温度场模糊随机有限元法研究

不确定性广泛存在于工程分析和设计过程中,正确估计不确定参数对温度分布的影响,对工程热设计具有十分重要的意义。以飞机刹车装置为例将模糊随机参数模型引入热传导温度场研究中,使用模糊随机参数模型对温度场不确定性进行描述,对随机性进行泰勒展开,建立方程求解温度响应变化区间。最后给出数值算例,说明模糊随机对温度场的影响,分析了各参数具有不确定性时,温度响应的模糊性与随机性,得到了相应条件下温度场响应,证明了使用模糊随机参数所建立的瞬态温度场模型合理、可行,为工程热设计提供了理论支持。     

单自由度不确定滞回系统振动响应的区间分析方法

利用区间理论并结合求解不确定非线性结构动力学响应的泰勒方法的二阶展开,推导出求解由滞回环本身的不确定性引起的、单自由度不确定滞回系统响应的有效数值方法,得到了系统响应的上下界.并与概率分析方法求得的系统响应进行比较分析,其计算结果与概率方法结果基本相吻合.当求解由滞回环本身的不确定性引起的非线性振动系统的不确定响应问题,而滞回环本身的不确定性统计信息较少概率方法无法适用时,利用本文所推导的区间方法可为工程实际提供参考.     

保证瞬态性能的迭代学习控制

讨论一类不确定非线性系统的可保证瞬态性能的迭代学习控制问题.引入限定跟踪误差瞬态特性的界函数,通过误差转换方法,定义一个转换误差变量,将跟踪误差的保证瞬态特性问题转化为该误差变量的有界性问题.采用Lyapunov方法,设计迭代学习控制器处理系统中参数和非参数不确定性.并且,采用完全限幅学习机制,保证转换误差变量的有界性和一致收敛性.从而既能得出系统输出在整个作业区间的完全跟踪性能,同时又能够保证跟踪误差在每次迭代的过程中具有保证的瞬态特性.仿真结果验证了所提控制方法的有效性.     

不确定性参数灵敏度分析的随机响应面法

动力学和控制系统中往往包含有不确定性参数,为此提出了一种基于随机响应面的不确定性参数灵敏度分析方法,以量化参数不确定性对响应变异性的影响.文中首先利用随机响应面建立不确定性参数和响应之间的表达式,然后通过求偏导方式推导参数的灵敏度系数,该系数综合反映了参数均值和标准差的影响.最后通过一根包含几何、材料不确定参数的数值梁来验证所提出方法,并与方差分析法结果进行了比较.     

基于LabVIEW和三点加重法的转子动平衡测试系统设计

基于LabVIEW开发了转子动平衡测试系统,使用单平面的三点加重方法,在转子上分别进行3次配重,通过测量转子振动速度、转速以及相互垂直方向上的轴心位移,计算转子的不平衡质量和角度,同时对振动参数进行时频分析,绘制轴心轨迹.实验结果证明了该方法的高精确性.     

转子突加不平衡的碰摩响应

航空发动机等高速旋转机械在工作时会出现叶片掉块、飞失等突加不平衡情况,突加不平衡引发的碰摩可能导致转子失稳和系统的破坏.本文对一个具有非线性支承刚度的跨中转子在突加不平衡时的碰摩响应进行了数值仿真,计算发现系统在突加不平衡发生后将会出现6种不同的碰摩响应形式,给出了参数-突加不平衡转速平面上不同稳态动力学响应模式的边界曲线,同时针对系统在突加不平衡发生后减速通过临界转速时的失稳现象,给出了失稳边界,研究表明增大转子阻尼,降低转静接触刚度和摩擦系数,可以减小失稳的转速范围.分析了参数对系统瞬态响应的影响,瞬态响应随转速的变化会发生跳跃,而瞬态响应随突加不平衡偏心量的变化,在低于共振转速时是平缓增大的,而在高于共振转速时也会有跳跃发生.     

广义区间系统非结构化参数不确定性的鲁棒滤波算法

本文针对广义区间系统的参数不确定性,将参数不确定性确定为随机非结构化参数形式,提出一种卡尔曼形式的递推鲁棒滤波算法.研究表明,滤波过程中的随机非结构化参数不确定性可以表示为一系列依赖系统真实状态的不确定性集合,数值仿真结果表明,当广义区间系统参数存在随机非结构化不确定性时,该算法能够实现递推状态估计,从而验证了该算法的有效性.     

