使用乙醇还原剂的NOx-SCR催化剂的性能评价

采用计算机辅助分析的方法设计了乙醇还原剂添加装置，并使用这套装置在发动机台架上进行了稀燃NOx选择催化还原（SCR）催化剂的性能评价试验。试验结果表明，在30000h^-1空速、排温为350～420℃范围的条件下，该催化剂的NOx转化率维持在90％以上，而在低温和高温下的转化率较低；随着乙醇添加量的增加，催化剂NOx转化率提高，但同时会增加THC排放；当空速超过40000h^-1后，催化剂性能有所下降。     

Transport energy consumption in mountainous roads. A comparative case study for internal combustion engines and electric vehicles in Andorra

This paper analyses transport energy consumption of conventional and electric vehicles in mountainous roads. A standard round trip in Andorra has been modelled in order to characterise vehicle dynamics in hilly regions. Two conventional diesel vehicles and their electric-equivalent models have been simulated and their performances have been compared. Six scenarios have been simulated to study the effects of factors such as orography, traffic congestion and driving style. The European fuel consumption and emissions test and Artemis urban driving cycles, representative of European driving cycles, have also been included in the comparative analysis. The results show that road grade has a major impact on fuel economy, although it affects consumption in different levels depending on the technology analysed. Electric vehicles are less affected by this factor as opposed to conventional vehicles, increasing the potential energy savings in a hypothetical electrification of the car fleet. However, electric vehicle range in mountainous terrains is lower compared to that estimated by manufacturers, a fact that could adversely affect a massive adoption of electric cars in the short term.     

相继增压系统对改善船用直列八缸发动机性能的试验研究

随着大型柴油发动机经济性和排放要求的不断提升,以及对发动机稳态、瞬态特性要求的不断提高,传统的涡轮增压系统已无法满足船用发动机全工况性能的要求。为了提高产品竞争力和适配性,针对某8缸直列船用柴油发动机开发了带有定压排气管的相继增压系统,并进行了发动机台架试验研究。结果表明：发动机中、低转速时,相继增压系统使发动机比油耗可以下降5%-10%,排气温度降低30%-45%,烟度下降55%-60%；最大扭矩可以提升15.1%-36.4%；发动机最大扭矩转速工况突加突卸响应性提升约28%-29%；相继增压系统中定压排气管的设计使得发动机高工况性能也有所提升,从而实现发动机全工况性能的提升。     

5000 PCC“SHANGHAI HIGHWY”船主机启动分析与处理

分析5 000 PCC"SHANGHAI HIGHWAY"船主机伺服油压低引起主机启动故障的原因、介绍调查与处理过程,在实施了恰当的处理方法后,解决了问题。以期为类似问题的处理与解决提供借鉴和参考。     

Free-floating bike sharing: Solving real-life large-scale static rebalancing problems

Free-floating bike sharing (FFBS) is an innovative bike sharing model. FFBS saves on start-up cost, in comparison to station-based bike sharing (SBBS), by avoiding construction of expensive docking stations and kiosk machines. FFBS prevents bike theft and offers significant opportunities for smart management by tracking bikes in real-time with built-in GPS. However, like SBBS, the success of FFBS depends on the efficiency of its rebalancing operations to serve the maximal demand as possible.Bicycle rebalancing refers to the reestablishment of the number of bikes at sites to desired quantities by using a fleet of vehicles transporting the bicycles. Static rebalancing for SBBS is a challenging combinatorial optimization problem. FFBS takes it a step further, with an increase in the scale of the problem. This article is the first effort in a series of studies of FFBS planning and management, tackling static rebalancing with single and multiple vehicles. We present a Novel Mixed Integer Linear Program for solving the Static Complete Rebalancing Problem. The proposed formulation, can not only handle single as well as multiple vehicles, but also allows for multiple visits to a node by the same vehicle. We present a hybrid nested large neighborhood search with variable neighborhood descent algorithm, which is both effective and efficient in solving static complete rebalancing problems for large-scale bike sharing programs.Computational experiments were carried out on the 1 Commodity Pickup and Delivery Traveling Salesman Problem (1-PDTSP) instances used previously in the literature and on three new sets of instances, two (one real-life and one general) based on Share-A-Bull Bikes (SABB) FFBS program recently launched at the Tampa campus of University of South Florida and the other based on Divvy SBBS in Chicago. Computational experiments on the 1-PDTSP instances demonstrate that the proposed algorithm outperforms a tabu search algorithm and is highly competitive with exact algorithms previously reported in the literature for solving static rebalancing problems in SBSS. Computational experiments on the SABB and Divvy instances, demonstrate that the proposed algorithm is able to deal with the increase in scale of the static rebalancing problem pertaining to both FFBS and SBBS, while deriving high-quality solutions in a reasonable amount of CPU time.     

