轴承-转子系统稳定性实验研究

研究了圆轴承非线性油膜力支承下转子系统动力学特性及稳定性,以及不平衡量及轴承参数对稳定性影响规律.结果显示,适当增大转子不平衡量、减小轴承长径比、降低润滑油黏度、减小轴承间隙会提高转子-轴承系统稳定性.对圆轴承支承下转子-轴承系统动力学特性进行了实验研究,给出了系统油膜失稳规律,验证了理论分析结果,同时验证了提出的模型正确.研究结果为油膜失稳故障预测及防范提供参考.     

不同油膜力模型下转子椭圆轴承系统的动力学分析

1引言滑动轴承的非线性油膜力模型是转子-轴承系统非线性动力学分析和研究的关键问题。滑动轴承的油膜力具有强烈的非线性特性,研究转子-椭圆轴承系统的非线性动力学问题关键在于获得椭圆轴承的油膜力模型。其中椭圆轴承的非线性油膜力的计算精度和计算速度将直接影响到转子-轴     

线性与非线性油膜力模型下转子振动稳定性对比研究

滑动轴承的油膜力直接影响转子振动的稳定性,首次对大型多自由度转子系统在线性和非线性油膜力作用下的振动稳定性进行了对比分析。针对200Mw汽轮机低压转子一轴承系统,采用数值计算方法分别模拟了转子在非线性油膜力和传统的8个刚度和阻尼系数表示的线性油膜力模型下,转子升速过程中发生油膜失稳的典型特征。通过比较分析得出考虑油膜力的非线性特性后转子发生油膜振荡的转速提前。因此为保证大型转子-轴承系统的安全稳定运行,有必要在传统线性理论设计的基础上,引入非线性油膜力形式对系统的稳定性作进一步的计算校验。     

横向流体激振力作用下叶轮转子的分岔特性

将离心叶轮转子系统简化为Jeffcott转子,建立了带有支座松动与碰摩耦合故障的不平衡离心叶轮转子在非线性横向流体激振力和非线性轴承油膜力作用下的振动分析模型,并推导了系统的无量纲运动方程.运用数值积分法研究了系统的分岔特性,最后分析了横向流体激振力对含有松动与碰摩的离心叶轮转子系统动力学性能的影响.结果表明:在转速较高时,横向流体激振力对该类转子的分岔特性有较大影响.     

挠性转子动平衡参数识别法研究

探讨了运用模态参数识别技术进行挠性转子动平衡的方法。不必加试重只需一次升速,将动不平衡量视作转子系统的模态参数,根据转子的振动响应运用模态参数识别技术确定转子系统的各阶模态不平衡量。     

200MW汽轮机低压转子-轴承系统的非线性动力学分析

采用数值计算方法对实际大型转子-轴承系统的非线性动力学特性进行了研究，计算结果能与现场的运行经验很好地吻合。用有限元法建立了200Mw汽轮机低压转子-轴承系统的非线性振动微分方程。采用Newmark逐步积分法对转子在升、降速过程中的振动响应进行了数值仿真，得到了转子发生油膜失稳的转速和油膜振荡的“惯性迟滞”现象。对转子的重力和不平衡量对系统的油膜涡动和油膜振荡的影响进行了计算和分析。计算得到了转子的振动随转子的偏心距、轴承的长径比、间隙比、润滑油粘度的变化规律，分析结果为定量和定性分析该类机组转子-轴承系统的稳定性提供了参考依据。图15表1参7     

转子-轴承系统的非线性动力学的研究

对转子 -轴承系统的混沌运动进行动力学研究 ,采用非线性油膜力数据库方法获得椭圆轴承的非线性油膜力 ,数值计算得到了系统在某些参数域中的分叉图、轴心轨迹、相图、Poincar啨映射、时域波形和频谱图 ,直观显示了系统发生混沌运动的性态 ,用李雅普诺夫指数对混沌运动时的时间序列进行了判断 ,为控制转子系统混沌运动的发生及动力学设计提供了理论基础     

滑动轴承非线性油膜力模型的对比分析

在转子-轴承系统的非线性动力学分析和设计中,滑动轴承的非线性油膜力模型的选取,将直接影响到非线性动力学分析结果的准确程度.为了搞清选取的非线性油膜力模型的计算结果与直接采有有限差分数值法解Reynolds方程的计算结果究竟有多大差别,对无限短轴承模型、无限长轴承模型、非稳态短轴承模型、基于Poincare变换的滑动轴承非线性油膜力数据库模型与直接采用有限差分数值法解Reynolds方程的计算结果进行了对比分析.通过对比分析表明:这些模型与有限差分数值法解Reynolds方程的计算结果在不同长径比下均存在不同程度的误差.这为进行转子-轴承系统的非线性动力学分析时选取恰当的油膜力模型提供了理论指导.     

