船舶柴油机颗粒物排放控制技术研究进展

颗粒物是船舶柴油机排气中的主要污染物,严重危害人体健康和大气环境,如何降低船机颗粒物排放已成为环境保护及柴油机领域的一个重点研究方向。本文对船机排放颗粒物的组分及来源进行分析,梳理论述目前主流的颗粒物排放控制技术及DPF再生技术。认为后处理技术是今后颗粒物排放控制技术研究的重点,其中,最有效、应用最广泛的是DPF技术,复合再生技术是DPF再生过程的发展方向,提出了船机颗粒物排放控制未来需重点研究的关键技术及长远发展目标。     

基于支持向量回归机和粒子群算法的船舶操纵运动模型辨识

基于仿真的Z形试验数据,应用支持向量回归机对船舶操纵运动响应模型进行了机理建模,从核函数结构中得到了模型中的操纵性指数,并利用建立的响应模型进行了Z形试验的运动预报,同时引入粒子群算法对惩罚因子C值进行寻优,以减少惩罚因子C值选择的任意性对船舶操纵运动模型辨识精度产生的不利影响。通过将运动预报结果同仿真试验数据进行比较,验证了文中方法的有效性。     

船舶岸电系统在集装箱码头的应用

靠港船舶岸电系统具有可以减少污染物排放和提高能源利用率的优点,正在我国港口大力推广。为探讨岸电系统在集装箱码头的应用前景,以大窑湾北岸某集装箱码头为例,利用仿真模型核算多种情景下2020年大窑湾北岸集装箱码头船舶使用岸电前后的碳排放量,并分析不同装卸效率对船舶的碳排放的影响。结果表明,集装箱码头使用岸电系统可实现低碳效益,提高装卸效率有利于船舶碳排放的减少,但应考虑提高装卸效率会引起装卸机械碳排放增加。     

A generic data assimilation framework for vehicle trajectory reconstruction on signalized urban arterials using particle filters

With trajectory data, a complete microscopic and macroscopic picture of traffic flow operations can be obtained. However, trajectory data are difficult to observe over large spatiotemporal regions—particularly in urban contexts—due to practical, technical and financial constraints. The next best thing is to estimate plausible trajectories from whatever data are available. This paper presents a generic data assimilation framework to reconstruct such plausible trajectories on signalized urban arterials using microscopic traffic flow models and data from loops (individual vehicle passages and thus vehicle counts); traffic control data; and (sparse) travel time measurements from whatever source available. The key problem we address is that loops suffer from miss- and over-counts, which result in unbounded errors in vehicle accumulations, rendering trajectory reconstruction highly problematic. Our framework solves this problem in two ways. First, we correct the systematic error in vehicle accumulation by fusing the counts with sparsely available travel times. Second, the proposed framework uses particle filtering and an innovative hierarchical resampling scheme, which effectively integrates over the remaining error distribution, resulting in plausible trajectories. The proposed data assimilation framework is tested and validated using simulated data. Experiments and an extensive sensitivity analysis show that the proposed method is robust to errors both in the model and in the measurements, and provides good estimations for vehicle accumulation and vehicle trajectories with moderate sensor quality. The framework does not impose restrictions on the type of microscopic models used and can be naturally extended to include and estimate additional trajectory attributes such as destination and path, given data are available for assimilation.     

在役丁烯-1球罐声发射检测

为确保在役丁烯-1球罐的使用安全性,制定了以声发射检测为主的检验方案。在球罐外表面布置50只传感器,采用2次加压循环过程对在役丁烯-1球罐进行声发射检测,仪器自动记录各加载阶段出现的数据信号并进行定位计算。通过对声发射源进行强度和活性分析,未发现活性声发射源信号,综合声发射检测结果和使用情况,该球罐安全状况良好,可继续使用。同时为保证使用安全,建议在使用期间做好年度检查工作,在适当时机开罐进行全面检验。     

Development of a driving cycle for Colombo,Sri Lanka: an economical approach for developing countries

Financial constraints and lack of availability of traffic‐related information significantly hinder the development of driving cycles in developing countries. This paper proposes an economical, practical, accurate methodology for the development of driving cycles, including the development of a driving cycle for Colombo, Sri Lanka. The proposed methodology captures regional traffic and road conditions and selects a model that represents the collected data sample with minimum available traffic‐related information. Existing methods were modified for route selection by dividing routes into links using nodes or physical junctions to minimize the number of trips required for data collection. Speed–time data for respective links were used to reconstruct speed–time profiles of identified origin–destination pairs. The on‐board method was used for data collection, and the Markov chain theory was used to develop a transition probability matrix of state changes. An additional matrix was introduced to the existing method to improve model representativeness to the collected data sample. Copyright © 2016 John Wiley & Sons, Ltd.     

