Numerical Investigation of Lid-Driven Deep Cavity with Local Grid Refinement of MRT-LBM

In the case of lid-driven deep cavity flow, the effects of different resolutions of local grid refinement have been studied in the frame of multiple relaxation times (MRT) lattice Boltzmann method (LBM). In all the cases, the aspect ratio and Reynolds number are set as 1.5 and 3.200, respectively. First, the applied method is validated by comparing it with two reported works, with which agreements are reached. Then, six separate degrees of local grid refinement at the upper left corner, i.e. purely coarse grid, including 1/64, 1/32, 1/16, 1/8, 1/4 refinements of the lattice number in the width direction have been studied in detail. The results give the following indications:① The refinement degrees lower than 1/8 produce similar results;② For single corner refinement, 1/4 refinement is adequate for clearing the noises in the singularity zone to a large extent;③ New noise around the interface between coarse and fine zones are introduced by local grid refinement. Finally, refinement of entire subzone neighboring the lid is examined to avoid introducing new noises and it has been found effective.

An Adaptive Local Grid Nesting-based Genetic Algorithm for Multi-earth Observation Satellites' Area Target Observation

The Scheduling of the Multi-EOSs Area Target Observation(SMEATO)is an EOS resource schedul-ing problem highly coupled with computational geometry.The advances in EOS technology and the ex-pansion of wide-area remote sensing applications have increased the practical significance of SMEATO.In this paper,an adaptive local grid nesting-based genetic algorithm(ALGN-GA)is proposed for developing SMEATO solutions.First,a local grid nesting(LGN)strategy is designed to discretize the target area into parts,so as to avoid the explosive growth of calculations.A genetic algorithm(GA)framework is then used to share reserve information for the population during iterative evolution,which can generate high-quality solutions with low computational costs.On this basis,an adaptive technique is introduced to determine whether a local region requires nesting and whether the grid scale is sufficient.The effectiveness of the proposed model is assessed experimentally with nine randomly generated tests at different scales.The results show that the ALGN-GA offers advantages over several conventional algorithms in 88.9%of instances,especially in large-scale instances.These fully demonstrate the high efficiency and stability of the ALGN-GA.

Local grid refinement in multigrid method for point contact problems of polycrystalline anisotropic material under dry and lubricated conditions

Predicting rolling bearing fatigue life requires knowledge of the three-dimensional(3D)stress fields in the roller and raceway near the lubricated contact.Owing to the increasingly severe operating conditions,the effect of localized features such as surface roughness,subsurface inclusions,and even the crystallographic structure of the material becomes important.Achieving such detail requires(locally)extremely dense gridding in simulations,which in 3D is a major challenge.Multigrid techniques have been demonstrated to be capable of solving such problems.In this study,multigrid techniques are shown to further increase the efficiency of the solution by exploiting local grid refinement while maintaining the simplicity of a uniform discretization.This is achieved by employing increasingly finer grids only locally,where the highest resolution is required.Results are presented for dry contact and elastohydrodynamically lubricated contact cases,circular as well as elliptic,with varying crystallographic structure,and with surface roughness.The results show that the developed algorithm is very well suited for detailed analysis,with also excellent prospects for computational diagnostics involving actual material crystallographic structure from electron backscatter diffraction measurements.

Local and parallel finite element algorithms based on two-grid discretization for steady Navier-Stokes equations

Local and parallel finite element algorithms based on two-grid discretization for Navier-Stokes equations in two dimension are presented. Its basis is a coarse finite element space on the global domain and a fine finite element space on the subdomain. The local algorithm consists of finding a solution for a given nonlinear problem in the coarse finite element space and a solution for a linear problem in the fine finite element space, then droping the coarse solution of the region near the boundary. By overlapping domain decomposition, the parallel algorithms are obtained. This paper analyzes the error of these algorithms and gets some error estimates which are better than those of the standard finite element method. The numerical experiments are given too. By analyzing and comparing these results, it is shown that these algorithms are correct and high efficient.

Exact local refinement using Fourier interpolation for nonuniformgrid modeling

Numerical solver using a uniform grid is popular due to its simplicity and low computational cost, but would be unfeasible in the presence of tiny structures in large-scale media. It is necessary to use a nonuniform grid, where upsampling the wavefield from the coarse grid to the fine grid is essential for reducing artifacts. In this paper, we suggest a local refinement scheme using the Fourier interpolation, which is superior to traditional interpolation methods since it is theoretically exact if the input wavefield is band limited.Traditional interpolation methods would fail at high upsampling ratios(say 50); in contrast, our scheme still works well in the same situations, and the upsampling ratio can be any positive integer. A high upsampling ratio allows us to greatly reduce the computational burden and memory demand in the presence of tiny structures and large-scale models, especially for 3D cases.

