发动机-悬置系统的能量法解耦及优化设计

本文通过发动机-悬置系统的能量分布得到系统解耦的能量指标,并以该能量指标为优化设计目标。以系统的固有频率为约束条件,应用DSFD算法对系统进行优化计算。所编制的优化设计程序具有计算可靠、收敛速度快的特点。为便于程序应用,本文指出了系统能量解耦目标、频率约束及设计变量等因素对优化计算的影响。对某一轻型货车发动机-悬置系统的优化计算表明,仅通过悬置刚度参数的调整即可使系统的解耦水平明显提高。     

Machine learning applications in activity-travel behaviour research: a review

ABSTRACT

This paper reviews the activity-travel behaviour literature that employs Machine Learning (ML) techniques for empirical analysis and modelling. Machine Learning algorithms, which attempt to build intelligence utilizing the availability of large amounts of data, have emerged as powerful tools in the fields of pattern recognition and big data analysis. These techniques have been applied in activity-travel behaviour studies since the early ’90s when Artificial Neural Networks (ANN) were employed to model mode choice decisions. AMOS, an activity-based modelling system developed in the mid-’90s, has ANN at its core to model and predict individual responses to travel demand management measures. In the dawn of 2000, ALBATROSS, a comprehensive activity-based travel demand modelling system, was proposed by Arentze and Timmermans using Decision Trees. Since then researchers have been exploring ML techniques like Support Vector Machines (SVM), Decision Trees (DT), Neural Networks (NN), Bayes Classifiers, and more recently, Ensemble Learners to model and predict activity-travel behaviour. A large number of publications over the years and an upward trend in the number of published articles over time indicate that Machine Learning is a promising tool for activity-travel behaviour analysis and prediction. This article, first of its kind in the literature, reviews these studies and explores the trends in activity-travel behaviour research that apply ML techniques. The review finds that mode choice decisions have received wide attention in the literature on ML applications. It was observed that most of the studies identify the lack of interpretability as a serious shortcoming in ML techniques. However, very few studies have attempted to improve the interpretability of the models. Further, some studies report the importance of feature engineering in ML-based studies, but very few studies adopt feature engineering before model development. Spatiotemporal transferability of models is another issue that has received minimal attention in the literature. In the end, the paper discusses possible directions for future research in the area of activity-travel behaviour modelling using ML techniques.     

Hydrogen refueling station networks for heavy-duty vehicles in future power systems

A potential solution to reduce greenhouse gas (GHG) emissions in the transport sector is to use alternatively fueled vehicles (AFV). Heavy-duty vehicles (HDV) emit a large share of GHG emissions in the transport sector and are therefore the subject of growing attention from global regulators. Fuel cell and green hydrogen technologies are a promising option to decarbonize HDVs, as their fast refueling and long vehicle ranges are consistent with current logistic operational requirements. Moreover, the application of green hydrogen in transport could enable more effective integration of renewable energies (RE) across different energy sectors. This paper explores the interplay between HDV Hydrogen Refueling Stations (HRS) that produce hydrogen locally and the power system by combining an infrastructure location planning model and an electricity system optimization model that takes grid expansion options into account. Two scenarios – one sizing refueling stations to support the power system and one sizing them independently of it – are assessed regarding their impacts on the total annual electricity system costs, regional RE integration and the levelized cost of hydrogen (LCOH). The impacts are calculated based on locational marginal pricing for 2050. Depending on the integration scenario, we find average LCOH of between 4.83 euro/kg and 5.36 euro/kg, for which nodal electricity prices are the main determining factor as well as a strong difference in LCOH between north and south Germany. Adding HDV-HRS incurs power transmission expansion as well as higher power supply costs as the total power demand increases. From a system perspective, investing in HDV-HRS in symbiosis with the power system rather than independently promises cost savings of around seven billion euros per annum. We therefore conclude that the co-optimization of multiple energy sectors is important for investment planning and has the potential to exploit synergies.     

