涡凹气浮联合固定化曝气生物滤池在含油废水中的应用

采用CAF联合BAF工艺处理高浓度炼油生产废水,并利用浮动环流收油器,对预处理废水中的油类污染物进行回收,无需外加压力,处理成本低。经该工艺二级气浮处理后,油类污染物回收率达到97％。工艺调试运行稳定后,对污染物COD、氨氮、硫化物和石油类进行监测。监测结果表明,COD、氨氮、硫化物和石油类污染物的去除率分别为：96．1％、94．3％、77％和96．7％,出水水质满足GB8978--1996《污水综合排放标准》中第二类污染物最高允许排放浓度一级标准要求。     

液压挖掘机液压系统清洁度的控制

液压系统是液压挖掘机的一个重要组成部分,保持液压系统的清洁度可以降低机器发生故障而停机的几率.主要介绍了通过控制污染物的生成,减少残留污染物和正确选用与合理使用滤油器等方法来控制液压系统的清洁度.     

Wave data assimilation with the Kalman filter

A new data assimilation method for ocean waves is presented, based on an efficient low-rank approximation to the Kalman filter. Both the extended Kalman filter and a truncated second-order filter are implemented. In order to explicitly estimate past wind corrections based on current wave measurements, the filter is extended to a fixed-lag Kalman smoother for the wind fields. The filter is tested in a number of synthetic experiments with simple geometries. Propagation experiments with errors in the boundary condition showed that the KF was able to accurately propagate forecast errors, resulting in spatially varying error correlations, which would be impossible to model with time-independent assimilation methods like OI. An explicit comparison with an OI assimilation scheme showed that the KF also is superior in estimating the sea state at some distance from the observations. In experiments with errors in the driving wind, the modeled error estimates were also in agreement with the actual forecast errors. The bias in the state estimate, which is introduced through the nonlinear dependence of the waves on the driving wind field, was largely removed by the second-order filter, even without actually assimilating data. Assimilation of wave observations resulted in an improved wave analysis and in correction of past wind fields. The accuracy of this wind correction depends strongly on the actual place and time of wave generation, which is correctly modeled by the error estimate supplied by the Kalman filter. In summary, the KF approach is shown to be a reliable assimilation scheme in these simple experiments, and has the advantage over other assimilation methods that it supplies explicit dynamical error estimates.     

Assimilation of altimetric data in the mid-latitude oceans using the Singular Evolutive Extended Kalman filter with an eddy-resolving, primitive equation model

A new data assimilation scheme has been elaborated for ocean circulation models based on the concept of an evolutive, reduced-order Kalman filter. The dimension of the assimilation problem is reduced by expressing the initial error covariance matrix as a truncated series of orthogonal perturbations. This error sub-space evolves during the assimilation so as to capture the growing modes of the estimation error. The algorithm has been formulated in quite a general fashion to make it tractable with a large variety of ocean models and measurement types. In the present paper, we have examined three possible strategies to compute the evolution of the error subspace in the so-called Singular Evolutive Extended Kalman (SEEK) filter: the steady filter considers a time-independent error sub-space, the apprentice filter progressively enriches the error sub-space with the information learned from the innovation vector after each analysis step, and the dynamical filter updates the error sub-space according to the model dynamics. The SEEK filter has been implemented to assimilate synthetic observations of the surface topography in a non-linear, primitive equation model that uses density as vertical coordinate. A simplified box configuration has been adopted to simulate a Gulf Stream-like current and its associated eddies and gyres with a resolution of 20 km in the horizontal, and 4 levels in the vertical. The concept of twin experiments is used to demonstrate that the conventional SEEK filter must be complemented by a learning mechanism in order to model the misrepresented tail of the error covariance matrix. An approach based on the vertical physics of the isopycnal model, is shown particularly robust to control the velocity field in deep layers with surface observations only. The cost of the method makes it a suitable candidate for large-size assimilation problems and operational applications.     

