应用于高速转子系统的金属橡胶阻尼环动力特性分析

针对金属橡胶构件的非线性特性及结构不同时弹性阻尼力学性能不同的特点, 利用实验和理论相结合的方法建立了金属橡胶构件迟滞回线边界变形过程力学模型, 推导了不同相对密度金属橡胶构件非线性迟滞恢复力表达式. 分析了简谐激励载荷作用下金属橡胶阻尼环动力学特性, 对该阻尼环进行了实验研究, 研究了存在预变形情况下阻尼环的弹性阻尼性能, 实验和理论分析结果基本一致, 为该种阻尼环的实际应用奠定了理论基础. 关键词： 金属橡胶构件 力学模型 非线性迟滞恢复力     

一种光电载荷非线性隔振装置的研究

针对光电载荷对隔振性能的需求,提出一种采用菱形连杆机构作为负刚度组件,具有高静、低动刚度特点的非线性隔振器（简称菱形HSLDS隔振器）。采用静力学分析方法,建立了隔振器数学模型,研究了刚度参数设定以及非线性调节方法;利用谐波平衡法（HBM）求解动力学方程,分析了各参数对隔振性能的影响关系;采用动力学仿真软件ADAMS及实物样机对理论模型与结论进行了验证。测试结果表明:菱形HSLDS隔振器具有较方便的参数调整能力,零位刚度及刚度非线性可通过拉簧参数与连杆参数进行设定、优化,隔振的刚度非线性优化程度受主隔振器阻尼以及零位刚度参数影响。相比于传统线性隔振器,菱形HSLDS具有显著的非线性隔振优势,可较好地满足光电载荷隔振需求。     

金属橡胶迟滞特性本构模型研究

金属橡胶作为一种新型阻尼材料, 其原材料及加工工艺具有一定特殊性. 基于材料的细观结构特征, 选取了金属丝螺旋卷作为金属橡胶的基本微元体结构, 并以圆柱压缩螺旋弹簧理论为基础, 分别建立了横向和纵向排列微元体结构的刚度. 鉴于库仑摩擦模型, 分别建立三种接触状态螺旋卷接触对的力学模型. 考虑整个加工工艺流程的特点, 分析了不同接触状态数目变化规律, 建立了金属橡胶迟滞特性本构模型. 从理论上解释了金属橡胶迟滞特性的特点, 以及刚度和阻尼非线性的产生机理. 最后, 通过对比不同相对密度金属橡胶试件的理论和试验结果, 验证了理论模型的适用性. 本模型从螺旋卷微元体结构上描述了金属橡胶迟滞特性, 为工程上预测和分析金属橡胶的刚度、阻尼特性和设计金属橡胶产品提供了有效的理论基础.     

金属橡胶隔振器随机振动加速度响应分析

通过对随机激励下干摩擦隔振系统理论的深入研究,推导了金属橡胶隔振系统随机激励均方加速度响应求解公式.对金属橡胶隔振器进行随机振动试验,分析了金属橡胶隔振器相对密度、预变形及所加试验量级对金属橡胶隔振系统均方根加速度响应的影响,得到了相关变化规律曲线.同时还分析了均方根加速度响应理论值与试验值的对比误差,确定了公式的适用范围. 关键词： 金属橡胶隔振器 随机振动 加速度响应     

金属橡胶热物理性能理论与试验研究

针对金属橡胶材料在高温环境下的热稳定性能和热传导性能, 基于金属橡胶的内部基本组分以及特殊的编织制作工艺, 构建了两种典型排列微元体结构, 并以此描述微元体在三种接触状态下的热膨胀特性, 提出了金属橡胶热膨胀Schapery分析模型, 从理论上解释了金属橡胶热膨胀的产生机理.根据金属橡胶基本组成单元的传热模式, 研究了金属橡胶的传热过程.利用热电比拟法和有限元法, 分析微元体的导热性能, 结合微元体的分布形式, 提出了金属橡胶的导热分析模型.试验测试了不同相对密度金属橡胶的热膨胀和热传导性能, 验证了金属橡胶热膨胀和导热特性分析理论模型的适用性.所得到的金属橡胶的热膨胀和热传导性能理论模型和试验结果, 为金属橡胶材料在高温环境下的热物理特性分析提供了理论基础和计算分析依据. 关键词： 金属橡胶 热膨胀 热传导 金属丝螺旋卷     

菱形HSLDS隔振器负刚度机构质量及摩擦力影响分析

以菱形负刚度机构HSLDS（high static low dynamic stiffness）隔振器（简称菱形HSLDS隔振器）为研究目标,采用虚功法建立负刚度机构等效摩擦力模型,并以拉格朗日方法建立包含负刚度机构质量及摩擦力因素的动力学方程;利用谐波平衡法（HBM）求解动力学方程,分析了负刚度机构质量及摩擦力对隔振的影响及其优化措施,并通过实物样机验证了理论模型的合理性。实验结果表明:负刚度机构质量及摩擦力对隔振均产生不利影响,应尽量减小;将负刚度机构连杆较短侧连接于载荷平台端,可以减小负刚度机构质量对较高频段隔振性能的影响;在限定隔振器刚度参数以及铰接副接触参数且同时满足刚度与摩擦力优化条件下,通过增大连杆机构杆长差的方式可以优化低频段隔振性能,并降低负刚度机构摩擦力对高频段隔振的影响。     

