具有刚性约束的非线性动力系统的局部映射方法

对具有刚性约束的n维非线性动力系统进行研究,建立了该类系统在刚性约束附近的局部映射关系.又根据连续性和横截性条件,通过几何方法推导并证明了局部映射的Jacobi矩阵的解析式.然后,通过引入局部映射,并利用Poincar啨映射方法,基于Floquet理论对刚性约束的n维非线性动力系统的周期运动的稳定性和分岔进行分析,给出了该类系统Poincar啨映射的Jacobi矩阵的计算方法.最后,以一类刚性约束的非线性动力系统为例,揭示了局部映射在其动力学分析中的重要作用.     

周期系统中概周期解的数值计算

非线性系统的长期规则运动除了平衡点和周期解以外,概周期解,或有时表现为拟周期解也是一种长期规则运动。无论是在理论上,还是在实际计算中,确定概周期解这种规则运动远比前二者困难。本文用简单胞映射方法结合一定的技巧,数值计算周期非线性系统的概周期解。     

计及短周期误差的直齿轮副近周期运动及其辨识

齿轮副中的齿距偏差等短周期误差使系统出现复杂的周期运动, 影响齿轮传动的平稳性. 将该类复杂周期运动定义为近周期运动, 采用多时间尺度Poincaré映射截面对其进行辨识. 为研究齿轮副的近周期运动, 引入含齿距偏差的直齿轮副非线性动力学模型, 并计入齿侧间隙与时变重合度等参数. 采用变步长4阶Runge-Kutta法数值求解动力学方程, 由所提出的辨识方法分析不同参数影响下系统的近周期运动. 根据改进胞映射法计算系统的吸引域, 结合多初值分岔图、吸引域图与分岔树状图等研究了系统随扭矩与啮合频率变化的多稳态近周期运动. 研究结果表明, 齿轮副中的短周期误差导致系统的周期运动变复杂, 在微观时间尺度内, 系统的Poincaré映射点数呈现为点簇形式, 系统的点簇数与实际运动周期数为宏观时间尺度的Poincaré映射点数. 短周期误差导致系统在微观时间尺度内的吸引子数量增多, 使系统运动转迁过程变复杂. 合理的参数范围及初值范围可提高齿轮传动的平稳性. 该辨识与分析方法可为非线性系统中的近周期运动研究奠定理论基础.      

叶片-转子-轴承系统的非线性动力学问题研究

以非线性动力学和转子动力学理论为基础,研究了在油膜力作用下,叶片和转轴耦合振动系统的动力学行为。为分析叶片的惯性影响,将叶片模化为单摆模型。采用Runge-Kutta数值方法求解了耦合系统的振动方程,并利用分岔图、Lyapunov指数图、Poincar啨映射图和频谱图等分析了系统的稳定性。分析结果表明,当转速变化时,系统响应会出现倍周期、拟周期和混沌运动等现象,在此基础上分析了叶片长度的变化对该系统非线性动力学行为的影响。     

基于积分本构黏弹性带的横向非线性动力学

研究了黏弹性传动带在1:1内共振时的横向非平面非线性动力学特性. 首先,利用Hamilton原理建立了黏弹性传动带横向非平面非线性动力学方程. 然后综合应用多尺度法和Galerkin离散法对偏微分形式的动力学方程进行摄动分析,得到了四维平均方程. 对平均方程的稳定性进行了分析,从理论上讨论了动力系统解的稳定性变化情况. 最后数值模拟结果表明黏弹性传动带系统存在混沌运动、概周期运动和周期运动.      

系泊海洋平台周期运动倍周期分岔的胞映射分析

应用胞映射方法研究了系泊海洋生产平台的周期运动及其倍周期分岔。系泊运动的数学模型是一个具有指数回复力特性的非线性强迫振子 ,以波浪作用力为外激励。将波浪激励周期作为分岔控制参数 ,研究了周期系泊运动的倍周期分岔。胞映射方法用于寻找系统的稳定吸引子并确定其吸引域。时间历程、相图、功率谱和Poincar啨映射用于确定吸引子的具体类型芯糠⑾?,分岔参数处于不同的区域时 ,系统存在着相异的倍周期分岔特性。观察到了倍周期分岔的产生和突然消失 ,也找到了一个趋于吸引子的倍周期分岔序列。根据吸引域的胞映射分析结果解释了上述不同的倍周期分岔特征。发现其原因在于倍周期序列中的每个吸引子是否具有全局吸引性。     