Requirements and Architecture Concept for a Data Processing System in Local Area Smart Grid

WINNER, a German research project, integrates local photovoltaic systems, charging infrastructure for electric vehicles and tenant households, focusing devices with and without smart grid abilities. The project＇s goal is to manage the local power grid operations in a way that allows locally produced energy to be consumed locally. This local optimised consumption is done by using currently available devices. Further, we want to analyse accruing data streams and optimise the usage of local devices to manage this time-base shifted consumption scenario by implementing a non-hard real-time processing system. In this paper, we outline the project＇s primary objectives from a technical point of view. First, we present ＂Wohnnngswirtschaftlich integrierte netzneutrale Elektromobilitat in Quartier und Region＂ （WINNER） and some related research projects. We describe the integration tasks, the data sources, and sinks. So, a project overview can be given. Afterwards, we compare our approach and already developed technologies. Requirements are derived from the system overview. According to them, we can outline an architectural View of the core component of data stream processing within this scenario. Finally, the results are discussed, and consequences are drawn.

An Efficient Grid Service Discovery Mechanism Based on the Locality Principle

With the explosion of services in grid environment, it＇s necessary to develop a mechanism which has the ability of discovering suitable grid services efficiently. This paper attempts to establish a layered resource management model based on the locality principle which classifies services into different domains and virtual organizations （VOs） according to their shared purposes. We propose an ontologybased search method applying the ontology theory for characterizing semantic information. In addition, we extend the UD- D1 in querying, storing, and so on. Simulation experiments have shown that our mechanism achieves higher performance in precision, recall and query response time.

基于自适应字典校正的稀疏恢复STAP算法

空时自适应处理(Space-Time Adaptive Processing,STAP)技术在时间维(脉冲维)和空间维(阵元维)联合进行信号处理,以实现动目标检测功能。但是,传统STAP技术的计算复杂度非常高,而且在优化处理信号过程中需要大样本的支撑,在实际的工作场景中,杂波环境复杂易变,不易获取足够多的独立同分布样本,因此杂波抑制效果较差。稀疏恢复空时自适应处理(Sparse Recovery Space-Time Adaptive Processing,SR-STAP)算法可以利用很少的训练样本实现杂波抑制,但大多数SR-STAP算法的计算量巨大,运行速度慢,算法实时性不高。此外,SRSTAP算法需要对连续空时二维平面进行离散化处理,将空时二维平面划分为很多细小的网格,由于真实的杂波在空时平面上是连续分布的,同时考虑雷达接收信号中噪声、系统参数误差等因素的影响,真实杂波点与离散化网格点之间一定存在着偏差,会造成网格失配现象,导致SR-STAP算法杂波抑制性能下降。针对此问题,本文提出了基于自适应字典校正的稀疏恢复STAP算法。该算法首先通过子空间投影法筛选出与杂波最相关的原子;然后围绕选定原子由粗到细进行自适应局部网格划分,按照局部网格迭代选优准则,不断调整选择局域内的最优原子,直到满足迭代终止条件,以匹配真实的杂波点;最后利用选定的最优原子对应的空时导向矢量构造杂波子空间,更新噪声子空间上与杂波子空间正交的投影矩阵得到STAP权值。仿真实验表明,所提算法与传统SR-STAP算法相比,具有更高的稀疏恢复精度,更快的运行速度,改善了STAP性能。

不均匀积雪致大跨网架结构局部损伤分析

网架结构管球连接处局部损伤是此类结构的重要安全隐患。在风致雪漂移引起的不均匀积雪荷载作用下,局部损伤会加剧,最终导致结构局部破坏。因此很有必要分析不均匀积雪荷载作用下带伤服役网架结构局部损伤劣化情况。采用CFD数值模拟技术,以一正放四角锥网架结构为研究背景,分析了在90°风向角、12 m/s风速下,持续降雪24 h中网架结构屋面不均匀积雪分布的变化情况,并建立可表征网架结构管球连接处存在裂纹损伤的多尺度模型,分析了网架结构节点存在不同裂纹尺寸局部损伤时,不均匀积雪致网架结构局部损伤劣化程度。结果表明:网架结构风致积雪不均匀程度非常显著,而且当网架结构管球连接处存在局部裂纹损伤时,在降雪中后期,有管球连接损伤的节点大多都出现了不同程度的裂纹扩展,节点为最不利分布的穿透型裂纹时,裂纹扩展最大为15.64 mm。说明在持续特大降雪这种极端荷载作用下,带伤服役网架结构局部损伤将进一步加剧,危及结构使用安全。