碰摩拉杆转子非线性动力学响应特性

对定点碰摩故障状态下拉杆转子的非线性动力学响应特性进行了研究.将轮盘之间的非线性接触特性等效为具有非线性抗弯刚度的弹簧,基于达朗贝尔原理建立了考虑轮盘之间非线性接触特性、非线性油膜力以及碰摩力作用的拉杆转子轴承系统运动方程,并采用数值积分方法对运动方程进行求解,分析了系统位移响应随轮盘的转速和碰摩刚度等参数变化的规律.结果表明:轮盘之间的非线性接触对系统位移响应特性影响较大;随着轮盘转速的升高,系统呈非线性特性;碰摩刚度是影响拉杆转子轴承系统运动状态的重要因素,若增大碰摩刚度,系统的运动状态将发生改变.     

轴承标高对多跨转子-轴承系统非线性稳定性的影响研究

建立了考虑转子-轴承标高与非线性油膜力的多跨转子-轴承系统高维非线性动力学模型。利用固定界面模态综合法对该模型进行降维,采用Newmark逐步积分法对降维后的动力学模型进行数值计算,可得到转子在各种标高情况下发生油膜失稳的转速门槛值和轴承标高变化对系统动力稳定性的影响规律。对200MW汽轮发电机组转子-轴承系统计算分析表明：由于低压转子后轴承的标高抬起,将导致发电机转子前轴承轴颈的失稳转速显著提前,系统的稳定性降低。分析结果从理论上解释了电站200MW汽轮发电机组发电机前轴承处转子易发生油膜失稳的原因,并且证明了本文所提出方法的有效性。     

汽轮发电机组轴系--支撑系统油膜失稳问题探讨

将油膜涡动归为汽轮发电机组轴系－支撑系统的固有振动,讨论了影响油膜稳定性的因素,并给出了消除油膜失稳的措施     

Synchronous machine parameters estimation using a new genetic‐based algorithm

A new genetic‐based algorithm (GA) for estimating synchronous machine parameters from frequency tests is presented in this paper. GAs are general search techniques based on biological concepts and are very suitable for solving optimization problems. The proposed method uses a set of digital measurements for the direct axis impedance magnitude and phase as functions of frequency for estimating both the d‐ and q‐axis parameters, such as direct reactance and time constants. The problem is formulated as an optimization problem and solved using the proposed method. Two different models along with different fitness functions are suggested to be used with the genetic algorithm. A practical example from the literature is used to test the proposed algorithm. The results obtained are compared with those given in the literature using other methods. The results and comparison show that the new algorithm is very applicable and highly accurate. Copyright © 1999 John Wiley and Sons, Ltd.     

滑动轴承非线性动态油膜压力分布特性的研究

针对有限长径向滑动轴承非线性动态油膜压力的分析与计算,定量阐明了非稳定润滑油膜的动态压力特性,进一步研究了轴承中转子自转速度、涡动速度及转子偏心速度等参数对非线性油膜压力分布变化规律的影响。图8参6。     

摩托车汽油机不平衡力幅值的研究

发动机不平衡力输出是引起摩托车振动的直接原因，对发动机不平衡力的研究是对整车振动研究的基础。它是进行振动模拟计算的边界条件，直接影响到计算结果的正确性。本文不仅推导出各不平衡力随曲柄转角的变化关系，而且着重考察了随转速变化的各不平衡力幅值在频域内的变化趋势，从而使采用省时的频域计算法来模拟计算整车振动成为可能。     

Electro-thermal battery model identification for automotive applications

This paper describes a model identification procedure for identifying an electro-thermal model of lithium ion batteries used in automotive applications. The dynamic model structure adopted is based on an equivalent circuit model whose parameters are scheduled on the state-of-charge, temperature, and current direction. Linear spline functions are used as the functional form for the parametric dependence. The model identified in this way is valid inside a large range of temperatures and state-of-charge, so that the resulting model can be used for automotive applications such as on-board estimation of the state-of-charge and state-of-health. The model coefficients are identified using a multiple step genetic algorithm based optimization procedure designed for large scale optimization problems. The validity of the procedure is demonstrated experimentally for an A123 lithium ion iron-phosphate battery.     

