial design parameters.     

Performance appraisal of a small wind turbine under the use of three rotor hub configurations

Clean Technologies and Environmental Policy - Small wind turbine blades are generally designed without accounting for geometric attributes of the rotor hub configuration. The present study...     

基于混沌粒子群优化算法的AGV路径规划研究

目的 优化物流AGV路径最优问题。方法 提出一种改进的混沌粒子群优化算法,采用基于Bézier曲线的路径规划模型,通过调整Bézier曲线的控制点数量,显著改善AGV轨迹路线的长度和平滑度。结果 采用混沌粒子群滤波算法（CPSO）最优化处理Bézier曲线的控制点数,引入适应度函数,评估是否满足终止标准,如果达到最大迭代次数或者在给定迭代次数时未修改最优解则终止CPSO算法,最后利用选取的控制点计算出更短、更平滑的轨迹路线,提高了算法的寻优能力。结论 采用CPSO算法初始化Bézier曲线可以获得更加平滑的最短路径。     

Mitigation of Torque Ripple in Interior Permanent Magnet Motors by Optimal Shape Design

Optimal shape design of an interior permanent-magnet (IPM) synchronous motor can substantially reduce its torque pulsation. However, the shape design variations should be feasible and practical. We report on an optimal shape design obtained by drilling small circular holes in the rotor. We found that an optimal location and radius of the holes effectively suppresses the torque pulsation of the IPM drive for various loads under steady-state conditions. The optimal design at rated load is considered as the final design. We use a transient finite-element analysis that is coupled with motion and adequate electric excitation for optimization purposes. An evaluation of the optimal design at various operating conditions showed torque ripple reduction and average torque improvement under all load conditions. The optimal design also shows improvement in the field-weakening region for high-speed operation     

叶片局部损伤对大型水平轴风力机静动态特性影响的仿真分析

基于参数化整机模型研究风力机的静态和动态特性,以及叶片局部损伤对单叶片和整机的静动态特性影响规律,揭示叶片局部损伤与这些特性的关系。使用ANSYS的APDL语言,实现MW级水平轴风力机复合材料叶片的参数化建模;采用公共几何元素、连接单元和自由度耦合等方法,组建包含叶片、轮毂、塔架等部件的整机有限元模型。计算分析了重力、离心力和风压力共同作用下正常整机和包含叶片局部损伤的整机在迎风位置时的静态响应。比较了正常和损伤状态下根部固定单叶片和装入整机中叶片的动态应变响应。研究表明:对根部固定的单叶片,风轮面内的动态应变响应随迎风面损伤程度的增大而减小;对整机中的叶片,垂直风轮面的动态应变响应随迎风面损伤程度的增大而增大。研究方法和结论对大型水平轴风力机叶片损伤的在线监测具有重要 意义。     

Multiobjective robust optimization method for drawbead design in sheet metal forming

It is recognized that fracture and wrinkling in sheet metal forming can be eliminated via an appropriate drawbead design. Although deterministic multiobjective optimization algorithms and finite element analysis (FEA) have been applied in this respect to improve formability and shorten design cycle, the design could become less meaningful or even unacceptable when considering practical variation in design variables and noises of system parameters. To tackle this problem, we present a multiobjective robust optimization methodology to address the effects of parametric uncertainties on drawbead design, where the six sigma principle is adopted to measure the variations, a dual response surface method is used to construct surrogate model and a multiobjective particle swarm optimization is developed to generate robust Pareto solutions. In this paper, the procedure of drawbead design is divided into two stages: firstly, equivalent drawbead restraining forces (DBRF) are obtained by developing a multiobjective robust particle swarm optimization, and secondly the DBRF model is integrated into a single-objective particle swarm optimization (PSO) to optimize geometric parameters of drawbead. The optimal design showed a good agreement with the physical drawbead geometry and remarkably improve the formability and robust. Thus, the presented method provides an effective solution to geometric design of drawbead for improving product quality.     

