重庆河段泥沙模型监控系统

介绍采用PC电脑实现可视管理的“重庆河段泥沙模型监控系统” .论述了系统的主要结构、基本功能以及系统控制原理 .并提出一种伺服电机PWM位控的原理和方法 .     

船闸引航道内的异重流淤积计算*

异重流淤积是引航道的主要淤积之一,其特点是淤积距离长、淤积总量大,如不及时清淤,则会减小通航水深、影响通航安全。在资料有限的条件下,采用已有公式对引航道的异重流淤积量进行初步计算,对认识引航道的淤积特性,制定初步的清淤方案有着重要的意义。在现有异重流淤积计算公式的基础上,提出了考虑异重流清水上升效应的沿程含沙量修正公式。与现有公式相比,其计算结果与实测值更接近,为异重流淤积量计算研究提供新途径。     

弯曲航道水流特性及岸坡稳定性研究*

对某段弯曲航道水流进行三维数值模拟,简要分析了弯道水流特性,并将弯曲航道水流淘刷作用耦合于岸坡稳定性二维数值模拟中,量化水流淘刷对岸坡稳定性的影响,在此基础上计算不同冲刷深度下的岸坡稳定性。结果表明：高水位情况下岸坡受环流淘刷作用的影响更大;冲刷深度越大,岸坡稳定系数越小;当冲刷达到一定程度,岸坡局部发生崩塌从而降低整体稳定性。     

低流量下蒸汽发生器倒U型管内流动传热的数值模拟

针对自然循环工况下蒸汽发生器部分倒U型传热管存在倒流现象,利用CFD软件对倒U型传热管内单相水流动传热特性进行研究。通过建立不同管长的倒U型管水动力曲线,分析了管长对倒流的影响,得出长管处易发生倒流的结论;通过对长管重位压降和摩擦压降随雷诺数变化情况进行分析,对倒流现象的出现进行了的理论解释。该文所得结果对倒流管空间分布的研究具有一定的意义。     

基于三维势流理论的耐波性数值预报

准确预报船体运动响应对于砰击等波浪载荷的计算以及合理结构设计具有重要意义。船舶在大幅波浪中的运动呈明显的非线性,而现阶段耐波性预报多采用线性切片方法。三维水动力分析软件 WASIM基于时域势流理论,采用 Rankine面元法预报船舶在波浪中的运动响应,并考虑了多种非线性因素。本文以标模 DTMB5512为对象,采用 WASIM预报其在不同航速下的耐波性,并与基于线性切片理论的计算结果和模型试验结果进行对比。结果表明：利用 WASIM计算得到的船体运动响应比其他方法更接近试验值,合理体现了船舶在风浪中的实际耐波性能。因此,利用 WASIM能够较好地评估船舶在波浪中的非线性耐波特性。     

基于Optimoor的内河码头船舶运动量下系泊分析

与沿海码头相比,内河码头船舶系泊作业受水流影响更为显著,且流向多为顺流。通过系泊仿真试验,以允许船舶系泊作业时的最大运动量为控制标准,分析允许船舶系泊作业时的各项限制条件,尤其是限定的水流条件。现行《规范》对码头系泊作业时基于船舶运动量下的限制波浪、风速、水流等未予以明述,本文研究结果可为相关船舶系泊作业提供理论参考。     

三维竖轴潮流水轮机水动力性能研究

竖轴水轮机作为潮流能转换为电能的核心装置,其水动力性能的优劣将会直接影响到整体发电系统的效率。为了研究大型竖轴水轮机叶片安装角对水轮机水动力性能的影响,基于多参考系模型(MRF),采用Fluent软件对流场中的模型进行3D数值模拟。在转速和来流速度保持不变,改变安装角时,分析同种翼型5个不同安装角叶片对潮流能水轮机的水动力性能的影响。同时分析在同一安装角和旋转速度条件下,不同来流速度对水轮机水动力性能的影响。结果表明,叶片安装角对竖轴潮流水轮机的能量利用率影响较大,来流速度对水轮机叶片表面的静压力和输出功率具有一定的影响。研究结果对今后竖轴水轮机的设计和生产具有借鉴意义。     

A methodology for identifying similar days in air traffic flow management initiative planning

This article describes a methodology for selecting days that are comparable in terms of the conditions faced during air traffic flow management initiative planning. This methodology includes the use of specific data sources, specific features of calendar days defined using these data sources, and the application of a specific form of classification and then cluster analysis. The application of this methodology will produce results that enable historical analysis of the use of initiatives and evaluation of the relative success of different courses of action. Several challenges are overcome here including the need to identify the appropriate machine learning algorithms to apply, to quantify the differences between calendar days, to select features describing days, to obtain appropriate raw data, and to evaluate results in a meaningful way. These challenges are overcome via a review of relevant literature, the identification and trial of several useful models and data sets, and careful application of methods. For example, the cluster analysis that ultimately selects sets of similar days uses a distance metric based on variable importance measures from a separate classification model of observed initiatives. The methodology defined here is applied to the New York area, although it could be applied by other researchers to other areas.     

超声波流量计在输油管道泄漏监测系统中的应用

为提高负压波法管道泄漏监测系统的报警可靠性,有必要将超声波流量信息引入管道泄漏监测系统。为保证管道泄漏监测系统中超声波流量计的应用效果,分析了超声波流量计的测量原理和测量精度影响因素,总结了输油管道超声波流量计在选型设计、现场安装及运行维护等方面的应用经验。开展了基于超声波流量计的管道泄漏监测技术的现场应用测试和结果分析,测试结果表明超声波流量计对提升管道泄漏监测系统报警可靠性具有显著作用。     

Short‐term traffic flow prediction with linear conditional Gaussian Bayesian network

Traffic flow prediction is an essential part of intelligent transportation systems (ITS). Most of the previous traffic flow prediction work treated traffic flow as a time series process only, ignoring the spatial relationship from the upstream flows or the correlation with other traffic attributes like speed and density. In this paper, we utilize a linear conditional Gaussian (LCG) Bayesian network (BN) model to consider both spatial and temporal dimensions of traffic as well as speed information for short‐term traffic flow prediction. The LCG BN allows both continuous and discrete variables, which enables the consideration of categorical variables in traffic flow prediction. A microscopic traffic simulation dataset is used to test the performance of the proposed model compared to other popular approaches under different predicting time intervals. In addition, the authors investigate the importance of spatial data and speed data in flow prediction by comparing models with different levels of information. The results indicate that the prediction accuracy will increase significantly when both spatial data and speed data are included. Copyright © 2016 John Wiley & Sons, Ltd.