迭代图胞映射方法的快速生成实现

在迭代图胞映射方法的框架下,基于摄动微分多项式的思想讨论了常微分方程的快速求解,将所得结果与迭代图胞映射方法有机结合,有效地解决了迭代图胞映射动力系统的快速生成问题,克服了微分方程动力系统生成迭代图胞映射系统过程中耗时较多、效率低下的不足,大大提高了计算效率。通过对典型非线性系统——杜芬方程的应用分析,证实了该方法的有...     

碰撞振动系统的一类余维二分岔及T2环面分岔

建立了三自由度碰撞振动系统的动力学模型及其周期运动的Poincaré映射,当Jacobi矩阵存在两对共轭复特征值同时在单位圆上时,通过中心流形-范式方法将六维映射转变为四维范式映射.理论分析了这种余维二分岔问题,给出了局部动力学行为的两参数开折.证明系统在一定的参数组合下,存在稳定的Hopf分岔和T2环面分岔.数值计算验证了理论结果.     

非线性动力系统全局分析的变胞胞映射法与转子／轴承系统的全局稳定性

在Ｐｏｉｎｃａｒｅ映射及胞映理论的基础上，提出了一种非线性动力系统全局分析的新方法－－变胞胞映射法，这种新方法改变了原胞映射法中胞在胞空间分布的不合理性及运算逻辑的不合理性，更适用于高维、大求解域非线性动力系统的求解。应用此方法，对具有非线性油膜力的Ｊｅｆｆｃｏｔ转子轴承系统进行了全局分析，绘制了系统分岔后的全局吸引域图，解释了一些工程中常见的非线性现象。     

胞映射中的拓扑度方法

本文给出了Ｎ维动力系统的数值拓扑方法。该方法是胞映射方法的辅助方法，可用于高维动力高维动力系统全局性质的分析，除了有理论价值外，还可用来寻找并确定强非线性系统的周期解，此外还给出了一种迭代格式，便各计算工便于计算机实现。     

Application of Cell Mapping methods to a discontinuous dynamic system

The Cell Mapping method is a robust tool for investigating nonlinear dynamic systems. It is capable of finding the attractors and corresponding basins of attraction of a system under investigation. To investigate the applicability of the Cell Mapping method to discontinuous systems, a forced zero-stiffness impact oscillator is chosen as an application. The numerical integration algorithm, the basic element in the Cell Mapping method, is adjusted to overcome the discontinuity. Four types of Cell Mapping techniques are applied: Simple Cell Mapping, Generalized Cell Mapping, Interpolated Cell Mapping, and Mixed Cell Mapping. The last type is a new modification to existing types. Each type of Cell Mapping is briefly explained. The results are compared to the exact solutions. The Interpolated Cell Mapping and Mixed Cell Mapping methods are found to produce the most accurate results for this case.     

Poincare型胞映射分析方法及其应用

本文用Poincare型胞映射方法对平衡及不平．衡轴承转子非线性动力系统的全局特性进行了分析研究,同时求得了一定状态空间内系统存在的周期解及其在各不同Poincare截面上的吸引域,得到了一些新的现象和规律,并通过对平衡及不平衡轴承转子系统的全局特性异同的比较,说明了要建立既适用于平衡轴承转子系统又适用于不平衡轴承转子系统的非线性稳定性准则应注意的几个问题     

高维非线性振动系统参数识别

将增量谐波平衡非线性识别推广到高维振动系统, 推导了基于增量谐波平衡的多自由度非线性系统的识别方程. 针对一个两自由度系统进行了数值模拟计算, 讨论了系统在单周期、倍周期和混沌运动状态下的参数识别, 以及噪声对识别结果的影响, 验证了增量谐波平衡非线性识别在多自由度系统中的有效性. 结果表明, 该方法具有较高的计算效率和识别精度, 以及良好的抗噪能力.      

