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.     

机动目标跟踪典型算法评述

机动目标跟踪算法可以划分为两种：极大似然法和贝叶斯估计法，前者通过对目标量测序列的分析，采用与当前目标运动模式匹配度最高（极大似然性）的模型（包括系统模型和观测模型）对目标状态进行跟踪和估计；后者则首先假定一组模型，再根据量测序列进一步估计出其中每个模型的匹配概率，最后采用贝叶斯公式对全部或部分模型估计进行综合。在此框架下，论文对其中的一些典型算法进行总结和分析。     

公路建设项目超设计概算的现状分析与对策

公路建设项目的批准设计概算是工程建设投资的最高限额,一般不能突破这一限额.但现实建设中,超过批准设计概算的情况很普遍,且调整幅度较大.根据公路工程建设项目各个环节的工程造价控制现状,分析超过批准设计概算的原因,提出控制工程造价的对策与措施.     

应用两步回归迭代算法校正数字式磁罗经误差

基于椭圆假设的参数估计目前是解决数字式磁罗经误差校正问题的较为先进的方法，然而对该方法中的非线性参数估计问题采取了扩展卡尔曼滤波的办法来解决，这造成误差在测量过程中的发散。提出了两步回归迭代算法来解决此问题的思路，即把待估计的非线性参数组合成线性参数，用卡尔曼滤波进行状态估计后，通过Causs-Newton法解得原非线性参数。实验结果证明了新方法的优越性。     

时频分布在FSK信号解调中的应用研究

时频分布作为现代信号处理研究的热点，它能同时描述信号在频率域和时间域内变化。移频轨道电路信号是一种相位连续的移频键控（FSK）信号，它的载频和低频信息通过FFT可以很容易的检测出来，但上下边频的高精度检测，一直是比较棘手的问题。本文提出了一种基于时频分布的FSK信号的解调方法，这种方法可以克服单一时域或频域FSK信号解调时的缺点，实现对FSK信号的上下边频的可靠、高精度的解调。文中对FSK信号的WVD、PWVD和SPWVD结果作了比较，选择了基于SPWVD来估计FSK信号的上下边频，并给出了具体的算法步骤。计算机仿真表明，采用SPWVD可准确地解调出移频信号的上下边频，与FFT相结合，可实现移频信号的完整解调。这为研制高精度的FSK信号检测仪器奠定了理论基础。     

用神经网络分析估计斜拉桥的施工控制参数

结合一座三塔四跨预应力混凝土斜拉桥施工过程,进行用神经网络分析估计斜拉桥施工控制参数的研究。用神经网络分析估计斜拉桥施工控制参数的过程主要包括建立神经网络、计算训练样本、训练神经网络和网络仿真四部分。运用神经网络仿真计算进行斜拉桥施工监控的具体方法是,先计算当前施工状态的索力和标高,再预测下一节段的索力和标高。经过往复循环,逐一进行节段预测调整,从而指导斜拉桥顺利施工。通过对比该斜拉桥合拢后部分节段索力及主梁控制点标高的计算值和实测值可知,用神经网络分析进行斜拉桥施工监控的效果达到设计的理想桥梁线型和索力要求。