形状记忆合金梁的建模及混沌阈值计算

从形状记忆合金（SMA）的等应变拉压实验数据出发,利用van-der-pol环模型模拟了形状记忆合金在加载和卸载过程中的应力应变迟滞环特性。并根据弹性理论和Galerkin方法建立了形状记忆合金简支梁在受轴向激励时的振动模型。随后得出了自由振动系统的分岔特性。在利用待定固有频率法研究了模型的非线性参数对系统固有频率的影响后,根据待定固有频率法的计算结果和时间尺度变化提出了系统Melnikov函数的改进表达式,提高了计算形状记忆合金梁模型在参数激励下发生混沌的阈值的精度。数值模拟的结果证明了该途径的有效性。     

基于中心流形理论的小水电并网系统Hopf分岔分析

针对小水电并网系统,用Matcont软件搜寻系统的Hopf分岔点绘制分岔图;利用中心流形理论将高维电力系统降到二维模型,并通过计算二维模型分岔稳定性指标的正负判定原系统Hopf分岔类型。结果表明,分岔稳定性指标大于零时电压失稳,小于零时电压稳定。用Matlab软件对讨论结果进行数值仿真,证明理论结果的正确性。     

矩形薄板在面内随机参数激励下的随机分岔研究

建立了四边简支的矩形薄板在受面内随机激励时的振动模型,并用Galerkin法将该系统化简为二自由度常微分非线性动力学方程组。得出系统的广义能量（Hamilton函数）表达式后,又利用拟不可积Hamilton系统平均理论将方程等价为一个一维的Ito随机扩散过程,并通过计算该系统的最大Lyapunov指数来研究系统的局部随机稳定性,同时利用基于随机扩散过程的奇异边界理论研究了模型的全局稳定性,最后通过稳态概率密度函数的形状变化探讨了系统参数变化对系统随机Hopf分岔的影响。数值模拟结果验证了理论分析的正确性。     

轨道随机激励对弹性约束轮对随机稳定性与随机分岔的影响

考虑轨道随机不平顺激励与结构自身随机参激建立了弹性约束轮对系统的伊藤随机微分方程组,运用拟不可积Hamilton系统理论和奇异边界性态求解了系统的随机局部稳定性和随机全局稳定性,通过分析稳态概率密度和联合概率密度得到了模型的随机Hopf分岔类型并讨论了分岔的条件。可知,分岔的发生不仅受到系统固有参数的影响同时也受随机因素的影响,即使满足一定的分岔条件分岔也并不一定会发生,系统分岔的发生是以概率特征来体现的,这和只能考虑系统固有参数下的确定性分岔有着明显差别;另外,不同随机强度下轮对系统有着不同的失稳临界速度,这和不能考虑随机因素作用下的确定性轮对系统只有一个确定的失稳临界速度有着本质区别。     

五自由度强非线性随机振动系统的首次穿越研究

利用基于广义谐和函数的随机平均法,建立了高斯白噪声激励下五自由度强非线性随机振动系统的Pon-tryagin方程及后向Kolmogorov方程。求解这两个高维偏微分方程,得到了系统的平均首次穿越时间、条件可靠性函数以及平均首次穿越时间的条件概率密度。用Monte Carlo数值模拟验证了理论方法的有效性。     

Furuta倒立摆的分岔与混沌动力学分析

利用Hopf定理和规范形理论,讨论了Furuta旋转倒立摆非线性数学模型的Hopf分岔特性。给出系统存在Hopf分岔的条件,讨论了周期轨道的稳定性,利用数值模拟,得到系统的相轨迹图,进一步验证分析过程的正确性。利用Silnikov定理,讨论了旋转倒立摆的混沌动力学特征。利用卡尔达诺公式和微分方程级数解讨论了该系统的特征值和同宿轨道的存在性,比较严格地证明了系统存在Smale马蹄意义下的混沌现象,并给出发生Silnikov型Smale混沌的条件。     

波轮式全自动洗衣机的摆动问题的分岔分析

为深入研究波轮式全自动洗衣机的摆动问题,讨论了其悬挂系统的受力情况并详细分析其切向阻尼力的构成形式;采用旋转坐标变换的方法将文献[5]中振动模型转为自治形式,分析自治系统的平衡点及其求解方法;采用数值分岔软件AUTO[6]完成了自治系统的分岔分析,指出脱水过程中的Hopf分岔现象,解释了摆动现象起源于自治系统解的不稳定性,进而讨论了Hopf点与脱水转速、脱水负载、悬挂系统轴向阻尼和悬挂弹簧刚度间的关系,并分析脱水过程中的稳定区与不稳定区;最后采用数值仿真的方法验证了分岔结论的正确性。     