舰艇武器布置问题的一种协同优化算法

针对舰艇武器布置问题的特点，提出了一种基于粒子群优化和分类器系统的协同优化算法，以粒子群优化进行优化计算，用分类器系统消除约束．计算实例表明，该算法能较好地实现优化计算，并能节省大量的计算时间．     

Energy and operation management of a microgrid using particle swarm optimization

This article presents an efficient algorithm based on particle swarm optimization (PSO) for energy and operation management (EOM) of a microgrid including different distributed generation units and energy storage devices. The proposed approach employs PSO to minimize the total energy and operating cost of the microgrid via optimal adjustment of the control variables of the EOM, while satisfying various operating constraints. Owing to the stochastic nature of energy produced from renewable sources, i.e. wind turbines and photovoltaic systems, as well as load uncertainties and market prices, a probabilistic approach in the EOM is introduced. The proposed method is examined and tested on a typical grid-connected microgrid including fuel cell, gas-fired microturbine, wind turbine, photovoltaic and energy storage devices. The obtained results prove the efficiency of the proposed approach to solve the EOM of the microgrids.     

Reconstruction of operating loads in wind turbines with inertial measurement units

Wind turbines are a major source of renewable energy. Load monitoring is considered to improve reliability of the systems and to reduce the cost of operation. We propose a load monitoring system which consists of inertial measurement units. These track the movement of rotor blade, hub and tower top. In addition, wind turbine states, e.g. yaw angle, pitch angle and rotation speed, are recorded. By solving a navigation algorithm with a Kalman Filter approach, the raw sensor data is combined with an error model to reduce the tracking error. In total, five inertial measurement units are installed on the research wind energy converter AD 8–180 on the test site in Bremerhaven. Results show that tracking the blade movement in full operation is possible and that loads can be estimated with a model-based approach. In comparison to simulations, the blade deflections can be approximated by an aeroelastic model. The presented approach can be used as basis for comprehensive load monitoring and observer system with additional increase of system robustness by measurement redundancy.

     

基于疲劳寿命的旋翼气动弹性多目标优化研究

基于Hamilton原理基础上推导了旋翼桨叶有限元动力学模型和疲劳寿命计算模型。以动力学特性的固有频率,自转惯量为约束,以剖面特性参数的挥、摆、扭刚度及桨叶线性密度为设计变量,进行最小质量及最大疲劳寿命的多目标优化。采用满足溢出分析的优化算法（Satisficing Trade-off Analysis）。结果在满足各约束条件下,实现旋翼桨叶质量减少7.27％,疲劳寿命循环次数由3.98 108次到4.73 108次,寿命提高了18.7%,优化效果明显。     

一种改进粒子群算法的立体阵列优化方法

利用传统粒子群算法对立体阵列所有臂的阵元分布形式同时进行优化时,不仅耗时,而且易于收敛到局部解。为了解决这个问题,提出了一种改进粒子群算法(Improved Particle Swarm Optimization,IPSO)。改进算法采用并行计算思想,同时初始化多个粒子群,每个粒子通过优化一个臂(优化臂)的阵元参数达到"降维"的目的,使用线性递减惯性权重,对多个粒子群同时进行预优化,获得中间解。利用中间解构建一个"升维"的新粒子,使用最小惯性权重对新粒子继续优化,满足停止条件后输出。通过对5臂星形立体阵列进行优化设计,发现改进算法不仅耗时短,而且能够得到更优的结果,最后通过6个仿真实验讨论了所设计的阵列的指向特性。     

Development of a wind turbine gearbox virtual load sensor using multibody simulation and artificial neural networks

A current development trend in wind energy is characterized by the installation of wind turbines (WT) with increasing rated power output. Higher towers and larger rotor diameters increase rated power leading to an intensification of the load situation on the drive train and the main gearbox. However, current main gearbox condition monitoring systems (CMS) do not record the 6‑degree of freedom (6-DOF) input loads to the transmission as it is too expensive. Therefore, this investigation aims to present an approach to develop and validate a low-cost virtual sensor for measuring the input loads of a WT main gearbox. A prototype of the virtual sensor system was developed in a virtual environment using a multi-body simulation (MBS) model of a WT drivetrain and artificial neural network (ANN) models. Simulated wind fields according to IEC 61400‑1 covering a variety of wind speeds were generated and applied to a MBS model of a Vestas V52 wind turbine. The turbine contains a high-speed drivetrain with 4‑points bearing suspension, a common drivetrain configuration. The simulation was used to generate time-series data of the target and input parameters for the virtual sensor algorithm, an ANN model. After the ANN was trained using the time-series data collected from the MBS, the developed virtual sensor algorithm was tested by comparing the estimated 6‑DOF transmission input loads from the ANN to the simulated 6‑DOF transmission input loads from the MBS. The results show high potential for virtual sensing 6‑DOF wind turbine transmission input loads using the presented method.

