港口高杆灯的无线智能控制系统设计

随着港口建设规模的扩大,港区堆场面积越来越大,配套的高杆灯及其他照明设施的数量也相应增加,需要一个自动化的、科学的管理方式和系统对其进行管理和控制。照明无线智能控制系统采用先进可靠的远距离无线通信技术,将人机操作界面和远程控制环节有机结合起来,实现对高杆灯的远距离无线控制。通过高杆灯无线智能控制系统,合理组合开启灯具数量和安排开启时间,智能调光,以达到高效节能、安全运行的良好效果。     

航道治理建设规划环境影响识别及指标体系建立

针对航道治理建设规划环境影响识别及指标体系建立问题,对长江干线"十三五"航道治理建设规划环境影响评价成果进行分析研究,采用系统分析、生态因子识别、生态影响判别等方法,得出规划要素与资源、环境要素之间的关系,并结合环境目标提出评价指标体系,可为航道规划环境影响评价提供参考和借鉴。     

旁海枢纽工程船闸输水系统布置及试验研究

高水头船闸常采用第二类输水系统以满足船闸闸室内水流条件的要求,但往往工程量大、施工复杂。根据船闸闸址处地形、地质条件,抬高闸底奠基高程以达到降低工程量并简化施工的目的,并在采取一定消能手段后,采用闸墙长廊道侧支孔输水形式。通过物理模型,对旁海航电枢纽输水系统进行布置及优化,证实采取加大闸室初始淹没水深、闸室两侧出水口加设连续消力坎、优化进出水口布置及进水口布置帷墙等措施可达到预期效果。     

水电解制氢装置宽功率波动适应性研究

随着风电行业的迅猛发展,对风能源的转化有了迫切需求,对能源转化系统的风电波动性适应提出了更高要求.氢气作为清洁的"新能源"成为消化弃风的最佳选择.利用调整各工艺参数的方法,研究传统水电解制氢装置在风电宽功率波动条件下的适应性,并探索相关解决方法.为水电解制氢装置的进一步改进提供支持,为微电网风电耦合制氢及氢能综合利用提供理论依据.     

拖曳式试验系统拖缆平台振动测试与分析

工程应用中,拖曳式试验系统拖缆平台存在明显的振动现象,振动产生的辐射噪声会对环境声场产生影响.基于拖缆平台的结构及运行原理,分析其内部的主要激励源,并进行现场振动测试.测试试验对平台支柱、绞车系统和隔振装置的振动信号进行了测量和分析,结果表明,平台支柱存在一定程度的振动,绞车机架振动主要由传动装置激励产生,隔振螺栓隔振效果不佳.测试数据及结果可为系统在工程应用中的优化设计和振动控制提供参考.     

舰载弹库泄压排导技术研究

通过对弹药发生爆炸时的内部压力流场进行分析,并分别针对弹药爆炸及导弹意外点火时产生的爆压进行探讨,提出安全有效的泄压排导措施;对泄压排导装置工作时的壳体强度、压盘受力情况进行研究,并给出具体的计算与校核方法,为弹库泄压排导设计提供重要理论依据与技术支撑.     

水下无人作战系统装备现状及发展趋势

海军水下无人作战系统因其造价低廉、隐蔽性强、避免人员伤亡等诸多优点而得到大力发展,美国等国已制定各类水下无人作战系统发展规划并开发出便携型、轻型、重型和巨型等多种水下无人作战系统,并向着提高智能化、模块化和标准化程度、拓展多平台搭载使用能力等方向发展。为了应对威胁,我国也应从顶层规划水下无人作战体系发展路线,开发多样化的水下无人作战系统。     

基于相关性模型的舰船系统测试性建模与分析

本文提出一种基于相关性模型的舰船系统级产品的测试性建模与分析方法.该方法通过建立产品任务模型、相关性图示模型等,利用一阶相关性列矢量法求解产品的相关性数学模型,进而建立产品诊断树和进行测试性预计,并为优化产品诊断策略提供建议.同时,根据舰用燃气轮机滑油系统组成和任务特征,建立其典型任务模型并进行精简,并在此基础上进行测试性建模、诊断树建立、测试性预计以及诊断策略优化等工作,为舰船系统级产品的测试性建模、分析和诊断策略研究提供新思路和手段.     

国外舰艇综合电力系统研究综述

舰艇综合电力系统实现了全舰能源的综合利用,是舰艇平台由机械化向电气化和信息化发展的必由之路,代表现代舰艇动力平台的发展方向.本文通过对国外舰艇综合电力系统应用现状、技术状态、军事意义及未来发展趋势进行分析梳理,供相关人员参考.     

Assessing the impact of reduced visibility on traffic crash risk using microscopic data and surrogate safety measures

Due to the difficulty of obtaining accurate real-time visibility and vehicle based traffic data at the same time, there are only few research studies that addressed the impact of reduced visibility on traffic crash risk. This research was conducted based on a new visibility detection system by mounting visibility sensor arrays combined with adaptive learning modules to provide more accurate visibility detections. The vehicle-based detector, Wavetronix SmartSensor HD, was installed at the same place to collect traffic data. Reduced visibility due to fog were selected and analyzed by comparing them with clear cases to identify the differences based on several surrogate measures of safety under different visibility classes. Moreover, vehicles were divided into different types and the vehicles in different lanes were compared in order to identify whether the impact of reduced visibility due to fog on traffic crash risk varies depending on vehicle types and lanes. Log-Inverse Gaussian regression modeling was then applied to explore the relationship between time to collision and visibility together with other traffic parameters. Based on the accurate visibility and traffic data collected by the new visibility and traffic detection system, it was concluded that reduced visibility would significantly increase the traffic crash risk especially rear-end crashes and the impact on crash risk was different for different vehicle types and for different lanes. The results would be helpful to understand the change in traffic crash risk and crash contributing factors under fog conditions. We suggest implementing the algorithms in real-time and augmenting it with ITS measures such as VSL and DMS to reduce crash risk.