铁路枢纽折角车流数值变化的动态模拟

以解编车流作业规则和系统资源限制为约束,构造不同目标函数下铁路枢纽编组站作业分工数学模型,给出折角车流理论最大值和最小值及相应编组站作业分工方式及车流条件。基于折角车流变化的复杂性和多样性,将外部车流环境视为一个动态的随机输入,以特定铁路枢纽为背景用计算机模拟技术和遗传算法计算不同条件下折角车流数值解。通过对模拟数据趋势变化比较分析,提出铁路枢纽运输组织不应过分追求折角车流最小等结论。     

内置式永磁同步电动机无传感器速度控制和转子初始位置估计方法的应用及实验研究

针对内置式永磁同步电动机传动提出了一种新的无速度传感器控制和转子初始位置估计方法.在内置式永磁同步电动机旋转过程中,采用扩展卡尔曼滤波方法,仅需测量电机定子的电压和电流,便可得到转速和转子位置的估计值.对内置式永磁同步电动机,采用扩展卡尔曼滤波方法的主要困难在于在静止坐标系中其动态模型的复杂性.由于磁路的不对称性,在静止坐标系中建内置式永磁同步电动机的模型比建表面式永磁同步电动机模型更复杂.在无传感器的传动系统中,电机的起动过程是一个问题,因为在起动之前没有任何信息可以获得.转子初始位置估计是在电机静止时,在瞬间对定子绕组施加合适的电压脉冲序列,通过测量峰值电流获得转子位置信息.利用磁饱和效应对凸极性的影响来区分南北磁极.给出基于浮点数字信号处理器TMS320C31平台得到的内置式永磁同步电动机实验结果.     

Performance estimation model of a torque converter part I: Correlation between the internal flow field and energy loss coefficient

The objective of this paper is to improve the performance estimation model of the internal flow field of a torque converter. Compared with performance experiment results, the converter based on the one-dimensional model does not satisfy the performance requirements demanded in practice. Therefore, we need to develop more predictable and reliable performance estimation models. In order to obtain shape information on three-dimensional blade geometry, a process of reverse engineering conducts a torque converter assembly, impeller, turbine and stator. In addition, a CFD simulation including mesh generation and post-processing was carried out to extract equivalent parameters from the internal flow field. The internal flow field can be explained by analyze the correlation between a performance estimation model and CFD analysis. The equivalent performance model adopts the variation of energy loss coefficients for a given operating condition according to the application of a changing energy loss coefficient by the least mean squares method. The estimated equivalent model improves the agreement in performance between experiments and the theoretical model. This model can reduce the error to within about 3 percent. Furthermore, this procedure for predicted performance achieves eminence in the estimation of the capacity factor.     

Real-time freeway traffic state estimation based on extended Kalman filter: Adaptive capabilities and real data testing

This paper reports on real data testing of a real-time freeway traffic state estimator, with a particular focus on its adaptive capabilities. The pursued general approach to the real-time adaptive estimation of complete traffic state in freeway stretches or networks is based on stochastic macroscopic traffic flow modeling and extended Kalman filtering. One major innovative feature of the traffic state estimator is the online joint estimation of important model parameters (free speed, critical density, and capacity) and traffic flow variables (flows, mean speeds, and densities), which leads to three significant advantages of the estimator: (1) avoidance of prior model calibration; (2) automatic adaptation to changing external conditions (e.g. weather and lighting conditions, traffic composition, control measures); (3) enabling of incident alarms. These three advantages are demonstrated via suitable real data testing. The achieved testing results are satisfactory and promising for subsequent applications.     

基于遗传支持向量机的综合管廊土建工程造价估算方法研究

综合管廊工程造价估算具有影响因素多、小样本及非线性的特点,传统的造价估算方法对于这类问题无法得到准确结果。针对这一问题,提出一种基于遗传支持向量机的综合管廊造价估算模型。分析综合管廊工程的具体特征,运用支持向量机建立工程造价与影响因子之间的非线性映射关系,利用遗传算法对支持向量机进行参数优化并对综合管廊的工程造价进行估算。以16条已建综合管廊工程作为数据库样本,对该模型进行分析验证。样本测试结果显示,利用该模型可将估算误差控制在10%以内,验证了模型估算的可行性。     

Vehicle response-based track geometry assessment using multi-body simulation

The assessment of the geometry of railway tracks is an indispensable requirement for safe rail traffic. Defects which represent a risk for the safety of the train have to be identified and the necessary measures taken. According to current standards, amplitude thresholds are applied to the track geometry parameters measured by recording cars. This geometry-based assessment has proved its value but suffers from the low correlation between the geometry parameters and the vehicle reactions. Experience shows that some defects leading to critical vehicle reactions are underestimated by this approach. The use of vehicle responses in the track geometry assessment process allows identifying critical defects and improving the maintenance operations. This work presents a vehicle response-based assessment method using multi-body simulation. The choice of the relevant operation conditions and the estimation of the simulation uncertainty are outlined. The defects are identified from exceedances of track geometry and vehicle response parameters. They are then classified using clustering methods and the correlation with vehicle response is analysed. The use of vehicle responses allows the detection of critical defects which are not identified from geometry parameters.     