变邻域搜索求解公共交通乘务调度问题

公共交通乘务调度问题是一个将车辆工作切分为一组合法班次的过程,它是NP难问题,许多求解方法的效率都与班次评价密不可分,本文通过裁剪TOPSIS方法(Technique for Order Preference by Similarity to an Ideal Solution)设计了TOPSIS班次评价方法.此外,通过裁剪变邻域搜索算法使之适合求解乘务调度问题,提出了基于变邻域搜索的乘务调度方法(Crew Scheduling Approach Based on Variable Neighbourhood Search,VNS),其中,并入了TOPSIS班次评价方法在调度过程中进行班次评价,设计了两种带概率的复合邻域结构以增加搜索的多样性,帮助跳出局部最优,在VNS中利用模拟退火算法进行局部搜索.利用中国公共交通中的11组实例进行了测试,测试结果表明,VNS优于两种新近提出的乘务调度方法,且其结果关于班次数接近于下界.     

The load-dependent vehicle routing problem and its pick-up and delivery extension

The present paper examines a Vehicle Routing Problem (VRP) of major practical importance which is referred to as the Load-Dependent VRP (LDVRP). LDVRP is applicable for transportation activities where the weight of the transported cargo accounts for a significant part of the vehicle gross weight. Contrary to the basic VRP which calls for the minimization of the distance travelled, the LDVRP objective is aimed at minimizing the total product of the distance travelled and the gross weight carried along this distance. Thus, it is capable of producing sensible routing plans which take into account the variation of the cargo weight along the vehicle trips. The LDVRP objective is closely related to the total energy requirements of the vehicle fleet, making it a credible alternative when the environmental aspects of transportation activities are examined and optimized. A novel LDVRP extension which considers simultaneous pick-up and delivery service is introduced, formulated and solved for the first time. To deal with large-scale instances of the examined problems, we propose a local-search algorithm. Towards an efficient implementation, the local-search algorithm employs a computational scheme which calculates the complex weighted-distance objective changes in constant time. Solution results are presented for both problems on a variety of well-known test cases demonstrating the effectiveness of the proposed solution approach. The structure of the obtained LDVRP and VRP solutions is compared in pursuit of interesting conclusions on the relative suitability of the two routing models, when the decision maker must deal with the weighted distance objective. In addition, results of a branch-and-cut procedure for small-scale instances of the LDVRP with simultaneous pick-ups and deliveries are reported. Finally, extensive computational experiments have been performed to explore the managerial implications of three key problem characteristics, namely the deviation of customer demands, the cargo to tare weight ratio, as well as the size of the available vehicle fleet.     

基于以太网的机舱监测报警系统设计

本文介绍了基于以太网的机舱监测报警系统的组成和功能,并对机舱监测报警系统的硬件、软件进行了设计,为后续的机舱监测系统设计提供参考。     

某船右主机不能正常建立滑油压力的故障排除

文章对某船右主机启动过程中,机带滑油泵不能正常建立滑油压力进行了分析,制定了针对性的排除故障措施,通过在机带滑油泵通舱管吸入口处安装吸入止回阀解决了滑油压力非正常下降的故障,实船应用证明措施合理,对同类故障排除有较好的借鉴意义。     

Real-time trip purpose prediction using online location-based search and discovery services

The use of smartphone technology is increasingly considered a state-of-the-art practice in travel data collection. Researchers have investigated various methods to automatically predict trip characteristics based upon locational and other smartphone sensing data. Of the trip characteristics being studied, trip purpose prediction has received relatively less attention. This research develops trip purpose prediction models based upon online location-based search and discovery services (specifically, Google Places API) and a limited set of trip data that are usually available upon the completion of the trip. The models have the potential to be integrated with smartphone technology to produce real-time trip purpose prediction. We use a recent, large-scale travel behavior survey that is augmented by downloaded Google Places information on each trip destination to develop and validate the models. Two statistical and machine learning prediction approaches are used, including nested logit and random forest methods. Both sets of models show that Google Places information is a useful predictor of trip purpose in situations where activity- and person-related information is uncollectable, missing, or unreliable. Even when activity- and person-related information is available, incorporating Google Places information provides incremental improvements in trip purpose prediction.