矩形薄板在面内随机参数激励下的随机稳定性与分岔研究

本文根据小挠度薄板的弹性理论建立了矩形薄板的受面内随机激励的振动模型,并用Galerkin变分法将其化简为常微分非线性动力学方程。又利用拟不可积Hamilton平均理论将方程等价为一个一维的Ito随机微分方程,并通过计算系统的最大Lyapunov指数来研究系统的局部随机稳定性,同时利用奇异边界理论研究了模型的全局稳定性,最后通过稳态概率密度函数的形状研究了系统参数对发生的随机Hopf分岔现象的影响,发现随机Hopf分岔在两个关键值附近发生,数值模拟结果验证了理论分析的正确性。     

用Normal Form直接法研究壁板的热颤振与控制

建立了二维受热壁板在超音速气流中的颤振方程。运用分岔理论求得了系统的Hopf分岔点,应用Normal Form直接法计算得到系统Hopf分岔Normal Form系数。引入wash-out滤波器技术对壁板热颤振进行了主动控制,延迟系统Hopf分岔的产生而不改变分岔类型。最后采用数值模拟验证了理论分析。     

具有间隙非线性的二元机翼的随机分岔和功率谱分析

研究了在纵向和垂向随机激励联和作用下,在俯仰方向具有间隙非线性的二元机翼系统的随机颤振。主要由随机系统的二维概率密度和最大Lyapunov指数研究了随机分岔,包括P-分岔和D-分岔,还分析了系统的功率谱密度函数。得到结果如下：当气流流速分别位于颤振前区域和颤振后区域时,随着随机扰动强度的由弱变强,双维概率密度的形状都发生了变化,证明了气流流速在这两种区域都发生了P-分岔。而由系统的最大Lyapunov指数表明不论在弱或强的随机扰动下,也不论气流流速位于哪种区域内,而D-分岔都没有发生。还得到了不同气流流速时的功率谱密度函数曲线,证实了颤振发生时,系统的能量更集中在颤振频率上。     

Nonlinear Dynamic Characteristics and Optimal Control of Giant Magnetostrictive Laminated Plate Subjected to In-plane Stochastic Excitation

In this paper, nonlinear dynamic characteristics and optimal control of giant magnetostrictive laminated plate (GMLP) subjected to in-plane stochastic excitation were studied. Von del Pol nonlinear item was introduced to interpret the hysteresis phenomenon of the strain–magnetic field intensity curve of giant magnetostrictive material, and the nonlinear dynamic model of GMLP subjected to in-plane stochastic excitation was developed. Local and global stochastic stabilities were analyzed according to largest Lyapunov exponent theory and singular boundary theory. The functions of steady-state probability density and joint probability density were obtained, and the condition of stochastic Hopf bifurcation was analyzed. The reliability function was solved from backward Kolmogorov equation, and the probability density of the first-passage time was obtained. Finally, the optimal control strategy was proposed in stochastic dynamic programming method. Numerical simulation shows that the stability of the solution varies with parameter, and stochastic Hopf bifurcation appears in the process; the reliability of the system was improved by optimal control, and the first-passage time was delayed. The result is helpful to engineering applications of GMLP.     

A new approach in modelling groundwater pollution under uncertainty

This article presents a new methodology to model the time and space evolution of a contaminant in a system of aquifers when certain components of the model, such as the geohydrologic information, the boundary conditions, the magnitude and variability of the sources or physical parameters are uncertain and defined in stochastic terms. The method is based on applications of modern mathematics to the solution of the resulting stochastic transport equations. This procedure exhibits considerable advantages over the existing stochastic modelling techniques. In particular, the semigroup solutions are not restricted to small variances in the stochastic elements (perturbation techniques), unsteady dynamic conditions are specifically considered, time and space randomness may be considered in the sources, the boundary conditions or the parameters, and the methodology reflects a well-posed functional-analytic theory. Several basic example problems are presented in order to illustrate the application of the methodology to the modelling of complex spatially and temporally distributed sources of interest in engineering hydrology today. Further potential applications of the method are very promising.     

随机最优控制下矩形薄板受面内随机参数激励的首次穿越研究

根据建立了四边简支受控矩形薄板受面内高斯白噪声激励的振动模型,并用Galerkin变分法将其化简为二自由度常微分非线性动力学方程。又利用拟不可积Hamilton系统平均理论将方程等价为一个一维的Ito随机过程,随后结合随机动态规划方法,得到了使系统可靠性最大的随机最优控制策略。最后建立了受控系统的条件可靠性函数所满足的Backward Kolmogorov(BK)方程,根据初始条件和边界条件得出数值结果。数值结果表明,随机最优控制对系统的可靠性提升有明显作用     

Identification of stochastic loads applied to a non-linear dynamical system using an uncertain computational model and experimental responses

The paper is devoted to the identification of stochastic loads applied to a non-linear dynamical system for which experimental dynamical responses are available. The identification of the stochastic load is performed using a simplified computational non-linear dynamical model containing both model uncertainties and data uncertainties. Uncertainties are taken into account in the context of the probability theory. The stochastic load which has to be identified is modelled by a stationary non-Gaussian stochastic process for which the matrix-valued spectral density function is uncertain and is then modelled by a matrix-valued random function. The parameters to be identified are the mean value of the random matrix-valued spectral density function and its dispersion parameter. The identification problem is formulated as two optimization problems using the computational stochastic model and experimental responses. A validation of the theory proposed is presented in the context of tubes bundles in Pressurized Water Reactors.     