大型汽轮发电机组现场动平衡试验研究进展

对于高参数、高效率、复杂轴系的大型超临界汽轮发电机组,启停成本过高,提高轴系动平衡的精度与效率,成为降低轴系动平衡的成本的关键要素。介绍了转子不平衡故障机理与振动特征,梳理了基于振动信号处理及深度学习的轴系不平衡故障识别方法,并列举了多种轴系动不平衡量的计算方法,建立了现场轴系动平衡治理框架。该框架融合了转子不平衡识别、动平衡修正以及轴系动平衡的影响系数数据库的建立,提高现场动不平衡识别精度。分析表明,高效动平衡能有效降低轴系振动,并成功应用于转子热弯曲、动静碰磨等多种故障预防中,提高汽轮发电机组的安全可靠性。同时,若加重量较大时,应采用高密度平衡块,能降低不平衡块的分散度,可进一步提高动平衡的效果。     

多电机驱动系统的保性能鲁棒Funnel控制

本文针对含参数不确定性的多电机驱动系统,提出一种基于最优保性能鲁棒的Funnel控制方法实现系统的规定跟踪性能.该控制方法通过构造Funnel函数对误差系统进行变换,并设计自适应反步控制器保证变换后系统的稳定性即可使跟踪误差的瞬态和稳态响应均被限制在给定的Funnel边界内.然而由于系统中存在的参数不确定性会影响系统的规定控制性能,本文在Funnel控制基础上又设计了最优保性能鲁棒控制器.它是通过将参数不确定性系统的保性能鲁棒控制问题转化为标称系统的最优控制问题,并求解新的黎卡提方程而得到的.因此所设计的控制器不但消除了参数不确定性对系统的影响并且能够使系统的性能指标达到一确定的上界.最后,对四电机驱动系统进行了仿真和实验验证,说明所提出控制方法的有效性.     

基于ANSYS的转子系统不平衡响应分析

通过对某离心机转子系统进行受力分析,得到可以计算的数学模型.利用有限元分析软件AN-SYS建立其有限元模型,获取不同工况下的不平衡响应曲线、峰值和共振频率.通过分析可知在目前使用工况下工作转速低于一阶临界转速,转子主轴属于刚性转子.离心机不平衡响应大小受转速、激振力、每组筒体配重差等因素影响,动平衡研究主要是解决粉体压...     

An extended probabilistic method for reliability analysis under mixed aleatory and epistemic uncertainties with flexible intervals

The reliability analysis approach based on combined probability and evidence theory is studied in this paper to address the reliability analysis problem involving both aleatory uncertainties and epistemic uncertainties with flexible intervals (the interval bounds are either fixed or variable as functions of other independent variables). In the standard mathematical formulation of reliability analysis under mixed uncertainties with combined probability and evidence theory, the key is to calculate the failure probability of the upper and lower limits of the system response function as the epistemic uncertainties vary in each focal element. Based on measure theory, in this paper it is proved that the aforementioned upper and lower limits of the system response function are measurable under certain circumstances (the system response function is continuous and the flexible interval bounds satisfy certain conditions), which accordingly can be treated as random variables. Thus the reliability analysis of the system response under mixed uncertainties can be directly treated as probability calculation problems and solved by existing well-developed and efficient probabilistic methods. In this paper the popular probabilistic reliability analysis method FORM (First Order Reliability Method) is taken as an example to illustrate how to extend it to solve the reliability analysis problem in the mixed uncertainty situation. The efficacy of the proposed method is demonstrated with two numerical examples and one practical satellite conceptual design problem.     

参数摄动机械臂鲁棒控制器的演化设

对于存在参数区间摄动的机械臂，提出了一种利用遗传算法进行鲁棒控制器演化设计的方法．将具有给定结构的控制器的参数编码后作为控制器种群，将机械臂的摄动参数编码后作为受控对象种群；对两个种群进行双向演化操作，得到对区间摄动系统具有足够鲁棒性的控制器和最差控制性能所对应的受控对象模型．对存在参数摄动的二自由度机械臂进行鲁棒控制器设计的结果表明，所提出的方法是有效的．     

Propagation of uncertainties in deterministic systems

This paper addresses the propagation of uncertainties in deterministic systems, i.e. the system definition is known but the system parameters and input to the system contain uncertain information. The effect of the uncertain information on the system response is to be assessed. Three models of uncertainties corresponding to differing degrees of knowledge about the uncertainty are considered: interval, fuzzy and random. A method to propagate uncertainties expressed as intervals is described; the method, called the Vertex method, is based on a generalization of combinatorial interval analysis techniques. It is shown how the Vertex method can be extended naturally to treat the propagation of uncertainties modeled as fuzzy sets. Finally, propagation of random uncertainties is described using the classical probabilistic technique of derived distribution functions. The computational implications of the three models of uncertainties and the corresponding methods of propagation are contrasted. It is suggested that when the available information is too crude to support a random definition, the interval or fuzzy model should be used to take advantage of the expediency with which interval and fuzzy uncertainties can be propagated and processed.     