非线性系统全局动态特性分析的PCM法及其在转子轴承系统中的应用

本文将Ｐｏｉｎｃａｒｅ映射的思想与胞映射法相结合，提出了可用于高维非线性动力系统全局稳定性分析的新型数值方法：ＰＣＭ（Ｐｏｉｎｃａｒｅ－Ｃｅｌｌ－Ｍａｐｐｉｎｇ）法，和胞映射法相比，新方法在实用上具有明显的优点。为说明ＰＣＭ法的有效性，本文应用此方法对平衡转子轴承非线性动力系统进行了全局稳定性分析，同时给出了一确定状态空间中存在的所有周期解及其吸引域。     

Study of differential control method for solving chaotic solutions of nonlinear dynamic system

In this paper, we focus on the need to solve chaotic solutions of high-dimensional nonlinear dynamic systems of which the analytic solution is difficult to obtain. For this purpose, a Differential Control Method (DCM) is proposed based on the Mechanized Mathematics-Wu Elimination Method (WEM). By sampling, the computer time of the differential operator of the nonlinear differential equation can be substituted by the differential quotient of solving the variable time of the sample. The main emphasis of DCM is placed on substituting the differential quotient of a small neighborhood of the sample time of the computer system for the differential operator of the equations studied. The approximate analytical chaotic solutions of the nonlinear differential equations governing the high-dimensional dynamic system can be obtained by the method proposed. In order to increase the computational efficiency of the method proposed, a thermodynamics modeling method is used to decouple the variable and reduce the dimension of the system studied. The validity of the method proposed for obtaining approximate analytical chaotic solutions of the nonlinear differential equations is illustrated by the example of a turbo-generator system. This work is applied to solving a type of nonlinear system of which the dynamic behaviors can be described by nonlinear differential equations.     

Bifurcation analysis for nonlinear multi-degree-of-freedom rotor system with liquid-film lubricated bearings

The oil-film oscillation in a large rotating machinery is a complex high-dimensional nonlinear problem. In this paper, a high pressure rotor of an aero engine with a pair of liquid-film lubricated bearings is modeled as a twenty-two-degree-of-freedom nonlinear system by the Lagrange method. This high-dimensional nonlinear system can be reduced to a two-degree-of-freedom system preserving the oil-film oscillation property by introducing the modified proper orthogonal decomposition (POD) method. The efficiency of the method is shown by numerical simulations for both the original and reduced systems. The Chen-Longford (C-L) method is introduced to get the dynamical behaviors of the reduced system that reflect the natural property of the oil-film oscillation.     

A highly-efficient method for stationary response of multi-degree-of-freedom nonlinear stochastic systems

Analytical and numerical studies of multi-degree-of-freedom(MDOF) nonlinear stochastic or deterministic dynamic systems have long been a technical challenge.This paper presents a highly-efficient method for determining the stationary probability density functions(PDFs) of MDOF nonlinear systems subjected to both additive and multiplicative Gaussian white noises. The proposed method takes advantages of the sufficient conditions of the reduced Fokker-Planck-Kolmogorov(FPK) equation when constructing the trial solution. The assumed solution consists of the analytically constructed trial solutions satisfying the sufficient conditions and an exponential polynomial of the state variables, and delivers a high accuracy of the solution because the analytically constructed trial solutions capture the main characteristics of the nonlinear system. We also make use of the concept from the data-science and propose a symbolic integration over a hypercube to replace the numerical integrations in a higher-dimensional space, which has been regarded as the insurmountable difficulty in the classical method of weighted residuals or stochastic averaging for high-dimensional dynamic systems. Three illustrative examples of MDOF nonlinear systems are analyzed in detail. The accuracy of the numerical results is validated by comparison with the Monte Carlo simulation(MCS) or the available exact solution. Furthermore, we also show the substantial gain in the computational efficiency of the proposed method compared with the MCS.     

Control of an Adaptive Refinement Technique of Generalized Cell Mapping by System Dynamics

Generalized cell mapping is an efficient and powerful numerical tool for the prediction of the long-term behavior and global analysis of nonlinear dynamic systems. The only drawback of this method is the enormous computational effort it requires for high-dimensional systems. We overcome this problem by adaptively refining a very rough starting cell grid, where the adaptation is controlled by the long-term dynamics of the system. We illustrate the efficiency of our approach by examples.