颗粒粒度检测技术综述

文章对涉及各个领域的粒度检测方法进行了总结,介绍了机械法、波动特性法、电感应法等传统颗粒粒度测量技术的方法和原理,并介绍了色谱法、质谱法、数字图像处理测量法等近年来发展起来的颗粒粒度测量新方法.     

基于截面显微法的光学材料亚表面损伤检测

针对光学材料亚表面损伤的截面显微法检测进行了深入研究.介绍了截面显微法的基本原理以及样品制备和检测工艺流程,并对检测结果进行了分析,得出了亚表面裂纹深度与磨料粒度之间的比例关系.     

耐火材料真密度检测的分析研究

通过试验研究了试样种类、试样粒度、选用的试验液体、容器内的真空度以及试验温度对耐火材料真密度检测蛄果的影响．并对检测数据进行了分析．表明采用适当的检测方法可以确保检测结果的准确性。     

粉体生产流程中粒径检测的方法研究

粉体颗粒常见的测试方法和仪器有直接观察法、颗粒图像处理仪、沉降法、库尔特粒度仪、激光粒度仪等。这些检测方法具有各自的优缺点,但都不能实现粉体生产流程中的在线检测。以微米级粉体在线粒度检测系统为例,阐述了粉体粒径在线检测相比于离线检测所具有的实时性、连续性及智能性的重要特点。因此,在现代的粉体生产过程中如能实现在线检测则为提高粉体加工质量、节能降耗及环境保护都具有十分重要的意义。     

样品粉碎粒度不同对挥发油提取作用的影响研究

对水蒸气蒸馏提取挥发油过程中样品粉碎粒度不同的影响进行了研究．以花椒为样品,对其不同粉碎粒度的挥发组分个数、挥发组分检出率及挥发油提取率进行了实验．未粉碎的花椒挥发油提取率为3．52％,粉碎粒度为20目花椒挥发油提取率为5．40％,粉碎粒度为40目花椒挥发油提取率为8．08％,粉碎粒度为80目花椒挥发油提取率为6．28％．结果表明,样品粉碎粒度对挥发油的提取作用有较大影响．     

用激光衍射法实现颗粒细度的测量技术

P232位浮点DSP为核心,用于在线检测装置的颗粒细度测量,原理为激光衍射法,通过DSPTMS320C6711的高速运算对采集到的光能进行拟合运算,得到粒度的分布,并对测量的原理和系统软、硬件的构成进行了阐述。     

MS2000激光粒度分析仪在沉积物分析中的应用

介绍了MS2000激光粒度分析的基本原理及结构组成,以及粒度分析前对沉积物样品进行预处理的方法,总结了近年来粒度分析方法在海洋地质学、第四纪地质学、古气候与古海洋学等研究领域的应用,并对其应用前景进行了预测。     

不同粒级炼焦煤的粘结指数变化研究

对四种炼焦煤进行不同粒度等级的筛分。通过对比不同粒度等级条件下的粘结指数变化情况,结合四种炼焦煤的结焦过程行为,对粒度这一煤的物理指标对炼焦煤质量的影响效果进行探索。结果显示不同煤的结焦过程行为对粒度的影响,大于单一煤种的粒度变化本身,如果考虑原煤的粒度等级之间的数量差值,可以考虑适当增加3mm以上的炼焦煤的量,发挥其粒度调整对某几类煤种的粘结能力的改善作用。     

Mastersizer 2000分析报告解析

结合Mastersizer 2000激光粒度分析仪测量实践和实例,对典型的粒度分析报告的栏目内容和参数特征进行了较深入的解析,为激光粒度仪的应用与结果分析提供有益的参考。     

用激光粒度测量法测定煤粉粒度时分散条件的选择

用激光粒度测量法对3种煤粉(其中包括烟煤、无烟煤、贫煤)在多种分散条件下进行了粒度测试，发现分散条件的改变对实验结果影响较大，阐明了分散条件的选择对煤粉激光粒度测量的重要性。     

