A GPU-Based Parallel Algorithm for 2D Large Deformation Contact Problems Using the Finite Particle Method

Large deformation contact problems generally involve highly nonlinear behaviors,which are very time-consuming and may lead to convergence issues.The finite particle method(FPM)effectively separates pure deformation from total motion in large deformation problems.In addition,the decoupled procedures of the FPM make it suitable for parallel computing,which may provide an approach to solve time-consuming issues.In this study,a graphics processing unit(GPU)-based parallel algorithm is proposed for two-dimensional large deformation contact problems.The fundamentals of the FPM for planar solids are first briefly introduced,including the equations of motion of particles and the internal forces of quadrilateral elements.Subsequently,a linked-list data structure suitable for parallel processing is built,and parallel global and local search algorithms are presented for contact detection.The contact forces are then derived and directly exerted on particles.The proposed method is implemented with main solution procedures executed in parallel on a GPU.Two verification problems comprising large deformation frictional contacts are presented,and the accuracy of the proposed algorithm is validated.Furthermore,the algorithm’s performance is investigated via a large-scale contact problem,and the maximum speedups of total computational time and contact calculation reach 28.5 and 77.4,respectively,relative to commercial finite element software Abaqus/Explicit running on a single-core central processing unit(CPU).The contact calculation time percentage of the total calculation time is only 18%with the FPM,much smaller than that(50%)with Abaqus/Explicit,demonstrating the efficiency of the proposed method.

Finite Difference Method of Modelling Groundwater Flow

In this study, finite difference method is used to solve the equations that govern groundwater flow to obtain flow rates, flow direction and hydraulic heads through an aquifer. The aim therefore is to discuss the principles of Finite Difference Method and its applications in groundwater modelling. To achieve this, a rectangular grid is overlain an aquifer in order to obtain an exact solution. Initial and boundary conditions are then determined. By discretizing the system into grids and cells that are small compared to the entire aquifer, exact solutions are obtained. A flow chart of the computational algorithm for particle tracking is also developed. Results show that under a steady-state flow with no recharge, pathlines coincide with streamlines. It is also found that the accuracy of the numerical solution by Finite Difference Method is largely dependent on initial particle distribution and number of particles assigned to a cell. It is therefore concluded that Finite Difference Method can be used to predict the future direction of flow and particle location within a simulation domain.

Free clustering optimal particle probability hypothesis density(PHD) filter

As to the fact that it is difficult to obtain analytical form of optimal sampling density and tracking performance of standard particle probability hypothesis density(P-PHD) filter would decline when clustering algorithm is used to extract target states,a free clustering optimal P-PHD(FCO-P-PHD) filter is proposed.This method can lead to obtainment of analytical form of optimal sampling density of P-PHD filter and realization of optimal P-PHD filter without use of clustering algorithms in extraction target states.Besides,as sate extraction method in FCO-P-PHD filter is coupled with the process of obtaining analytical form for optimal sampling density,through decoupling process,a new single-sensor free clustering state extraction method is proposed.By combining this method with standard P-PHD filter,FC-P-PHD filter can be obtained,which significantly improves the tracking performance of P-PHD filter.In the end,the effectiveness of proposed algorithms and their advantages over other algorithms are validated through several simulation experiments.

基于随机增强量子粒子群算法的弹性波数值模拟

在本文中,我们提出了一种随机增强量子粒子群优化算法,并基于该随机增强量子粒子群算法提出了一种新的有限差分格式。随机增强量子粒子群优化算法具有明显的收敛速度优势,可以在第200代内收敛。在相同条件下,未改进的量子粒子群算法的收敛速度远低于随机增强量子粒子群算法。数值频散分析表明,基于随机增强量子粒子群算法的优化有限差分格式具有更大的频谱覆盖范围并将精度误差控制在了有效范围之内,这意味着随机增强量子粒子群算法具有更好的搜索全局精确解的能力。最后,采用基于随机增强量子粒子群算法的优化有限差分格式对弹性波动方程进行数值模拟。数值模拟结果表明,基于随机增强量子粒子群算法的优化有限差分格式能有效压制数值频散。

