新冠肺炎疫情对交通运输行业的影响及政策建议

2019年底开始的新型冠状病毒肺炎疫情对交通运输行业造成了明显冲击。为分析疫情对当前交通运输行业带来的影响,重点从客运、货运、港口生产、交通固定资产投资四方面进行了研究。然后,提出了四方面政策建议,包括加强交通运输疫情防控、积极发挥交通投资稳定器作用、加大高速公路通行服务保障和确保关系国计民生的重点物资基本运输需求,以便为各级交通运输管理部门决策提供参考,争取将疫情对行业的影响降到最低程度。     

纯电动汽车能量回收控制策略研究

纯电动汽车的能量回收技术有很高的研究价值,是提高整车经济性的有效手段之一。以一种并联能量回收方案为研究对象,从再生制动原理进行分析,在电池回收能力、电机回收能力及负载功率消耗的限制前提下,提出一个合理的能量回收控制策略,兼顾整车的经济性及乘客的舒适性,并通过ADVISOR进行NEDC工况仿真验证。试验表明,与无能量回收车型对比,相同时间内该策略下整车电池SOC值明显下降缓慢,该研究为制动能量回收策略算法的进一步优化开发提供参照。     

弹性工作制和动态停车收费组合策略研究

提出弹性工作制与动态停车收费的组合管理策略,探究组合策略下,出行分布的变化机理及动态停车收费费率的制定,以达到缓解通勤拥堵的目的. 构建双层规划模型建立动态停车收费费率的制定与多用户出行选择的相互作用关系,基于灵敏度分析的下降算法求解. 仿真模拟出不同弹性工作制规模时,均衡状态下多用户出行分布模式和优化的动态收费费率. 结果表明,组合策略能促使弹性与非弹性员工错峰出行,有效分散交通流,与单一策略相比,缓解路网拥堵的效果更好.研究成果为交通管理优化策略的制定提供了新思路.     

圆钢管H型钢再生混凝土短柱的轴压承载力分析

为了研究圆钢管H型钢再生混凝土短柱轴压力学性能,对此类构件轴压承载力计算公式进行了理论推导,基于极限分析法,运用双剪统一强度理论,并依据H型钢和钢管对核心区再生混凝土约束效果的不同,分别计算H型钢约束区再生混凝土和钢管约束区再生混凝土承载力,提出一套圆钢管H型钢再生混凝土短柱轴压承载力计算公式,考虑了钢管内径厚比、套箍系数、H型钢配钢指标以及再生粗骨料取代率对短柱轴压承载力的影响, 同时也适用于无H型钢的圆钢管再生混凝土短柱轴压承载力计算. 将推导得到的钢管有效约束力代入承载力计算公式所得结果与相关试验结果对比误差在10%以内,吻合较好,验证了承载力计算公式的有效性和精确度.      

四轮转向汽车操纵稳定性研究

为了研究转向工况对四轮转向汽车操纵稳定性的影响,基于Matlab/Simulink建立四轮转向汽车前轮转角比例前馈加横摆角速度模糊PID反馈控制模型,通过与Trucksim车辆模型和Simulink控制模型联合仿真,分别在低速和中高速下进行方向盘角阶跃输入离线仿真和方向盘正弦角输入实时仿真试验,与前轮转向汽车在相同工况下侧向加速度、横摆角速度以及质心侧偏角的仿真结果进行对比分析。试验结果表明:四轮转向控制仿真结果优于前轮转向结果,搭建的四轮转向前轮转角比例前馈加横摆角速度模糊PID反馈控制策略,能提高汽车低速转向时的操纵轻便性和机动性以及中高速转向时的操纵稳定性。     

感应加热电源并联控制系统的通信设计与实现

基于西门子S7-200 SMART PLC和DSP处理器,设计了感应加热电源并联控制系统,建立了DSP和PLC之间、PLC与触摸屏HMI之间的通信协议并编写程序,实现了DSP与PLC之间的数据传输。实际应用表明:该系统通信效果良好,具有较强的实用价值。     