航空发动机污染物排放量估算方法

借助发动机性能模型, 研究了发动机性能退化对氮氧化物(NO_x)、一氧化碳(CO)、未燃碳氢化合物(UHC)与碳烟颗粒(Soot)排放的影响, 提出了基于飞行参数与发动机性能模型的污染物排放总量估算方法。利用ICAO排放数据拟合得到发动机地面状态下的参考排放指数, 利用相对法模型得到飞行状态下的排放指数, 根据航班飞行参数和发动机性能模型估算航班污染物排放总量。研究结果表明: 性能退化对污染物的排放指数影响较大, 仅考虑进对燃油消耗量的影响, 性能退化对NO_x排放总量的影响较小, 但会引起CO、UHC与Soot排放总量的上升。执行中短途航班的双发民航飞机的NO_x排放总量最高约为100kg, 其次为CO, 约为20kg, 而UHC和Soot的排放总量较低, 小于1kg。老化发动机的CO、UHC与Soot的排放总量增加约为10%, 而NO_x排放总量增加约为2%。     

性能基导航中飞行技术误差估计研究综述

从人为因素、飞行器性能与环境因素3方面剖析了性能基导航中飞行技术误差的影响因素及其特性。依据导航方式与特定运行条件分类论述了基于飞行试验的飞行技术误差试验统计研究, 指出了此类研究方法的局限性。评述了基于成型机理的飞行技术误差方差估计模型与方法, 讨论了各种方法的特点和应用范围。从飞行技术误差估计方法与应用2方面分析了现有研究的局限性与后续研究展望。分析结果表明: 在手动操作方式下, 不同的飞行策略对飞行技术误差有较大影响, 航径偏移指示器的灵敏度与侧向飞行技术误差负相关。在航路和终端区, 某些通用航空飞行器的侧向飞行技术误差的标准差分别高达759.32、481.52m, 主要原因是未安装高精度导航系统。对飞行试验结果进行统计拟合的估计方法无法全面地覆盖全部机型、航段与天气条件, 因而具有一定的局限性, 而基于成型机理的飞行技术误差估计方法估计的侧向、高度飞行技术误差的标准差分别为2.68、1.13m, 通过与仿真结果和实测数据的比较, 方法的有效性得到验证。性能基导航设备在实时估计飞行技术误差时, 采用假设常值而不进行实时估计, 将会使飞行器处于危险中。从多重因素入手研究如何减小性能基导航中飞行技术误差是未来的重要研究方向。     

交汇水域船舶轨迹预测与航行意图识别

针对典型水上交通场景交汇水域,研究了1种数据驱动的船舶轨迹预测与航行意图识别方法。设计CNN+LSTM组合神经网络,通过学习交汇水域船舶的历史轨迹,以CNN+LSTM网络为编码器提取其通航环境及船舶航行时空特征,LSTM与全连接层为解码器同步输出未来时段内船舶轨迹序列和航路选择,从而形成船舶轨迹与航行意图识别模型。同时,引入Dropout网络结构描述该模型的预测不确定性,采用随机关闭CNN+ LSTM核心网络部分神经单元的方式,以相同轨迹序列作为输入获取多组相近的预测结果,根据其统计均值与方差对船舶轨迹预测的不确定性进行量化。以美国沿海某交汇水域公开AIS数据为对象开展实验,创建了该交汇水域船舶航行轨迹数据集,以输入时长60 min,采样频率3 min作为输入条件,Dropout值取0.5,实验结果表明：所提方法对未来60 min时段内的轨迹预测误差为3.946 n mile,航行意图识别准确率达87%,不确定性估计覆盖率达85.7%。与LSTM预测方法相比,当船舶操纵性发生改变时,所提CNN+LSTM模型的轨迹预测误差降低了31.6%,而且兼具船舶航行意图识别及预测不确定性估计能力,有利于智能航行与海事监管技术发展。