Numerical simulation of the flow about self-propelling tanker models

A time-marching CFD simulation is performed for self-propelling ships. The flow about the hull is simulated by the finite-volume method, and the propeller action is approximated as a propeller disk for which the solution is given by a simplified propeller model. The interaction of two flow models is treated in a time-marching procedure converging towards the steady self-propelling condition. This method is applied to five tanker models, and detailed comparisons are made between the simulated results and corresponding experimental results. It is shown that the flow field in the self-propelling condition is qualitatively well reproduced in the simulation, and the estimated thrust deduction factors for the five hull forms agree well with measured ones. However, the effective wake factors are underestimated, since the Reynolds number in the simulations differs from that in the experiment.     

沪杭磁浮线的社会经济效益分析

沪杭磁浮线对长江三角洲的经济将会带来巨大影响,同时其投入资金巨大.从可量化效益(节约乘客出行时间、提高安全性、提高乘客的劳动生产率、节约城市土地、降低污染、带动沿线不动产的增值、节约能源)和不可量化效益(改善交通促进城市发展、改善居民生活质量)这两大方面对沪杭磁浮线的社会经济效益进行分析.     

采用新型钢轨焊缝保护装置后钢轨焊缝处的轮轨动力学特性

根据轮轨相互作用机理,建立安装新型钢轨焊缝保护装置后钢轨焊缝处的车辆—轨道耦合动力学模型,对此处的轮轨垂向力、脱轨系数、轮重减载率等轮轨动力学指标进行仿真计算,并分别与采用鼓包鱼尾板和没有焊缝保护装置时进行对比,研究采用新型钢轨焊缝保护装置时焊缝处的轮轨动力学特性。对比分析结果表明:采用新型钢轨焊缝保护装置后,轮轨垂向力降幅分别为1.28%和4.63%,脱轨系数降幅分别为1.49%和2.94%,轮重减载率降幅分别为3.41%和7.68%;新型钢轨焊缝保护装置在各速度条件下均能够有效地减小焊缝振动和动态受力。由此可见,采用新型钢轨焊缝保护装置,可消除打螺栓孔带来的安全隐患,有效减小焊缝处的动力响应,加强焊缝处的轨道结构整体性。现场动态测试结果进一步验证了新型钢轨焊缝保护装置结构的合理性。     

小间距隧道施工监控量测分析及应用

对于临近既有隧道新建小间距隧道而言,由于两洞之间的相互影响,围岩及衬砌结构受力比较复杂,施工过程中的现场监控量测显得尤为重要,这既是对既有隧道结构安全性的检测,也是对新建隧道施工的妥当性以及加固措施有效性的评价。针对包西铁路通道新宝塔山施工中洞周收敛、拱顶下沉、围岩压力、喷射混凝土应力及钢架应力等进行了监控量测,确保施工的顺利进行。     

柔性桩与土相互作用非线性分析的增量传递矩阵法

对桩侧土采用非线性荷载传递函数,对桩端土采用线性荷载传递函数,同时考虑桩周土所分担的荷载对单桩荷载传递规律的影响,利用增量荷载传递矩阵法及微分方程的近似解法--子域法,推出了刚性承台下柔性桩与地基非线性相互作用的近似解析算式。为了验证该方法的可行性,通过试验将试验结果与有限元结果及该方法所得结果进行了对比,对比表明:该理论解与现场实测值、有限元计算值都非常接近。     

基于人-车交互的行人轨迹预测

针对行人轨迹预测具有复杂、拥挤的场景和社会交互问题,基于长短时记忆网络(Long Short-term Memory Network,LSTM)对行人与车辆、行人与其他行人的交互进行建模,提出一种基于人-车交互的行人轨迹预测模型(VP-LSTM).该模型同时考虑了行人与行人的交互、行人与车辆的交互,更适用于复杂的交通场...     

