绿色船舶的营运选择(英文)

Environmental issues and rising fuel prices necessitate better energy-efficiency in all sectors.The shipping industry is one of the major stakeholders,responsible for 3% of global CO2 emissions,14%-15% of global NOX emissions,and 16% of global SOX emissions.In addition,continuously rising fuel prices are also an incentive to focus on new ways for better energy-effectiveness.The green ship concept requires exploring and implementing technology on ships to increase energy-efficiency and reduce emissions.Ship operation is an important topic with large potential to increase cost-and-energy-effectiveness.This paper provided a comprehensive review of basic concepts,principles,and potential of operational options for green ships.The key challenges pertaining to ship crew i.e.academic qualifications prior to induction,in-service training and motivation were discussed.The author also deliberated on remedies to these challenges.     

高速公路特殊气象环境的管理

针对浓雾、沙尘、暴雨、大雪、强烈侧风等气象环境严重影响高速公路行驶车辆稳定性的问题,通过在特殊气象环境下对高速公路车辆行驶稳定性的各项影响因素的分析,得出了高速公路管理部门必需对气象条件、环境和道路条件进行实时检测,并且根据检测结果采取各种有效相应的措施.     

基于VDM与APSO优化极限学习机的船舶运动姿态预报

为提高船舶在海上运动的耐波性与适航性,并为解决具有非线性、随机性和非平稳性特点的船舶运动姿态难以准确预测的问题,提出运用一种基于变分模态分解和自适应粒子群算法优化极限学习机的组合预测模型。该算法首先利用变分模态分解将船舶运动姿态序列分解为一系列限带内本征模态函数,并且变分模态分解可以避免经验模态分解技术所产生的模态混叠和端点效应,可以降低序列的非平稳性对预测精度的影响;然后对各模态分量分别建立极限学习机预测模型,并用改进的粒子群算法对极限学习机的初始权值和阈值进行优化;最后将各模态分量预测结果进行叠加,得到最终的船舶运动姿态预测值。通过模拟试验测试并与其他传统的预测方法进行比较,结果表明所建立的组合预测模型具有更高的预测精度。     

人工湖积水对下方地铁盾构区间结构变形影响分析

为明确下穿人工湖地铁盾构隧道结构下沉变形原因,并对变形后盾构隧道结构安全进行评估,以某人工湖受极端天气影响水位快速上升后,下穿人工湖的地铁盾构隧道产生沉降为例展开研究。首先通过荷载结构模型对盾构隧道强度进行验算,然后采用地层结构模型模拟盾构隧道上方堆载及卸载工况对盾构隧道变形的影响,并与现场监测数据进行对比分析。结果表明：人工湖水位上升为盾构隧道变形下沉的主要原因,产生变形后隧道结构自身承载能力及裂缝宽度均满足设计和规范要求,盾构隧道结构自身是安全的,同时对人工湖抽水后隧道上浮值进行预测,盾构隧道结构在上浮后仍然处于安全状态,研究成果可以为后续人工湖及地铁隧道处理措施提供参考依据。     

电子海图系统中叠加气象信息的矢量化方法研究

为了更好地保障船舶航行安全,将气象信息叠加到电子海图系统中,以便给航海人员提供决策依据。根据电子海图系统中图像数据的特点,文章提出一种将气象预报图等压线矢量化的方法。     

恶劣环境对驾驶员反应时间的影响研究

驾驶员反应时间受其本身状况和所处环境条件的影响：针对雨、雾等灾害性天气以及强光、路面亮度不足等恶劣环境下驾驶员的反应时间状况,在国内外研究现状的调研分析的基础上,对能见度、眩光强度、路面亮度等道路环境与驾驶员反应时间的关系进行研究,结果表明驾驶员的反应时间随能见度增加、眩光增强、亮度减少而增长。     

城市轨道交通灾害性天气的定义、分类及研究方向

阐述了灾害性天气对城市轨道交通的影响及其相关研究的重要性和必要性。综述了国内外相关问题的研究进展。在此基础上,对城市轨道交通灾害性天气作了定义,界定了城市轨道交通17种灾害性天气,并对其进行了分类。指出了今后城市交通灾害性天气研究的主要方向,为今后深入研究城市轨道交通灾害性天气的发生机理及影响规律,以及建立高效的城市轨道交通灾害性天气预防系统奠定基础。     

不同渗透率下自动驾驶混合交通流在天气条件影响下的通行能力建模

随着世界范围内对自动驾驶汽车及其相关产业发展的高度重视,自动驾驶车辆上路已成为重点领域协同创新、构建未来交通系统的重要载体。本文主要研究自动驾驶车辆不同渗透率参与的混合交通流受场景天气条件的耦合影响因素下的复杂车辆行为逻辑分析,影响机理解析,跟驰模型及通行能力模型构建等。最后我们通过SUMO仿真实验对模型进行嵌套及分析,以期对自动驾驶汽车在测试及上路引导中起到重要理论决策依据。     

Stochastic averaging for estimating a ship roll in random longitudinal or oblique waves

In this work, the C11 container ship is taken as an example to analyze its rolling performances in random longitudinal or oblique waves. Firstly, a dynamic model of C11 roll in random waves is improved, and it is verified by the model test and numerical simulation. Mathematically, this dynamic model is a one-dimensional stochastic differential equation with random parametric (and external) excitation. Secondly, an enhanced stochastic averaging method is proposed to solve this stochastic differential equation. The validity of the solutions was verified by Monte Carlo simulation. At last, the probabilistic characteristics of the extreme rolling response were investigated based on the calculated results using enhanced stochastic averaging method. According to the analysis, some advices for ship's manoeuvring can be put forward when ships are navigating in random waves.     

Minimum sample size for extreme value statistics of flow-induced response

Extreme value statistics are one way to determine the maximum design loads for systems in extreme conditions, such as operational loads experienced by ships. Accurate predictions typically require large sample sizes, which are not always possible to obtain. Conversely, small sample sizes lead to more variation in the predictions. Increasing the sample size improves the variance to a desired range. The proposed method aimed to estimate a minimum sample size for an extreme value process by specifying and obtaining an acceptable variance. Minimum sample sizes for extreme value statistics depend on the distribution's behavior, so the method proposed here was designed for use before and during measurements. To test the proposed method, the response of a cantilever fin with a varying angle of attack was measured. The proposed method was able to estimate minimum sample sizes for several distributions. Accuracy was demonstrated by randomly drawing measured and simulated samples.