多孔紊动射流的数值模拟与实验研究进展

多孔射流作为流体运动的一种重要类型,在工程中具有非常广泛的用途。由于多孔射流各股水流之间的卷吸和掺混的存在,增加了流场的复杂性。长期以来研究人员从理论分析、实验测量和数值模拟方面对多孔紊动射流进行了大量的研究工作,目前已经认识了流场中的许多流动特性和流动机理。从数值模拟和实验研究两个方面,比较分析了国内外多孔紊动射流的研究现状和研究结果,评述了不同湍流模型以及不同实验测量方法对紊动射流的预测能力,讨论了存在的问题并对该领域的研究方向进行了展望。     

土壤吸湿-湿过程中水力传导度的拟合研究

非饱和含水介质在吸湿和脱湿的不同过程中,水力传导度随介质含水率的变化规律是不同的。本文选取大沽河流域两种代表性砂土,对其吸湿-脱湿两个过程分别进行试验观测,并使用VanGenuchten—Mualem模型对其吸湿-脱湿过程进行水力传导度的拟合。研究结果表明：砂土在吸湿过程的非饱和水力传导度与脱湿过程的非饱和水力传导度大小并不相同,在含水率相同的情况下,吸湿过程的水力传导度要比脱湿过程大,在含水率较低时（小于15％）相差可达1个数量级。     

多尺度空间线状实体形状相似关系的表达与度量

本文对空间数据多尺度表达中线状实体综合前后的形状相似性进行了描述、分析和研究。通过调查统计,得出了衡量线状实体形状相似程度的两个重要参量SIMⅠ、SIMⅡ,基于这两个参量给出了空间线状实体形状相似性的定义以及在不同尺度下线状实体形状相似性的度量方法,并用实例进行验证。为空间数据多尺度表达时线状实体的形状表达提供了一种分析和评价手段。     

城市公共卫生安全应急管理系统技术集成研究

从国内城市公共卫生安全应急管理系统的研究和应用现状出发,指出城市公共卫生安全应急管理系统建设的核心要务是集成现有的卫生信息系统、消除卫生信息孤岛、实现数据共享与交换;并提出了城市公共卫生安全应急管理系统数据采集类型和业务支持部门。在此基础上,构建了一个基于数据仓库、以GIS为应急响应支持平台、能统一管理信息、可与其他电子政务系统互连互通的城市公共卫生安全应急管理系统框架结构,并详细阐述了各业务模块的基本功能和系统实现的关键技术方法。     

一种时空数据静态可视化表达方法

本文简要介绍了时空数据可视化的必要性,以及目前人们采用的时空数据可视化方法,并分析了这些方法的局限性,提出了一种将年轮(annual ring)作为时空载体,结合视觉变量对面状地物的属性数据进行静态可视化的方法。该方法将面状地物的同心环按照类似树木年轮的生长特征进行划分,作为空间和时间的载体,不同时期属性数据映射到相应的同心环中,同心环的厚度和色彩的差异揭示了属性数据的变化。最后以武汉市城市管理网格化平台的统计评价数据为例实现了对比率量表数据的时态可视化表达。本文所提出的静态时空可视化方法将面状地物的空间、属性和时间紧密地结合在一起,克服了以往静态可视化方法中时间-空间、时间-属性相分离的不足,试验证明了这种方法在实践中的可行性。     

空间数据重复记录的清理方法研究

在空间数据的更新过程中,需要利用多种数据源对现有数据进行更新,这样可能会导致在获得的空间数据中出现重复记录。利用计算机自动对这些重复记录进行检查处理,将会极大地降低数据生产者的工作量。本文根据在数字制图系统研制过程中所遇到的此类问题,提出了对重复空间数据进行检查与处理的方法,并在所研制的数字制图系统中得到了应用。     

国土资源数据分析及其整合与集成

本文详细分析了国土资源数据特点、综合数据来源、产生方式和使用方法等方面,对数据进行了分类,比较了不同的数据格式。在此基础上,阐述了国土资源数据的集成框架,讨论了数据整合和集成的方法和流程,提出了数据整合与集成的三种框架问题。     

Using Topological Relationships to Inform a Data Integration Process

When spatial datasets are overlaid, corresponding features do not always coincide. This may be a result of the datasets having differing quality characteristics, being captured at different scales or perhaps being in different projections or datums. Data integration methods have been developed to bring such datasets into alignment. Although these methods attempt to maintain topological relationships within each dataset, spatial relationships between features in different datasets are generally not considered. The preservation of inter‐dataset topology is a research area of considerable current interest. This research addresses the preservation of topology within a data integration process. It describes the functional models established to represent a number of spatial relationships as observation equations. These are used to provide additional information concerning the relative positions of features. Since many topological relationships are best modelled as inequalities, an algorithm is developed to accommodate such relationships. The method, based on least squares with inequalities (LSI), is tested on simulated and real datasets. Results are presented to illustrate the optimal positioning solutions determined using all of the available information. In addition, updated quality parameters are provided at the level of the individual coordinate, enabling communication of local variation in the resultant quality of the integrated datasets.     

Modelling Positional Uncertainty of Line Features by Accounting for Stochastic Deviations from Straight Line Segments

The assessment of positional uncertainty in line and area features is often based on uncertainty in the coordinates of their elementary vertices which are assumed to be connected by straight lines. Such an approach disregards uncertainty caused by sampling and approximation of a curvilinear feature by a sequence of straight line segments. In this article, a method is proposed that also allows for the latter type of uncertainty by modelling random rectangular deviations from the conventional straight line segments. Using the model on a dense network of sub‐vertices, the contribution of uncertainty due to approximation is emphasised; the sampling effect can be assessed by applying it on a small set of randomly inserted sub‐vertices. A case study demonstrates a feasible way of parameterisation based on assumptions of joint normal distributions for positional errors of the vertices and the rectangular deviations and a uniform distribution of missed sub‐vertices along line segments. Depending on the magnitudes of the different sources of uncertainty, not accounting for potential deviations from straight line segments may drastically underestimate the positional uncertainty of line features.