On the sensitivity of computed surges to open-boundary formulation

A coarse-grid (resolution of order 7 km) model of the west coast of Britain is used to examine the sensitivity of computed storm-surge elevations and currents to a range of open-boundary conditions. The storm-surge period 1 to 26 March 1994 is used for this comparison, as it is a time of significant wind activity. Also current measurements in the North Channel of the Irish Sea together with coastal elevation measurements are available for model validation. Elevations and currents previously computed with a coarse-grid shelf-wide model can also be incorporated into the open-boundary condition to examine the influence of far-field effects. Initial model calculations with no far-field input show the importance of including shelf-wide effects from either the external shelf model, or by using observations from coastal gauges interpolated along the open boundary of the west-coast model. Provided the west-coast models open boundary is taken sufficiently far away from the region of interest, in this case the Irish Sea, then either a radiation condition or an elevation-specified condition is appropriate provided far-field effects are taken into account. If these are not included, then neither boundary condition is successful. For the radiation condition it is necessary to include both elevations and currents from a far-field model in order to reproduce the surge. In the case of an elevation-specified boundary condition far-field effects can be incorporated in hindcast calculations by including observed sea-level changes. In a storm-surge prediction calculation the radiation condition with a far-field model is required. Calculations show that computed elevations are spatially more coherent than currents, with flows through the western Irish Sea showing the greatest sensitivity to open-boundary formulation during storm events.Responsible Editor: Phil Dyke     

基于VisIt的全球科学数据并行可视化——以大气温度场为例

针对当前大规模全球科学数据可视化中存在的单机可视化数据量有限、从底层开发并行可视化系统难度大等问题,该文基于分布式环境和VisIt,提出了一种简便、开放而又有效的大规模全球科学数据可视化方法。介绍了VisIt的体系结构及运行机制,给出了自定义数据的并行可视化方法;并基于NCEP数据集及全球空间格网,在小规模集群环境下实现了小粒度适应性球体退化八叉树格网(SDOG)下的全球大气温度场的并行可视化。VisIt的并行可视化性能测试结果表明:通过增加计算节点,VisIt能有效摆脱传统单机可视化对数据量的限制,可实现大规模全球科学数据的并行可视化。     

DEM与地形分析的并行计算

总结了数字高程模型构建、特征提取等并行算法的研究进展,概述了不同并行算法的主要内容;探讨了DTA并行技术在海量地形数据可视化和高性能地学计算的应用,随着DEM的需求日益增大,高精度、高分辨率DEM产品及其附加服务也逐步产品化。最后,通过分析并行计算发展的关键问题,提出DTA并行技术的研究趋势及研究意义,合适的数据划分和结果融合策略、通用并行算法、容错机制和负载均衡策略的设计是今后研究的重要内容,尤其是如何在多种计算模式共同发展的背景下利用并行计算解决地学难题,从而得到更接近现实世界地理环境的模拟,并扩大数字地形分析的应用范围。     

求解半变异函数的常用方法与新方法研究

半变异函数的获取既有人为主观求取法,又有运用数学工具客观求取法,但是后者是今后的主要趋势。笔者在前人工作的基础上,归纳出多项式回归法、线性规划法和目标规划法三种求解半变异函数的成熟方法,以及非线性回归理论直接求解法、进化计算和积分面积三种新兴方法,并总结出各种方法的优缺点。最后概括出计算权重的两种方法。     

浅议基于像素工厂的ADS 80影像处理技术

摄影测量进入数字时代以来,ADS80因其独特的优势逐步成为航摄影像获取的主流平台,对ADS80数据后续快速处理也成为目前数据处理软件研究的重点。本文介绍了基于像素工厂系统快速处理ADS80数据的技术流程和数据处理的主要技术环节,总结了像素工厂系统数据处理的优劣势。通过实际项目实践,验证了基于像素工厂系统处理ADS80数据的可靠性与高效性。     

多作业管理方式在风能资源数值模拟中的应用

通过对MM5和CALMET风能资源数值模拟耦合模式的计算流程分析,基于并行运算思想,设计了MM5和CALMET耦合模式模拟运算的多作业管理方式。在浙江省风能资源高分辨率数值模拟试验中,完成浙江区域1个月时间段的风能资源参数模拟运算,MM5和CALMET耦合模式在实施多作业管理方式前后,CALMET模式的运算时间由原来的1501.2 min缩短为149.5 min,运算时效提高了9倍;整个耦合模式的运算时间由原来的1709.9 min缩短为358.2 min,运算时效提高了4倍。数值模拟试验证实了多作业管理方式可在现有计算资源的基础上,大幅提高数值模式的运算时效,且随着数值模式模拟时间段的加长和模拟区域范围的扩大,多作业管理方式对数值模式运算功效的增强越加明显。     

基于GPGPU的全波形并行分解算法

针对EM(Expectation Maximization)波形分解算法具有多次迭代和大量乘、除、累加等高密集运算的特点,提出一套将EM算法在通用计算图形处理器GPGPU上并行化的方案。针对通用并行计算架构CUDA的存储层次特点,设计总体的并行方案,充分挖掘共享存储器、纹理存储器的高速访存的潜能;根据波形采样值采用字节存储的特征,利用波形采样值的直方图求取中位数,从而降低求噪音阈值的计算复杂度;最后,采用求和规约的并行策略提高EM算法迭代过程中大量累加的计算效率。实验结果表明,当设置合理的并行参数、EM迭代次数大于16次、数据量大于64 M时,与单核CPU处理相比,GPU的加速比达到了8,能够显著地提高全波形分解的效率。     

CYS-300型全液压地热水井钻机的研制

本文介绍了CYS300型全液压地热水井钻机的性能参数、关键机械部件结构及液压系统的设计。钻机选用齿轮泵作为液压系统动力元件,配合液压多路阀实现对动力头双液压马达和给进油缸等执行元件液压油的合流,通过液控二速阀实现动力头双液压马达的串并联工作,输出4种转速和扭矩,在满足钻机性能参数设计要求的同时,简化液压系统的设计,减少液压系统能量损耗,降低维护和使用成本。钻机采用油缸三链条倍速机构实现动力头的给进提升,具有结构简单、性能可靠、抗冲击能力强、提升平稳等优点。现场工程施工应用表明：CYS300型全液压地热水井钻机性能稳定可靠,工艺和复杂地层适用能力强,操作安全舒适,钻进效率高。     

Leveraging parallel spatio-temporal computing for crime analysis in large datasets: analyzing trends in near-repeat phenomenon of crime in cities

ABSTRACT

Crime often clusters in space and time. Near-repeat patterns improve understanding of crime communicability and their space–time interactions. Near-repeat analysis requires extensive computing resources for the assessment of statistical significance of space–time interactions. A computationally intensive Monte Carlo simulation-based approach is used to evaluate the statistical significance of the space-time patterns underlying near-repeat events. Currently available software for identifying near-repeat patterns is not scalable for large crime datasets. In this paper, we show how parallel spatial programming can help to leverage spatio-temporal simulation-based analysis in large datasets. A parallel near-repeat calculator was developed and a set of experiments were conducted to compare the newly developed software with an existing implementation, assess the performance gain due to parallel computation, test the scalability of the software to handle large crime datasets and assess the utility of the new software for real-world crime data analysis. Our experimental results suggest that, efficiently designed parallel algorithms that leverage high-performance computing along with performance optimization techniques could be used to develop software that are scalable with large datasets and could provide solutions for computationally intensive statistical simulation-based approaches in crime analysis.