基于小波分析的齿轮箱故障诊断

通过对齿轮箱噪声机理进行研究,建立了相应的数学模型,提出了一种基于小波变换的故障诊断方法.其基本思想是:首先利用小波变换的多分辨率特性,滤除信号的随机噪声,并将提纯后的信号分解到不同的频区;通过对各频区信号进行比较与分析,选取能量集中、包含了调制成分的部分进行复小波变换,利用多尺度小波包络谱获得信号的包络信息;最后,结合傅里叶频谱,确定齿轮箱的故障原因.研究结果表明:该方法能有效识别出齿轮箱系统中轴和齿轮的缺陷,并能锁定故障发生位置、衡量故障程度.     

Using the lead vehicle as preview sensor in convoy vehicle active suspension control

Both ride quality and roadholding of actively suspended vehicles can be improved by sensing the road ahead of the vehicle and using this information in a preview controller. Previous applications have used look-ahead sensors mounted on the front bumper to measure terrain beneath. Such sensors are vulnerable, potentially confused by water, snow, or other soft obstacles and offer a fixed preview time. For convoy vehicle applications, this paper proposes using the overall response of the preceding vehicle(s) to generate preview controller information for follower vehicles. A robust observer is used to estimate the states of a quarter-car vehicle model, from which road profile is estimated and passed on to the follower vehicle(s) to generate a preview function. The preview-active suspension, implemented in discrete time using a shift register approach to improve simulation time, reduces sprung mass acceleration and dynamic tyre deflection peaks by more than 50% and 40%, respectively. Terrain can change from one vehicle to the next if a loose obstacle is dislodged, or if the vehicle paths are sufficiently different so that one vehicle misses a discrete road event. The resulting spurious preview information can give suspension performance worse than that of a passive or conventional active system. In this paper, each vehicle can effectively estimate the road profile based on its own state trajectory. By comparing its own road estimate with the preview information, preview errors can be detected and suspension control quickly switched from preview to conventional active control to preserve performance improvements compared to passive suspensions.     

高速磁悬浮列车间隙信号数字滤波器设计与实现

分析了高速磁浮列车间隙信号噪声的特点,采用基于FPGA的FIR数字滤波器滤除噪声。应用基于位级联的多查找表分布式算法实现16阶低通FIR滤波器,由verilog-HDL语言完成设计文件,并在Simulink及ModelSim ISE 6.1平台上进行了试验仿真,结果均满足设计要求。     

恶劣海况下船舶航向控制仿真及应用研究

船舶在海面航行时,会受到风浪的干扰.此时,船舶航向控制困难,操舵频繁.采用Kalman滤波和模糊自整定PID控制,并基于线性化船舶运动方程的线性时,不变连续时间系统的设计方法得到的船舶操纵控制器,具有抗干扰能力强、鲁棒性好的特点,有效地解决了船舶在风浪干扰条件下的船舶航向控制时的操舵频繁与无效操舵问题.     

基于双重采样粒子滤波的目标轨迹跟踪研究

为保证对目标轨迹跟踪的实时性,提高其精度,提出双重采样粒子滤波模型。首先,利用粒子聚合技术对状态空间的粒子权值进行加权平均处理,形成聚合粒子,使得粒子在状态空间内分布更为合理。然后,利用线性优化方法重新产生新的粒子,优化粒子在状态空间的分布特性,增加粒子的多样性,提高算法的精确性。经仿真验证,本文提出的算法较准确,鲁棒性也较高。     

车用柴油机微粒捕集器热再生的一维数值模拟

以壁流式蜂窝陶瓷微粒捕集器(DPF)为研究对象,建立了DPF一维热再生过程的数学模型。对陶瓷孔道内微粒(PM)的燃烧过程及其温度分布进行了模拟,结果表明,再生过程进行到一半时,DPF载体的温度达到最大值。设计时必须保证在此温度下DPF的载体壁面不会发生破裂,以免影响其正常工作。