     

柔性板压电作动器的优化位置与主动控制实验研究

对柔性悬臂板主动控制中作动器的优化位置进行研究,其中作动器采用压电形式,优化算法采用粒子群方法,指标函数采用基于能量的可控Gramian优化配置准则。仿真和实验结果显示,粒子群优化算法能够有效地对作动器的优化位置进行计算,尤其适用于多个作动器的位置优化问题,基于作动器最优位置的控制设计能够取得良好的控制效果。     

风电机组塔筒模态的环境脉动实测与数值模拟研究

摘 要：基于随机振动及系统识别理论,对内蒙古京能乌兰伊利更风电场中三座风电机组塔筒进行了环境脉动实测,提出了 “桨叶—轮毂—机舱—塔筒”耦合的整体建模的方法,数值模拟与实测结果表明,风电机组塔筒可以有效地避免共振,满足GL规范的设计要求;塔筒主要振动形式为侧向弯曲振动、前后弯曲振动和扭转振动;塔筒一阶平动阻尼比为1.78%左右,一阶扭转阻尼比为0.6%左右。采用整体建模方法建立的模型与实测结果有较好的一致性,可以指导风力发电塔系统的风致动力响应分析和振动控制分析。     

变基圆半径涡旋膨胀机型线参数的优化选取

涡旋型线在涡旋膨胀机的优化设计中发挥着重要作用。目前对变基圆半径涡旋型线的研究大多集中于几何、数学模型的建立和推导，缺乏对型线参数选取的深入研究。为了弥补上述不足，采用粒子群算法对变基圆半径涡旋型线的参数进行优化选取。首先，建立了变基圆半径涡旋型线的啮合盘径和变基圆半径涡旋膨胀机膨胀比的数学模型；其次，分析了基圆变化系数对变基圆半径涡旋膨胀机性能的影响；最后，对变基圆半径涡旋型线的参数进行了单目标和多目标优化。优化后得到在啮合盘径和膨胀比各自取得极值时基圆半径和基圆变化系数的取值，以及以啮合盘径和膨胀比为共同优化目标时基圆半径和基圆变化系数的非劣解集。研究结果可为涡旋膨胀机的型线设计提供参考。     

A particle swarm optimizer for constrained multi-objective engineering design problems

This article presents a particle swarm optimizer (PSO) capable of handling constrained multi-objective optimization problems. The latter occur frequently in engineering design, especially when cost and performance are simultaneously optimized. The proposed algorithm combines the swarm intelligence fundamentals with elements from bio-inspired algorithms. A distinctive feature of the algorithm is the utilization of an arithmetic recombination operator, which allows interaction between non-dominated particles. Furthermore, there is no utilization of an external archive to store optimal solutions. The PSO algorithm is applied to multi-objective optimization benchmark problems and also to constrained multi-objective engineering design problems. The algorithmic effectiveness is demonstrated through comparisons of the PSO results with those obtained from other evolutionary optimization algorithms. The proposed particle swarm optimizer was able to perform in a very satisfactory manner in problems with multiple constraints and/or high dimensionality. Promising results were also obtained for a multi-objective engineering design problem with mixed variables.     

基于粒子群与聚类的多目标优化算法

针对粒子群优化算法容易陷入局部最优的问题,提出了一种基于粒子群优化与分解聚类方法相结合的多目标优化算法。算法基于参考向量分解的方法,通过聚类优选粒子策略来更新全局最优解。首先,通过每条均匀分布的参考向量对粒子进行聚类操作,来促进粒子的多样性。从每个聚类中选择一个具有最小聚合函数适应度值的粒子,以平衡收敛性和多样性。动态更新全局最优解和个体最优解,引导种群均匀分布在帕累托前沿附近。通过仿真实验,与4种粒子群多目标优化算法进行对比。实验结果表明,提出的算法在27个选定的基准测试问题中获得了20个反世代距离(IGD)最优值。