天牛须算法在优化磁致伸缩换能器中的应用

磁致伸缩换能器换能效率不高是锚杆无损检测准确度较低的重要原因,确定合适的线圈结构参数对提高换能效率至关重要。基于Comsol多物理场有限元仿真模拟软件建立锚杆无损检测模型,首先,确定不同线圈匝数和不同提离距离对换能效率的影响规律;其次,将天牛须算法和粒子群遗传算法应用于线圈参数的本体优化问题,提出以提高换能器换能效率为优化目标、以线圈匝数和线圈提离距离为自变量的单目标优化设计模型,筛选出最优的线圈结构参数;最后,搭建锚杆无损检测实验平台,将自变量取值范围内的数值分别进行实验验证。研究结果表明:增加线圈的匝数、缩短线圈提离距离可以提高磁致伸缩换能器的换能效率;天牛须算法和粒子群遗传算法优化参数相同且符合仿真得到的影响规律,相比于粒子群遗传算法,天牛须算法原理简单、参数少、运算量少,在处理低维优化问题时具有更大的优越性;实验得到的线圈参数取值与算法优化结果一样,验证了天牛须算法用于锚杆换能器参数优化可靠且快速。

Optimal Linear Phase Finite Impulse Response Band Pass Filter Design Using Craziness Based Particle Swarm Optimization Algorithm

An efficient method is proposed for the design of finite impulse response(FIR) filter with arbitrary pass band edge,stop band edge frequencies and transition width.The proposed FIR band stop filter is designed using craziness based particle swarm optimization(CRPSO) approach.Given the filter specifications to be realized,the CRPSO algorithm generates a set of optimal filter coefficients and tries to meet the ideal frequency response characteristics.In this paper,for the given problem,the realizations of the optimal FIR band pass filters of different orders have been performed.The simulation results have been compared with those obtained by the well accepted evolutionary algorithms,such as Parks and McClellan algorithm(PMA),genetic algorithm(GA) and classical particle swarm optimization(PSO).Several numerical design examples justify that the proposed optimal filter design approach using CRPSO outperforms PMA and PSO,not only in the accuracy of the designed filter but also in the convergence speed and solution quality.

鳍条效应软体采摘机械手建模与试验

针对现有鳍条效应软体手指建模分析方法难以兼顾高精度与小运算量的问题,本研究以课题组设计的苹果采摘软体机械手为对象,在观察软体手指变形的基础上,提出将鳍条效应软体手指等效为串连铰链四杆机构;运用虚功原理推导了给定变形状态下软体手指各处接触力与驱动力矩计算方法;提出用线弹性扭簧模型描述变形恢复力矩,利用改进的粒子群算法求解各级四杆机构扭簧劲度系数;基于有限元模型,结合二次开发建立了给定关节转角和充气压力下驱动力矩的BP神经网络静态模型。搭建试验平台对3个不同大小的仿真苹果在3个不同高度位置下进行抓取力测量试验。试验结果表明,所建立的力学模型计算抓取力相对误差绝对值小于8.6%,与有限元模型精度相当;驱动力矩测量试验结果表明关节神经网络模型计算值与测量值变化趋势相同,相对误差小于12.7%。

基于改进粒子群算法的卸料台框架轻量化研究

针对传统经验设计的某垃圾处理流水线卸料台框架质量冗杂问题,文中采用改进粒子群算法对卸料台框架进行轻量化研究。首先,使用ANSYS有限元分析软件对卸料台框架进行静力学分析,再根据静力学分析结果对卸料台框架进行简化,最后,采用改进的粒子群算法对简化后的卸料台框架进行轻量化研究。优化后的卸料台框架在满足强度和刚度要求的前提下,较原始卸料台框架减轻了64.16%。

基于Halbach阵列的新型吸附结构设计及其优化

为解决目前爬壁机器人永磁式吸附结构难以改变吸附力大小的问题,提出了一种基于Halbach阵列的新型永磁式吸附结构,该结构由方形永磁体和圆柱形永磁体按一定的充磁方向排列组合而成,通过转动圆柱形永磁体改变充磁方向,从而改变吸附力大小.采用ANSYS Maxwell软件研究了吸附结构各参数(永磁体数量、轭铁厚度、气隙大小)对吸附力和单位吸附力的影响,并对以上参数进行优选;以单位吸附力为目标参数,基于粒子群算法对两种永磁体尺寸进行优化,优化前永磁体体积为219823 mm^(3),吸附力为744 N,永磁利用率为0.00352N/mm^(3),优化后永磁体体积为160027.4 mm^(3),吸附力为819.7 N,永磁利用率为0.00510 N/mm^(3).研究结果表明:吸附结构优化后节省了27.2%所占据的空间,增加了15.1%的总体吸附力,提升了45.5%的永磁利用率,优化后吸附结构的吸附性能有了显著提高,可为永磁式吸附结构的设计及优化提供参考.