基于故障树分析的汽车防盗系统故障诊断研究

应用故障树分析法对汽车防盗系统不解除故障进行研究,建立了模块不工作、钥匙不工作、线路故障和启动装置按钮不工作的故障树模型。以迈腾B8装备的第五代防盗系统故障为例,进行故障分析和排查,结果表明该方法能够快速查找分析故障原因,提高效率,可为汽车防盗系统的故障诊断提供参考依据。     

高速铁路标准简支箱梁设计优化

高速铁路标准简支箱梁已在我国高速铁路建设中得到应用推广,其经济性对高速铁路建设成本影响较大,有必要开展优化设计研究。在总结前期客运专线建设和运营经验基础上,结合大吨位锚具在实际工程中的应用成果,通过动静力计算,对既有跨度32 m标准简支箱梁进行优化设计,提出不同检算速度下的推荐梁高;对40 m简支箱梁进行系列化设计研究,分析大跨度梁体基频、梁端转角、残余变形等参数影响,提出合理的腹板厚度、梁高等关键截面参数。经设计优化后的高速铁路标准简支箱梁,降低了工程造价,节省了建设周期。     

S7000K-C转辙机防水解决方案探讨

随着防水罩的去除,在用转辙设备自身防水能力难以满足现场要求的问题日益显现,促使各设备厂家针对进水病害的防治启动了新一轮的设计改进。本文以S700K-C转辙机为例,提出了一套“防水+排水结合”为主,“重点病害区优化”为辅的系统化解决方案,为转辙机行业的防水改进提供了一种不同的发展方向和思路。     

The allocation problem of electric car-sharing system: A data-driven approach

Car-sharing is an emerging transportation mode with increasing applications of electric vehicles (EVs). One of the important issues for one-way electric car-sharing systems (ECS) is unbalanced vehicle distributions and high relocation costs. To improve its efficiency and overall profit, this research proposes a data-driven optimization model with the consideration of demand uncertainty. Firstly, a large amount of historical order data from an ECS company are analyzed to characterize the dynamics of the vehicles and the behavioral features of the users. An important observation is that the daily demand by users, i.e., pick-ups, follows Poisson distribution; and the arrival rates vary across time exhibiting four major temporal stages. Based on this observation, this research constructs the ECS reallocation problem as a data-driven optimization model which is a combination of a probability expectation model and a linear programming problem with real-time data as input. More importantly, different from existing research, this research formulates the profit as the mathematical expectation of a discrete random variable with uncertain consumer demands. This allows for a comprehensive consideration of all possible future demands. Furthermore, driving range constraint has been considered in the proposed model as EV is the focus of this paper. A linear solution method is proposed to obtain the global optimal. At the end, the model is validated using real data from 30 ECS stations. The results indicate the daily improvement of profit could be as high as 19.05% with an average of 10.16%.     

轿车前照灯配光镜注塑工艺优化

轿车前照灯配光镜采用聚碳酸酯(PC)料制作。为克服在注塑中前照灯配光镜出现缺陷,对注塑中工艺进行了优化设置,从选材、设备及工艺参数设置等进行了分析,对烘料设备改进和3台模温机的应用提出了独特想法。产品注塑位置详细计算和成型过程得到较好的控制,解决了成型配光镜中出现的缺陷。经生产实践验证,工艺参数优化设置合理,能防止产品缺陷,保证质量可行,配光镜能得到用户的认同。     

Dynamic modelling and experimental validation of three wheeled tilting vehicles

The present paper describes the study of the stability in the straight running of a three-wheeled tilting vehicle for urban and sub-urban mobility. The analysis was carried out by developing a multibody model in the Matlab/SimulinkSimMechanics environment. An Adams-Motorcycle model and an equivalent analytical model were developed for the cross-validation and for highlighting the similarities with the lateral dynamics of motorcycles. Field tests were carried out to validate the model and identify some critical parameters, such as the damping on the steering system. The stability analysis demonstrates that the lateral dynamic motions are characterised by vibration modes that are similar to that of a motorcycle. Additionally, it shows that the wobble mode is significantly affected by the castor trail, whereas it is only slightly affected by the dynamics of the front suspension. For the present case study, the frame compliance also has no influence on the weave and wobble.     

基于ANSYS的汽车后桥焊接变形优化

建立了某汽车后桥的有限元模型,确定了材料热物理性能参数和热源模型,介绍了利用生死单元方法模拟焊缝金属的填充过程,并根据实际情况制定了焊接变形合格范围.采用ANSYS有限元仿真技术对后桥焊接变形进行了预测及优化,通过改变焊缝的焊接顺序及方向实现了抑制焊接变形的目的.结果表明,优化后后桥焊接变形量最小为1.3 mm,模拟计算结果与实测结果相吻合.