重庆市内河船舶污染物排放现状及LNG动力船舶推广效益分析

重庆地处长江上游经济带核心地区,水运发达,随着客货运量逐年增加,通行的内河船舶尾气排放严重影响港口及航道附近大气环境,已成为重庆大气污染的主要来源之一,颗粒物、NO_x的排放分别占重庆市总排放的4.3%、21.9%,SO_2占年排放的7.4%(14.8%。在大气环境问题日益严峻的情况下,LNG动力船舶因其可明显降低排气污染物,越发受到社会关注,对重庆市单LNG动力示范船舶尾气排放进行实测,对比柴油动力船舶,颗粒物、NO_x、SO_2减排显著,减排率分别达99.94%、72.69%、100%。因此,LNG动力船舶的推广表现出明显的环境效益,同时,综合分析了当前LNG动力船舶推广存在问题及困难。     

Integrated solution for anomalous driving detection based on BeiDou/GPS/IMU measurements

There has been an increasing role played by Global Navigation Satellite Systems (GNSS) in Intelligent Transportation System (ITS) applications in recent decades. In particular, centimeter/decimetre positioning accuracy is required for some safety related applications, such as lane control, collision avoidance, and intelligent speed assistance. Lane-level Anomalous driving detection underpins these safety-related ITS applications. The two major issues associated with such detection are (1) accessing high accuracy vehicle positioning and dynamic parameters; and (2) extraction of irregular driving patterns from such information. This paper introduces a new integrated framework for detecting lane-level anomalous driving, by combining Global Positioning Systems (GPS), BeiDou, and Inertial Measurement Unit (IMU) with advanced algorithms. Specifically, we use Unscented Particle Filter (UPF) to perform data fusion with different positioning sources. The detection of different types of Anomalous driving is achieved based on the application of a Fuzzy Inference System (FIS) with a newly introduced velocity-based indicator. The framework proposed in this paper yield significantly improved accuracy in terms of positioning and Anomalous driving detection compared to state-of-the-art, while offering an economically viable solution for performing these tasks.     

基于壅塞理论的CNG加气管流量计算分析

为了计算CNG加气系统的高压胶管的流量,以气体动力学为基础,建立等截面一维定常绝热摩擦流动的数学模型.利用VC+ +语言编制界面计算程序对模型进行编程求解,对壅塞状态和非壅塞状态下流量以及出口压力进行计算和拟合.当天然气汽车储瓶内的压力小于临界压力时,加气流量最大,且与胶管两端的压差无关,并计算出最大流量与胶管管长的关...     

Preliminary investigation of PM1, PM2.5, PM10 and its metal elemental composition in tunnels at a subway station in Shanghai,China

In this study, real-time monitoring campaigns were conducted in two tunnels (Line A and Line B) at a subway station in Shanghai, including temperature, relative humidity, PM₁, PM_2.5 and PM₁₀, in order to understand the climate and PM characteristics in the transportation microenvironment. In addition, collected floor dust particles in the tunnel were analyzed by ICP for their metal elemental composition. Strong correlations occurred between all PM levels and meteorological parameters in the tunnel of Line A (with platform screen doors), in comparison with the weak correlations between such parameters in the tunnel of Line B (without platform screen doors). PM_2.5 and PM₁₀ between peak hours and off-peak hours for both lines presented significant differences (p < 0.05), respectively. Nevertheless, PM₁ showed a different pattern, with p > 0.05 for Line A and p < 0.05 for Line B, respectively. In addition, statistical results concluded that PM had an evident weekly variation for both lines. Friday was the highest day of all particulate matters in monitoring periods for both lines. Ratios of PM₁/PM₁₀ and PM_2.5/PM₁₀ were high when trains were out of service and low when trains were in service. Relative abundance of metal elements detected from floor dust particles proved that floor dust particles in tunnels might be a major source of airborne PM in the subway microenvironments, with Fe as the most abundant metal element, followed by Ca, Al, Mg, Mn, Zn, Cu, Cr, Ni, Pb and Hg.