手动-自动驾驶混合交通流元胞传输模型

为了分析自动驾驶车辆对交通流宏观特性的影响, 以手动驾驶车辆与自动驾驶车辆构成的混合交通流为研究对象, 提出了不同自动驾驶车辆比例下的混合交通流元胞传输模型(CTM); 应用Newell跟驰模型作为手动驾驶车辆跟驰模型, 应用PATH实验室真车测试标定的模型作为自动驾驶车辆跟驰模型; 计算了手动驾驶与自动驾驶车辆跟驰模型在均衡态的车头间距-速度函数关系式, 推导了不同自动驾驶车辆比例下的混合交通流基本图模型, 计算了混合交通流在不同自动驾驶车辆比例下的最大通行能力、最大拥挤密度以及反向波速等特征量, 依据同质交通流CTM理论建立了不同自动驾驶车辆比例下的混合交通流CTM; 选取移动瓶颈问题进行算例分析, 应用混合交通流CTM计算了不同自动驾驶车辆比例下的移动瓶颈影响时间, 应用跟驰模型对移动瓶颈问题进行微观数值仿真, 分析了混合交通流CTM计算结果与跟驰模型微观仿真结果之间的误差, 验证了混合交通流CTM的准确性。研究结果表明: 混合交通流CTM能够有效计算移动瓶颈的影响时间, 在不同自动驾驶车辆比例下, 混合交通流CTM计算结果与跟驰模型微观仿真结果的误差均在52 s以下, 相对误差均小于10%, 表明了混合交通流CTM在实际应用中的准确性; 混合交通流CTM体现了从微观到宏观的研究思路, 基于微观跟驰模型与目前逐步开展的小规模自动驾驶真车试验之间的关联性, 混合交通流CTM能够较真实地反映未来不同自动驾驶车辆比例下单车道混合交通流演化过程, 增加了模型研究的应用价值。      

Magnetic field tuning characteristics of bimodal ultrasonic motor stator

The magnetic field tuning characteristics of an ultrasonic motor (USM) stator are discussed. The stator consists of two piezoelectric ceramic transducer (PZT) plates and one sandwiched-in Terfenol-D plate. The dimensions of the stator are carefully adjusted to specifically discuss the influence of the magnetic field on the frequency difference between the longitudinal and bending modes of the stator. The frequency difference discussed in this paper is usually small and mainly caused by uneven materials, machining errors and changes in external conditions (temperature, pre-stress or load). The longitudinal and bending modes of the stator are simultaneously excited by an external electric field to generate the elliptic motion trajectories of the driving points. A direct current (DC) magnetic field is applied to decrease the difference between the two mode frequencies of the fabricated stator. In experiments, the dependences of the two mode frequencies and their difference on DC magnetic fields are all investigated. The experimental results indicate that the difference between the longitudinal and bending mode frequencies of the PZT/Terfenol-D/PZT composite stator can be tuned by changing the intensity of the external DC magnetic field.     

Fault diagnosis for wind turbine based on improved extreme learning machine

A fault diagnosis method based on improved extreme learning machine (IELM) is proposed to solve the weakness (weak generalization ability, low diagnostic rate) of traditional fault diagnosis with feedforward neural network algorithm. This method fuses signal feature vectors, extracts six parameters as the principal component analysis (PCA) variables, and calculates correlation coefficient matrix among the variables. The weight values of control parameters in the extreme learning model are dynamically adjusted according to the test samples’ constantly changing. Consequently, the weight fixed drawback in the original model can be remedied. A fault simulation experiment platform for wind turbine drive system is built, eight kinds of fault modes are diagnosed by the improved extreme learning model, and the result is compared with that of other machine learning methods. The experiment indicates that the method can enhance the accuracy and generalization ability of diagnosis, and increase the computing speed. It is convenient for engineering application.     

Calculation of strain amplification matrix for strain invariant failure theory based on representative volume element models with periodical boundary condition

Strain invariant failure theory (SIFT) is a micro-mechanics-based failure theory for multi-scale failure analysis of composite materials originally proposed by Gosse and Christensen. In this paper, the approach for obtaining strain amplification matrix which is a key step for the execution of SIFT is improved by adopting representative volume element (RVE) finite element models considering periodical boundary condition, based on which more actual deformation mode is reflected. The deformation modes and strain data at the characteristic points of the centroid cell of multi-cell RVE model are analyzed and taken as a reference. It can be concluded that more reasonable deformation mode and relationship between the micro-mechanical and macro-mechanical strain states are obtained by employing the new model. Finally, numerical examples are provided to illustrate the determination of strain amplification factors within the RVEs considering periodical boundary condition at the characteristic points.