基于遗传算法优化支持向量机的电能质量暂态扰动识别新方法

针对电能质量识别领域中,采用随机参数的支持向量机(SVM)分类器识别随机暂态扰动信号准确率低、优化耗时长等问题,提出一种基于遗传算法(GA)优化SVM识别电能质量暂态扰动(PQD)的新方法(GASVM)。首先,仿真生成具有随机噪声水平和扰动参数的9种PQD信号;接着,通过S变换,提取出6种信号特征构成输入特征向量,用于训练SVM分类器;再采用GA对SVM进行参数寻优,进而获得优化的GA-SVM分类器;最后,采用GA-SVM识别PQD信号。仿真对比试验表明,新方法能准确识别不同噪声环境下的9种PQD信号,分类准确率及优化所需时间均优于PSO优化SVM方法(PSO-SVM)。     

累计储量模型的非线性最小二乘估计

确定累计储量模型中的待定参数需要求解一个三维数值优化问题,常规算法难以给出合适的迭代初始值,并且迭代过程容易收敛于局部极小点。将三维优化问题转换成一元非线性方程的求解,给出了该一元方程的解区间搜索算法和二分法求解算法。实际算例表明．本文算法不仅计算过程稳定、计算速度快,而且计算误差小。本文算法可应用于石油地质储量、油田产量预测等一系列实际问题。     

人工神经网络模拟生物膜滴滤塔废气处理过程

在生物滴滤塔处理有机废气过程中，参数非线性度大，其处理过程适合用人工神经网络(ANN)模拟。通过建立神经网络模拟生物滴滤塔处理有机废气的过程，对遗传算法的改进能够提高其收敛速度，并采用改进遗传算法(MGA)与LMBP算法相结合(MGA—LMBP)，利用已有的实验数据样本训练神经网络，取得了较好的效果。     

Optimum Pareto design of non-linear predictive control with multi-design variables for PEM fuel cell

The control process of Proton Exchange Membrane Fuel Cells (PEMFCs) is a difficult task due to the non-linearities and uncertainties associated with the electrochemical processes governing it. Designing of a non-linear controller based on model predictive control for PEMFCs is presented in some previous works to regulate the cell voltage or power output based on just one of the input variables like hydrogen pressure or operating temperature respectively, but they use a constant signal for other important control variables due to computational limitations in on-line optimization of multivariable highly non-linear system. In this paper, by use of Approximate Predictive Control (APC) method based on neural network model, the whole control process is designed with three input variables. Operating temperature and hydrogen pressure are assumed as control design variables and current density as measured disturbance to manage the cell voltage. Moreover, multi-objective optimization based on multi-objective uniform-diversity genetic algorithm (MUGA) is used for optimal selection of the parameters of controller. The comparison of the obtained results with those in literature demonstrates the superiority of the results of this work.     

Intelligent soft-computing based modelling of naturally ventilated buildings

The paper presents recent results on the application of the soft computing methodology for modelling of the internal climate in office buildings. More specifically, a part of a recently completed naturally ventilated building is considered which comprises three neighbouring offices and one corridor within the Portland Building at the University of Portsmouth. The approach adopted uses fuzzy logic for modelling, neural networks for adaptation and genetic algorithms for optimisation of the fuzzy model. The fuzzy models are of the Takagi-Sugeno type and are built by subtractive clustering. As a result of the latter, the initial values of the antecedent non-linear membership functions and the consequent linear algebraic equations parameters are determined. A method of extensive search of fuzzy model structures is presented which fully explores the dynamics of the plant. The model parameters are further adjusted by a back-propagation training neural network and a real-valued genetic algorithm in order to obtain a better fit to the measured data. Results with real data are presented for two types of models, namely Regression Delay and Proportional Difference. These models are applied for predicting internal air temperatures.