基于Lévy飞行的粒子群算法在大地电磁反演中的应用

粒子群优化算法在大地电磁测深反演中相较于一般的线性反演算法具有多种优点。然而标准粒子群算法在多维优化问题中存在早熟问题,为此,采用基于Lévy飞行随机游走策略的优化粒子群算法来处理局部最优解,增加寻优能力。通过对地电模型的反演对比表明,改进后的粒子群算法相较于标准粒子群算法适应度值下降速度更快、寻优能力更好。最后将该算法应用于已知钻孔旁实测数据,结果较好,表明该算法具有较好的实用性。

永磁—凸铁复合极直线同步电机多目标优化

提出一种永磁—凸铁复合极直线同步电机(PM-SICPLSM),电机次级为U型分段永磁结构,具有聚磁强、漏磁小的特点。为提升电机整体性能,以推力波动和平均推力为优化目标。首先,通过灵敏度分析得出影响显著的优化参数;然后,构建响应面数学模型;最后,在约束条件下通过多目标粒子群优化(MOPSO)算法求解响应面模型,得出电机永磁最优结构参数。利用有限元法对优化前后的电磁推力、空载反电势及气隙磁密等电磁特性进行对比,经响应面模型和MOPSO结合优化后的PM-SICPLSM不仅增大平均推力还提高了永磁利用率。

基于锥形渐变耦合结构的可扩展多模弯曲波导

提出了一种基于锥形渐变耦合结构的可扩展多模弯曲波导。该器件利用模式等效折射率匹配原理,通过对称的锥形渐变耦合结构,实现高阶模式与基模的相互转化,完成多模弯曲传输功能。同时,结合时域有限差分方法和粒子群优化算法,优化锥形渐变耦合结构区域,提升器件性能。实验测试结果表明,在1520~1600 nm的波长范围内,当输入模式分别为TE_0、TE_1、TE_2、TE_(3)和TE_(4)时,该器件的插入损耗分别小于1.71 dB、3.04 dB、2.90 dB、3.16 dB和4.00 dB,对应的串扰分别小于-10.60 dB、-11.35 dB、-10.92 dB、-10.35 dB和-11.45 dB。

导管架式海上风机基础结构优化设计

目前海上风机基础结构存在明显的设计冗余,导致海上风机建造成本过高。为提高经济效益,需要对导管架式海上风机基础结构进行优化设计。该文首先基于海上实测数据对海上风机所处环境载荷进行模拟,得到其时间历程;其次通过有限元方法对平箱梁四桩导管架式海上风机基础结构进行强度校核,发现结构可进行轻量化处理;最后以结构最大平均应力、最大位移和质量为目标响应,通过试验设计(design of experiments,DOE)方法和粒子群算法组合的优化方法,得到结构尺寸对结构目标响应的贡献度和主效应关系,并确定各结构最优尺寸。对海上风机基础结构进行优化设计,能在保证安全的前提下降低建造成本,可为后续海上风机基础结构设计建造提供参数参考。

基于IPSO-BP神经网络的连续刚构桥有限元模型修正

针对大跨度连续刚构桥有限元模型修正问题,提出一种基于改进粒子群算法优化BP神经网络的有限元模型修正方法。首先建立有限元模型修正的数学优化模型,其次通过改进标准粒子群算法对BP神经网络超参数进行优化,最后基于优化BP神经网络代理模型对有限元结构参数进行寻优求解。研究结果表明:基于优化BP神经网络的代理模型相较于未经优化的具有更高的拟合精度;修正后的有限元模型挠度理论计算值与实测值的平均相对误差仅为1.86%。

Multi-objective optimization in a finite time thermodynamic method for dish-Stirling by branch and bound method and MOPSO algorithm

There are various analyses for a solar system with the dish-Stirling technology.One of those analyses is the finite time thermodynamic analysis by which the total power of the system can be obtained by calculating the process time.In this study,the convection and radiation heat transfer losses from collector surface,the conduction heat transfer between hot and cold cylinders,and cold side heat exchanger have been considered.During this investigation,four objective functions have been optimized simultaneously,including power,efficiency,entropy,and economic factors.In addition to the fourobjective optimization,three-objective,two-objective,and single-objective optimizations have been done on the dish-Stirling model.The algorithm of multi-objective particle swarm optimization(MO P S O)with post-expression of preferences is used for multi-objective optimizations while the branch and bound algorithm with pre-expression of preferences is used for single-objective and multi-objective optimizations.In the case of multi-objective optimizations with post-expression of preferences,Pareto optimal front are obtained,afterward by implementing the fuzzy,LINMAP,and TOPSIS decision making algorithms,the single optimum results can be achieved.The comparison of the results shows the benefits of MOPSO in optimizing dish Stirling finite time thermodynamic equations.