船舶交通事故的分析

  目的  为研究近距并行两船的相互干扰效应对船舶操纵性的影响,  方法  基于RANS方程对静水中并行两船的水动力干扰作用进行数值模拟,分析两船在不同横向间距、纵向间距和航速条件下阻力、横向力、纵向力及摇艏力矩的变化规律,并在此基础上进一步阐述各种干扰力成分在两船水动力干扰中的变化及贡献比例。  结果  研究结果表明,两船所受横向力在纵向间距为0（即中对中）时最大,表现为吸引力;随着横向间距的增加,相互作用效应减弱,横向作用力最大降幅达到50%以上。纵向间距对摇艏力矩的影响较大,两船在进入与驶离补给阵位时,所受摇艏力矩使两船艏艉相互接近,此时容易发生碰撞。在低速状态下可以忽略航行兴波对两船相互干扰的影响,而高速航行时则不容忽略。  结论  所得结果可为研究两船操纵运动时相互作用力数学模型的构建奠定基础。     

基于用户认知的新能源汽车内饰研究

在体验经济时代(在消费过程中追求顾客满意、注重自我体验),新能源汽车作为汽车行业的新兴产业,不断受到人们的追捧,其内饰比外观更为重要。在新能源汽车的内饰中,许多零部件具有不可替代的互动性,这也是用户了解新能源汽车内饰的关键因素之一。在智能化时代的背景下,大多数新能源汽车制造商单纯地将智能技术应用到新能源汽车上,其内饰造型、人机交互和使用体验已经不能满足客户对新能源汽车的要求。人机工程学是新能源汽车内饰设计的出发点,掌握用户形象认知是新能源汽车内饰设计的关键。因此,本文从用户的认知角度来研究新能源汽车的内饰设计与创新。     

Socially optimal electric driving range of plug-in hybrid electric vehicles

This study determines the optimal electric driving range of plug-in hybrid electric vehicles (PHEVs) that minimizes the daily cost borne by the society when using this technology. An optimization framework is developed and applied to datasets representing the US market. Results indicate that the optimal range is 16 miles with an average social cost of $3.19 per day when exclusively charging at home, compared to $3.27 per day of driving a conventional vehicle. The optimal range is found to be sensitive to the cost of battery packs and the price of gasoline. When workplace charging is available, the optimal electric driving range surprisingly increases from 16 to 22 miles, as larger batteries would allow drivers to better take advantage of the charging opportunities to achieve longer electrified travel distances, yielding social cost savings. If workplace charging is available, the optimal density is to deploy a workplace charger for every 3.66 vehicles. Moreover, the diversification of the battery size, i.e., introducing a pair and triple of electric driving ranges to the market, could further decrease the average societal cost per PHEV by 7.45% and 11.5% respectively.     

Identifying tourists and analyzing spatial patterns of their destinations from location-based social media data

Reliable travel behavior data is a prerequisite for transportation planning process. In large tourism dependent cities, tourists are the most dynamic population group whose size and travel choices remain unknown to planners. Traditional travel surveys generally observe resident travel behavior and rarely target tourists. Ubiquitous uses of social media platforms in smartphones have created a tremendous opportunity to gather digital traces of tourists at a large scale. In this paper, we present a framework on how to use location-based data from social media to gather and analyze travel behavior of tourists. We have collected data of about 67,000 users from Twitter using its search interface for Florida. We first propose several filtering steps to create a reliable sample from the collected Twitter data. An ensemble classification technique is proposed to classify tourists and residents from user coordinates. The accuracy of the proposed classifier has been compared against the state-of-the-art classification methods. Finally, different clustering methods have been used to find the spatial patterns of destination choices of tourists. Promising results have been found from the output clusters as they reveal most popular tourist spots as well as some of the emerging tourist attractions in Florida. Performance of the proposed clustering techniques has been assessed using internal clustering validation indices. We have analyzed temporal patterns of tourist and resident activities to validate the classification of the users in two separate groups of tourists and residents. Proposed filtering, identification, and clustering techniques will be significantly useful for building individual-level tourist travel demand models from social media data.