一种基于粒度的聚类算法

聚类和粒度具有天然的相通性,本文探讨了基于粒度聚类算法的一般框架,并基于该框架,研究了一种基于网格密度的文本聚类算法,最后以例证说明这一方法的可行性。     

基于业务场景的用例粒度划分范式

用例驱动整个统一软件开发过程,但用例划分缺乏统一标准规范,从而导致用例划分不够准确。针对该问题,以业务场景为基础,对用例粒度划分展开研究,提出采用3种范式规范用例粒度划分。从用例划分源头、建模阶段及实际工程规模展开进行分析,3种范式为建模人员在具体业务场景下的用例划分提出解决方案,可为建模人员节省建模时间、提升建模效率,从而完善系统架构。     

关于沙尘暴沙粒在高空运动的数学模型及分析

文章从物理学的流体力学出发,运用相关的因素进行分析,建立沙尘暴颗粒扩散的数学模型,分析了沙尘颗粒扩散的内在因素,并且试图寻找减少沙尘暴危害的方法。     

煤系硫铁矿处理含汞废水的研究

本文利用煤系硫铁矿对含汞废水的处理进行了研究.实验证明,废水的pH值、所用煤系硫铁矿矿石的粒度、反应温度、吸附时间及搅拌方式对汞的去除率有显著的影响.用还原铁粉对煤系硫铁矿(粒径为0.08mm)进行改性后,去除效果明显提高;当还原铁粉与煤系硫铁矿的配比为40:100、处理温度为200～250℃时,除汞效果最好.     

Dynamic Distribution Model with Prime Granularity for Parallel Computing

Dynamic distribution model is one of the best schemes for parallel volume rendering. However, in homogeneous cluster system, since the granularity is traditionally identical, all processors communicate almost simultaneously and computation load may lose balance. Due to problems above, a dynamic distribution model with prime granularity for parallel computing is presented. Granularities of each processor are relatively prime, and related theories are introduced, A high parallel performance can be achieved by minimizing network competition and using a load balancing strategy that ensures all processors finish almost simultaneously, Based on Master-Slave-Gleaner （ MSG） scheme, the parallel Splatting Algorithm for volume rendering is used to test the model on IBM Cluster 1350 system. The experimental results show that the model can bring a considerable improvement in performance, including computation efficiency, total execution time, speed, and load balancing.     

Learning and teaching: Differentiation and relation

In this paper we discuss the articulation between teaching and learning, how to differentiate them and how to establish relations between them, limiting ourselves to aspects dealing with knowledge. The aim is to allow the design of teaching situations more relevant for learning. The characteristics differentiating teaching and learning are used to analyse research studies relative to two time scales, one of the order of months or years and the other of the order of hours. The comparison shows the importance of the grain size chosen to analyse the knowledge involved both in teaching and in learning. On the first scale, the analysis of the students' knowledge and that of the knowledge to be taught are done independently to the extent that students' knowledge is not analysed in reference to the knowledge to be taught (in terms of error or missing aspect) but on the basis of the student's coherency. The decomposition of these two types of knowledge into similar components allows us to compare them and leads us to propose “intermediate notions” between the usual physics knowledge to be taught and the students prior knowledge. These intermediate notions can be rather far from complete correct physics knowledge but are learnable by the students. On the second scale, detailed analysis of a single teaching session and the students' processes during this session needs a fine level of knowledge granularity. Such a level allows us to make hypotheses based on the elements of students' prior knowledge from which they can construct new knowledge and not only on the prior knowledge which has to be modified. This granularity level allows an emphasis on the positive aspects of students' prior knowledge and enables us to construct hypothesis in order to design teaching situations. Making explicit “intermediate notions” in the knowledge to be taught at a rather large level of granularity of knowledge and the positive aspects of students' prior knowledge at a fine level of granularity, are proposed as ways to improve teaching for fruitful learning.     

动力加固前后细粒土粒度分布的分维特征及其工程意义

岩土体材料大都表现出分形分布,具有自相似性特征或尺度不变性.通过对河北石家庄市世纪花园地基土的粒度分析,运用分形几何学理论,研究了动力加固前后细粒土粒度成分的分形分维特征,探讨了分维特征的工程意义.     

基于元数据的学习对象库的构建模型及其应用

在学习对象相关概念的基础上,参考国内外学习技术标准,设计了一种学习对象库模型,以实现对不同粒度大小的学习对象进行构建和管理,并将此模型应用到课程的设计开发当中,提高了课程的重用性。