加固后钢筋混凝土十字形柱斜向抗震性能研究

钢筋混凝土十字形柱的斜向抗震性能是影响结构安全性能的重要因素。文章设计了3种新型约束补强方式加固的钢筋混凝土十字形柱试件,在验证有限元建模方法有效性的基础上模拟了斜向低周往复加载,通过分析试件的滞回曲线、骨架曲线、承载力和延性等抗震性能指标研究了试件的斜向抗震性能,并参考相关规范得出了新型约束补强方式十字形柱的压弯承载力计算公式。结果表明:钢筋网格局部加强使试件的斜向峰值位移提高了7.4%~19.4%;柱端嵌贴碳纤维布(Carbon Fiber Reinforced Polymer,CFRP)板条会使试件峰值承载力、延性系数分别提高了约29.2%~34.2%和7.5%~22.9%;柱端嵌贴CFRP板使试件斜向峰值承载力、延性系数分别提高了约99.9%~105.5%和17.4%~24.8%。3种加固方式均可改善钢筋混凝土十字形柱的斜向抗震性能,改善程度由大到小依次为柱端嵌贴CFRP板、柱端嵌贴CFRP板条和钢筋网格局部加强。

VSLAM系统中的快速特征点匹配算法

连续图像帧之间特征点匹配是视觉同步定位与建图(VSLAM)技术的关键技术;针对VSLAM系统中连续图像帧之间特征点匹配耗时且精度低的问题,一种基于局部像素运动模型的快速特征点匹配算法(LPMM)被提出;该算法基于运动平滑约束假设,利用连续图像帧中局部区域内的像素点的运动具有一致性这一特性,将图像划分成局部网格区域后利用每个网格中的部分特征点估算该网格区域的运动向量,并以此为基础,计算下一帧匹配特征点的搜索范围的中心点;最终在计算出下一帧的中心点的局部邻域内搜索与当前帧的特征点相匹配的特征点;实验结果表明,所提出的匹配算法与广泛使用的ORB-SLAM2中的投影匹配算法相比,平均匹配速度提高50%以上,匹配精度提升了约4%。

图像失真条件下的模板匹配算法研究

传统模板匹配算法在图像出现局部遮挡、形变等失真情况时适用性不高,为提高图像失真时的匹配性能,提出一种基于等距有效点的局部模板匹配算法。首先,利用局部图像网格处理方法得到候选局部特征向量;然后,根据对应坐标位置的局部相似度提出一种等距有效点的判定方法;最后,通过归一化等距有效点数量及其相似度得到综合相似度。利用局部相似度对失真部分进行筛除,从而使得参与综合相似度计算部分的特征值更准确,进一步增大了综合相似度的真实性和可靠性。所提算法在原始图像集和干扰图像集中的匹配率均为100.0%,在遮挡图像集和形变图像集中则分别为94.1%和83.3%。实验结果表明该算法在图像失真条件下具备较强的抗干扰性,且对噪声和光照变化也具备鲁棒性。

基于LGR模拟裂缝的页岩油藏注CO_(2)吞吐开发参数优化

页岩油商业化开采需要进行大规模水力压裂来建立高渗流通道,从而提高采收率。但压裂后裂缝易闭合,地层压力衰竭快,导致采收率低。CO_(2)吞吐开发技术能够有效补充地层能量,改善原油流动性,对提高页岩油藏开发效果有极大潜力。不同的CO_(2)吞吐开发参数会对页岩油的采收效果产生影响。采用基质-人工裂缝-天然裂缝的多重介质模型,并结合局部网格加密(LGR)来模拟人工裂缝,以提高模拟精度与效率。基于实例研究,对注气总量、注气速度、焖井时间、吞吐轮次等主要参数进行了优化,以提高CO_(2)吞吐开发的效益。研究结果显示,多重介质数值模拟方法结合CO_(2)吞吐开发可以有效预测生产效果,并优化开发参数,为提高页岩油藏CO_(2)吞吐开发效益提供有力的依据。

基于改进GSA-GWO算法的含分布式电源局部电网孤岛划分策略

为进一步提高局部电网在故障恢复过程中的高效性和可靠性,文章提出了一种基于改进GSA-GWO算法的局部电网故障下孤岛划分策略。首先,采用最优-最劣法对负荷进行评价,得到负荷权重值,从而确定局部电网故障下孤岛划分重要负荷恢复优先级;其次,结合负荷优先级确定负荷等级权重系数值,构建含分布式电源的局部电网孤岛划分目标函数模型;再次,为了获取更佳的目标函数求解结果,引入混沌反向学习和遗传退火算法(Genetic Simulated An-nealing,GSA)对灰狼优化算法(Grey Wolf Optimization,GWO)进行改进,以提高算法的优化性能;最后,以修改后的IEEE 69节点为例进行仿真分析,运用改进的GSA-GWO算法对局部配电网故障模型进行求解,得到更佳的孤岛划分结果。算例分析表明,文章提出的策略可准确实现局部电网故障下孤岛划分最优策略,保证了重要负荷的电力供应,验证了策略的有效性和优越性。