随机激励下高维包装振动系统的可靠性分析

研究了两自由度包装振动系统在受高斯白噪声外激励下的可靠性.利用由随机平均法导出的关于系统的独立运动积分的随机平均方程,通过系统本身的特性以及对漂移与扩散系数作出的一些假定,给出了系统的可靠性函数以及首通损坏的条件转移概率密度函数所满足的后向柯尔莫哥洛夫方程和福克-普朗克方程,并进一步详细地讨论了其边界与可解性条件.数值结果表明,关于系统的可靠性函数以及首通损坏的条件转移概率密度函数方面的结果是比较合理的,这也表明文中的分析方法是正确的.     

基于灰色周期外延的航线客流量区间序列预测模型

为解决无法获取先验分布模式的“贫信息、小样本”航线随机客流量预测问题,提取这类航线客流量时间序列的上、下界信息,并在中间增加一个偏好值,形成包含左界点、中间点和右界点的三元区间数结构的航线客流量表达形式,将三元区间数数据结构转换为左半径、中心及右半径3个独立的时间序列,再利用灰色系统理论建立航线客流量预测模型,并利用周期外延模型对上述模型得出的残差序列进行修正。采用2004—2019年民航客运量数据进行验证分析。结果发现,ARIMA(autoregressive integrated moving average model)模型预测检验的平均绝对百分比误差为6.77%,灰色周期外延模型的平均绝对百分比误差为1.66%,因此后者在短期预测上有较大优势。     

A non‐intrusive model reduction approach for polynomial chaos expansion using proper orthogonal decomposition

In this paper, a non‐intrusive stochastic model reduction scheme is developed for polynomial chaos representation using proper orthogonal decomposition. The main idea is to extract the optimal orthogonal basis via inexpensive calculations on a coarse mesh and then use them for the fine‐scale analysis. To validate the developed reduced‐order model, the method is implemented to: (1) the stochastic steady‐state heat diffusion in a square slab; (2) the incompressible, two‐dimensional laminar boundary‐layer over a flat plate with uncertainties in free‐stream velocity and physical properties; and (3) the highly nonlinear Ackley function with uncertain coefficients. For the heat diffusion problem, the thermal conductivity of the slab is assumed to be a stochastic field with known exponential covariance function and approximated via the Karhunen–Loève expansion. In all three test cases, the input random parameters are assumed to be uniformly distributed, and a polynomial chaos expansion is found using the regression method. The Sobol's quasi‐random sequence is used to generate the sample points. The numerical results of the three test cases show that the non‐intrusive model reduction scheme is able to produce satisfactory results for the statistical quantities of interest. It is found that the developed non‐intrusive model reduction scheme is computationally more efficient than the classical polynomial chaos expansion for uncertainty quantification of stochastic problems. The performance of the developed scheme becomes more apparent for the problems with larger stochastic dimensions and those requiring higher polynomial order for the stochastic discretization. Copyright © 2015 John Wiley & Sons, Ltd.     

Stochastic BEM with spectral approach in elastostatic and elastodynamic problems with geometrical uncertainty

The present paper proposes a method for stochastic problems that have uncertainty in the boundary geometry. The method is developed by applying the spectral stochastic approach to the boundary element method and is called the spectral stochastic boundary element method (SSBEM). In the SSBEM, the uncertainty in the boundary geometry is represented by the Karhunen–Loève expansion. It is shown that, by utilizing material derivative, variation of boundary element matrices associated with the geometrical fluctuation of the boundary can be approximated by the Taylor expansion. The solution is represented by a stochastic process expressed in the form of polynomial chaos expansion. The stochastic equation is then projected on a homogeneous chaos space. This procedure reduces a stochastic equation to an ordinary linear matrix equation that can be solved by conventional schemes. The SSBEM can estimate not only mean values and variances of the solutions but also their probability density functions. In order to examine the performance, the SSBEM is applied to two-dimensional elastostatic and elastodynamic problems with geometrical boundary uncertainty. Computation results of the SSBEM exhibit good agreement with those obtained by Monte Carlo simulation. The efficiency of the SSBEM is verified by comparison of their computation times.     

Stochastic second-order BEM perturbation formulation

This paper presents the stochastic second order moment perturbation approach to the classical deterministic Boundary Element Method (BEM) formulation. Numerous applications of such a formulation in different problems of stochastic mechanics, especially in the field of computational modeling of structural defects in homogeneous and composite materials occurring randomly in solids and engineering structures, were the main reasons to introduce the proposed model. The stochastic boundary element method (SBEM) formulation of the general linear elasticity boundary value has been provided together with an appropriate discretization. The equations describing the expected values and the covariances of stress and strain tensors for points lying on the boundary and inside the region are considered. This set of equations constitutes a formal mathematical statement of the problem and is suitable for computational implementation.