东篱科研大数据发现系统（DRDS）

位置：成果数据库 > 期刊 > 期刊详情页

The Velocity Measurement of Two-phase Flow Based on Particle Swarm Optimization Algorithm and Nonlinear Blind Source Separation

ISSN号：1004-9541
期刊名称：《中国化学工程学报：英文版》
时间：0
分类：TN911.7[电子电信—通信与信息系统;电子电信—信息与通信工程] TP301.6[自动化与计算机技术—计算机系统结构;自动化与计算机技术—计算机科学与技术]
作者机构：[1]College of Physics, Liaoning University, Shenyang 110036, China, [2]Key Laboratory of Condition Monitoring and Control for Power Plant Equipment, Ministry of Education, North China Electric Power University, Beijing 102206, China, [3]School of Information Science and Engineering, Northeastern University, Shenyang 110006, China
相关基金：Supported by the National Natural Science Foundation of China （50736002, 61072005）; the Youth Backbone Teacher Project of University, Ministry of Education, China; the Scientific Research Foundation of the Department of Science and Technology of Liaoning Province （20102082）; and the Changjiang Scholars and Innovative Team Development Plan （IRT0952）.

作者：吴新杰, 崔春阳, 胡晟, 李志宏, 吴成东

关键词：非线性盲源分离, 粒子群优化算法, 速度测量, 两相流, 电容式传感器, 矩阵多项式, 互信息估计, 输出信号, particle swarm optimization, nonlinear blind source separation, velocity, cross correlation method

中文摘要：

以便克服噪音的骚乱，这份报纸论述了一个方法用粒子群优化算法，非线性的瞎的来源分离和十字测量二阶段的流动速度关联方法。因为在在管道的电容传感器和液体的产量信号之间的非线性的关系，非线性的瞎的来源分离被使用。在非线性的瞎的来源分离，更高的顺序的奇怪的多项式被用来适合非线性的转变函数，并且分离信号的相互的信息被用作评估函数。然后，多项式和线性分离矩阵的参数能被分离信号和粒子群优化算法的相互的信息估计，因此，来源信号能与混合信号被分开。有从在上游、下游的传感器被获得的噪音的二阶段的流动信号被非线性的瞎的来源分离方法分别地处理以便噪音能有效地被移开。基于这些压制噪音的信号，因此，生气关联功能和运输时间的不同曲线被获得，然后二阶段的流动的速度能是精确地计算的。最后，模拟试验性的结果被给。结果证明了这个方法能满足二阶段的流动速度的测量要求。

英文摘要：

In order to overcome the disturbance of noise,this paper presented a method to measure two-phase flow velocity using particle swarm optimization algorithm,nonlinear blind source separation and cross correlation method.Because of the nonlinear relationship between the output signals of capacitance sensors and fluid in pipeline,nonlinear blind source separation is applied.In nonlinear blind source separation,the odd polynomials of higher order are used to fit the nonlinear transformation function,and the mutual information of separation signals is used as the evaluation function.Then the parameters of polynomial and linear separation matrix can be estimated by mutual information of separation signals and particle swarm optimization algorithm,thus the source signals can be separated from the mixed signals.The two-phase flow signals with noise which are obtained from upstream and downstream sensors are respectively processed by nonlinear blind source separation method so that the noise can be effectively removed.Therefore,based on these noise-suppressed signals,the distinct curves of cross correlation function and the transit times are obtained,and then the velocities of two-phase flow can be accurately calculated.Finally,the simulation experimental results are given.The results have proved that this method can meet the measurement requirements of two-phase flow velocity.

同期刊论文项目