Uncertain Method for Optimal Control Problems With Uncertainties Using Chebyshev Inclusion Functions

In this paper, a new uncertain analysis method is developed for optimal control problems, including interval variables (uncertainties) based on truncated Chebyshev polynomials. The interval arithmetic in this research is employed for analyzing the uncertainties in optimal control problems comprising uncertain‐but‐bounded parameters with only lower and upper bounds of uncertain parameters. In this research, the Chebyshev method is utilized because it generates sharper bounds for meaningful solutions of interval functions, rather than the Taylor inclusion function, which is efficient in handling the overestimation derived from the wrapping effect due to interval computations. For utilizing the proposed interval method on the optimal control problems with uncertainties, the Lagrange multiplier method is first applied to achieve the necessary conditions and then, by using some algebraic manipulations, they are converted into the ordinary differential equation. Afterwards, the Chebyshev inclusion method is employed to achieve the solution of the system. The final results of the Chebyshev inclusion method are compared with the interval Taylor method. The results show that the proposed Chebyshev inclusion function based method better handle the wrapping effect than the interval Taylor method.     

Evolutionary algorithms for optimization problems with uncertainties and hybrid indices

Many optimization problems in real-world applications contain both explicit (quantitative) and implicit (qualitative) indices that usually contain uncertain information. How to effectively incorporate uncertain information in evolutionary algorithms is one of the most important topics in information science. In this paper, we study optimization problems with both interval parameters in explicit indices and interval uncertainties in implicit indices. To incorporate uncertainty in evolutionary algorithms, we construct a mathematical uncertain model of the optimization problem considering the uncertainties of interval objectives; and then we transform the model into a precise one by employing the method of interval analysis; finally, we develop an effective and novel evolutionary optimization algorithm to solve the converted problem by combining traditional genetic algorithms and interactive genetic algorithms. The proposed algorithm consists of clustering of a large population according to the distribution of the individuals and estimation of the implicit indices of an individual based on the similarity among individuals. In our experiments, we apply the proposed algorithm to an interior layout problem, a typical optimization problem with both interval parameters in the explicit index and interval uncertainty in the implicit index. Our experimental results confirm the feasibility and efficiency of the proposed algorithm.     

转子动力学研究进展

本文简要回顾了转子动力学的发展历程,指出了转子动力学的研究对象,如以汽轮发电机、燃气轮机、离心/轴流压缩机和航空发动机等大型装备为代表的复杂转子系统;主要研究内容涉及转子系统动力学建模、临界转速和振动响应计算、柔性转子动平衡技术、支承转子的各类轴承动力学特性、转子系统动力稳定性、转子系统非线性动力学、转子系统振动故障及其诊断技术、转子系统振动控制和多场耦合激励下转子系统振动,如机电耦合振动等.未来的研究主要聚焦在转静子系统耦合振动,基于大数据的转子系统智能诊断和考虑新材料、新结构的转子系统振动控制技术等方面.     

Topology optimization of structure for dynamic properties considering hybrid uncertain parameters

In the design and manufacturing of mechanical components, the dynamic properties of continuum structure are one of the most significant performances. At the same time, the uncertainty is widespread in these dynamic problems. This paper presents a robust topology optimization methodology of structure for dynamic properties with consideration of hybrid uncertain parameters. The imprecise probability uncertainties including materials, geometry and boundary condition are treated as an interval random model, in which the probability distribution parameters of random variables are modeled as the interval variables instead of given precise values. Two dynamic properties, including dynamic-compliance and eigenvalue, are chosen as the objective function. In addition, different excitation frequency or eigenvalue is discussed. In this work, the bi-directional evolutionary structural optimization (BESO) method is adopted to find the optimal robust layout of the structure. A series of numerical examples is presented to illustrate the optimization procedure, and the effectiveness of the proposed method is demonstrated clearly.     

Dynamic neural network method-based improved PSO and BR algorithms for transient probabilistic analysis of flexible mechanism

To improve the computing efficiency and precision of transient probabilistic analysis of flexible mechanism, dynamic neural network method (DNNM)-based improved particle swarm optimization (PSO)/Bayesian regularization (BR) (called as PSO/BR-DNNM) is proposed based on the developed DNNM with the integration of extremum response surface method (ERSM) and artificial neural network (ANN). The mathematical model of DNNM is established based on ANN on the foundation of investigating ERSM. Aiming at the high nonlinearity and strong coupling characteristics of limit state function of flexible mechanism, accurate weights and thresholds of PSO/BR-DNNM function are discussed by searching initial weights and thresholds based on the improved PSO and training final weights and thresholds by the BR-based training performance function. The probabilistic analysis of two-link flexible robot manipulator (TFRM) was investigated with the proposed method. Reliability degree, distribution characteristics and major factors (section sizes of link-2) of TFRM are obtained, which provides a useful reference for a more effective TFRM design. Through the comparison of three methods (Monte Carlo method, DNNM, PSO/BR-DNNM), it is demonstrated that PSO/BR-DNNM reshapes the probability of flexible mechanism probabilistic analysis and improves the computing efficiency while keeping acceptable computational precision. Moreover, the proposed method offers a useful insight for reliability-based design optimization of flexible mechanism and thereby also enriches the theory and method of mechanical reliability design.