Grid-Scan算法定位精度和定位率的改进

针对Grid-Scan算法定位率及定位精度较低的问题,提出了一种基于虚拟锚节点的Grid-Scan定位算法。具体做了3个方面的工作:有邻居锚节点的未知节点只利用邻居锚节点进行定位,定位后的节点升级为虚拟锚节点;没有邻居锚节点的未知节点利用虚拟锚节点完成定位;锚节点及虚拟锚节点与未知节点采用不同的通信半径进行扫描并完成定位。仿真结果表明,相比传统算法,改进算法的定位精度和定位率分别平均提高了约6.35%和23.37%。

Grid-Scan定位算法的虚拟锚节点策略改进

针对无线传感器网络中Grid-Scan算法定位精度较低的问题,提出了一种基于虚拟锚节点策略的Grid-Scan定位算法。具体做了三个方面的工作:对未知节点设置可定位阈值,邻居锚节点数大于可定位阈值的未知节点使用Grid-Scan算法进行定位,定位后的节点升级为虚拟锚节点;邻居锚节点数小于可定位阈值的未知节点利用极大似然法完成定位,定位后的节点升级为虚拟锚节点;锚节点及虚拟锚节点共同参与对剩余未知节点的定位。仿真结果表明,改进算法在不同锚节点密度、不同通信半径和不同栅格大小的网络中以及通过不规则传播模型后都具有较好的定位精度。

基于大脑海马认知机理的主从式AUV协同定位方法

针对AUV协同定位系统中水声测距信息存在大量野值的问题,提出一种基于哺乳动物大脑海马体空间细胞认知机理启发的主从式AUV协同定位方法。首先,利用二维连续吸引子网络模型对网格细胞进行建模,并基于从艇速度与航向信息实现路径积分;其次,引入竞争性Hebb网络对多个网格细胞板自组织编码,从而基于多尺度网格细胞板加权生成具有单个放电活动包的位置细胞板;最后,将主从艇之间的水声测距量测信息转化为位置细胞放电活动,并利用其对位置细胞放电进行更新,从而校正从艇路径积分累积误差。由于位置细胞能够通过多次放电更新逐渐逼近真实位置,从而可以有效降低单一量测野值对于系统定位精度的影响。仿真实验结果表明,在水声测距量测噪声含有大量野值点而不满足高斯分布的情况下,所提出的类脑协同定位算法对量测噪声野值具有更高的鲁棒性,与有阈值判断的扩展卡尔曼滤波(EKF)协同定位算法相比定位精度提高28%。

基于局部信息熵的智能电网数据离群点识别

识别智能电网数据离群点能够准确地确定智能电网数据对象,但是目前提出的识别方法识别精准度较差,识别效率较低。为此,基于局部信息熵研究了一种新的智能电网数据离群点识别方法,通过方差定义选取优选空间,确定优选空间集阈值进行数据加权处理,计算优选空间信息熵;通过无量化处理判断临界点数值,得到子空间最优范围。检索与分析离群状态变化情况,计算离群因子,根据概率值增强算法对冗余数据的甄别能力,删除离群点冗余数据,将平均值计入值域,实现智能电网数据离群点识别。实验结果表明,所研究方法精准度最终能够达到95%,识别效率在90%以上。

500kV干式并联电抗器技术在巴西的实践与意义

随着巴西高压电网的快速发展,电力系统对并联电抗器的性能和可靠性提出了更高要求。本研究深入探讨了500kV干式并联电抗器技术在巴西的引入与应用过程。通过系统分析巴西电力技术标准,研究团队制定了符合当地要求的技术规范,成功克服了技术标准差异、气候环境适应性等挑战,并在希尔瓦尼亚变电站实现了该技术的首次应用。实践结果表明,500kV干式并联电抗器在损耗控制、噪声抑制和环境适应性等方面表现优异,为巴西电网的稳定运行作出了重要贡献。本研究不仅为干式并联电抗器技术在新兴市场的推广提供了范例,也为中国电力装备技术的海外应用和标准的制定积累了宝贵经验。

深圳坚强局部电网的建设方法研究

分析现有技术标准和网络结构下的电网抗灾防灾能力及存在问题,研究深圳坚强局部电网的建设方法。