基于Kriging模型的空心板梁有限元模型修正

针对空心板梁有限元模型修正,利用Kriging模型作为响应面,对空心板梁不同位置的损伤进行准确修正。文章以不同空心板梁不同位置的弹性模量作为修正变量,通过最优拉丁超立方抽样法进行抽样,通过移动载荷下空心板梁挠度影响线差值平方和作为目标函数,建立Kriging响应面,利用线性权重递减粒子群算法进行优化求解。结果证明该方法能够求解出空心板的局部损伤弹性模量。

Springback prediction for incremental sheet forming based on FEM-PSONN technology

In the incremental sheet forming （ISF） process, springback is a very important factor that affects the quality of parts. Predicting and controlling springback accurately is essential for the design of the toolpath for ISF. A three-dimensional elasto-plastic finite element model （FEM） was developed to simulate the process and the simulated results were compared with those from the experiment. The springback angle was found to be in accordance with the experimental result, proving the FEM to be effective. A coupled artificial neural networks （ANN） and finite element method technique was developed to simulate and predict springback responses to changes in the processing parameters. A particle swarm optimization （PSO） algorithm was used to optimize the weights and thresholds of the neural network model. The neural network was trained using available FEM simulation data. The results showed that a more accurate prediction of s!oringback can be acquired using the FEM-PSONN model.

光滑粒子法及其与有限元耦合算法的研究进展

拉格朗日型的有限元法和光滑粒子法在模拟材料大变形问题时各存优缺点,而有限元与光滑粒子耦合算法实现了在小变形区域采用有限元法计算,在局部的大变形区域采用光滑粒子法计算,有效地综合了有限元法计算效率高和光滑粒子法能够自然地模拟材料大变形问题的特点.重点论述了有限元法、光滑粒子法以及有限元与光滑粒子耦合算法的研究现状及应用进展,并讨论了各方法中需要进一步解决的问题.最后通过算例对3种方法的计算精度和计算效率进行了分析,供研究人员参考.

一种光滑粒子流体动力学-有限元法转换算法及其在冲击动力学中的应用

为了解决冲击动力学中的大变形问题,提出了一种光滑粒子流体动力学-有限元法(SPH-FEM)转换算法,以等效Mises应力作为转换判据,将冲击过程中局部大变形区域的有限元网格转换为SPH粒子.该算法在大变形区域使用具有优势的SPH,在小变形区域使用精度和效率更高的FEM,为冲击动力学问题的数值计算提供了一条有效途径.使用SPH-FEM转换算法对圆柱形钢弹正冲击钢板发生冲塞破坏的过程进行了三维数值计算,计算结果与实验吻合较好,显示了该算法在计算精度方面的优势.在实际工程中,需要根据具体材料的失效模式,选择更加合适的转换判据.

采用有限元和粒子群算法优化特高压复合绝缘子均压环结构

交流1 000kV同塔单回输电线路中采用的是V串和I串复合绝缘子,其分裂导线、连接金具和均压环等结构较为复杂,电场强度较高,易发生电晕,因此需对均压环结构进行优化研究。运用有限元分析软件ANSYS建立3维模型进行电场计算,同时采用ANSYS参数化编程语言(ANSYS parametric design language,APDL)实现了粒子群(particle swarm optimization,PSO)算法。对因模型结构复杂导致的计算量增加问题,运用ANSYS中的Submodeling技术有效减少了计算量,且能达到较高精度。研究结果表明:V串均压环的最大场强比I串约高出13.9%,V串绝缘子沿面最大场强比I串高出约29.2%,且结合有限元法(finite element method,FEM)与PSO算法,在ANSYS计算环境下能较好实现V串和I串复合绝缘子均压环结构的多参数优化。研究成果可使均压环和绝缘子沿面最大场强满足控制要求,挂网运行后未出现电晕。此方法能较好解决大场域、多介质复杂模型的结构优化问题,对粒子群智能算法在绝缘结构优化设计领域的研究具有参考价值。