钢丝绳隔振器隔振性能试验

钢丝绳隔振器最早由美国于20世纪70年代末研制成功，它是一种典型的非线性隔振器，阻尼特性与变形有关，具有良好的隔振性能。文中以特定电子器件的隔振设计为出发点，对比研究了采用钢丝绳隔振器时的隔振效果，为最终设计定型积累了试验数据。     

正弦激励仪表板支架系统谐波响应分析

基于工程实践中出现的大量谐波振动的特点,总结认为非线性谐波响应的现象是结构弱非线性作用的结果。根据仪表板支架系统、隔振器的振动试验结果,将仪表板支架系统简化为硬刚度弱非线性Duffing方程,根据系统参数变化,     

机械弹性储能机组储能过程非线性动力学模型与混沌特性

提出了一种新的基于机械弹性的储能方法, 推导了永磁电机式机械弹性储能机组的非线性动力学模型, 论证了机组储能过程中某些参数及运行条件下会出现混沌运动, 分析了机组非线性动力学模型的线性稳定性. 在此基础上, 基于本实验室正在研发的0.16 kWh/0.8 kW机械弹性储能机组运行参数, 将机组3维非线性模型降阶为带时变参数的2维投影子系统, 采用坐标平面投影法研究了机组的混沌特性, 分析了降维子系统平衡点位置及特征方程随时变参数的变化关系, 并对机组混沌运动进行了数值验证, 在一定程度上解决了解析方法, 适用性较差导致对多维机电耦联系统只能仿真求解难以理论分析的问题.     

基于实验模态的某结构动力学模型修正

目前，动力学建模主要有3种方法：理论建模、实验建模、理论与实验综合建模。理论建模的主要方法是有限单元法，它所建立的有限元模型往往不能完全真实反映结构实际情况，计算结果与实验结果可能不一致。实验建模主要是基于实验模态分析方法。它可以识别结构动力学模型，能够用来修正有限元模型，使其模态参数与实验结果一致或基本一致。第3种方法也被广泛采用，通过大型有限元分析软件建立结构的有限元模型，计算理论模态参数，再结合实验识别出的模态参数对有限元模型进行动力学修正。     

Efficient model order reduction for dynamic systems with local nonlinearities

In the nonlinear structural analysis, the nonlinear effects are commonly localized and the rest of the structure behaves in a linear manner. Considering this fact, this research work proposes a harmonic balance solution in order to determine the nonlinear response of the structures. The solution is simplified by using an exact dynamic reduction along with the modal expansion technique. This novel approach, which is applicable to both discrete and continuous systems, converts the system equations of motion in each harmonic to a small set of nonlinear algebraic equations. The full set of system equations is reduced to a discrete system with a few generalized degrees of freedom (DOFs) confined to the localized nonlinear regions. The resultant reduced order model is shown to be accurate enough for determining the periodic response. To demonstrate the capability of the proposed method, numerical case studies for continuous and discrete systems, including systems with internal resonance, have been studied and the outcomes are validated with benchmark studies. In addition, the method is applied in the identification process of an experimental test setup with unknown frictional support parameters, and the results are presented and discussed.     

Nonlinear model and system identification of a capacitive dual-backplate MEMS microphone

This paper presents the nonlinear identification of a capacitive dual-backplate microelectromechanical systems (MEMS) microphone. First, a nonlinear lumped element model of the coupled electromechanical microphone dynamics is developed. Nonlinear finite element analyses are performed to verify the accuracy of the lumped linear and cubic stiffnesses of the diaphragm. In order to experimentally extract the system parameters, an approximate solution using the second-order multiple scales method is synthesized for a nonlinear microphone model, subject to an electrical step input. A nonlinear least-squares technique is then implemented to extract system parameters from laser vibrometry data of the diaphragm motion. The results indicate that the theoretical fundamental resonant frequency, damping ratio and nonlinear stiffness parameter agree with the corresponding extracted experimental parameters with 95% confidence interval estimates.     

Nonlinear finite element model updating of an infilled frame based on identified time-varying modal parameters during an earthquake

A model updating methodology is proposed for calibration of nonlinear finite element (FE) models simulating the behavior of real-world complex civil structures subjected to seismic excitations. In the proposed methodology, parameters of hysteretic material models assigned to elements (or substructures) of a nonlinear FE model are updated by minimizing an objective function. The objective function used in this study is the misfit between the experimentally identified time-varying modal parameters of the structure and those of the FE model at selected time instances along the response time history. The time-varying modal parameters are estimated using the deterministic–stochastic subspace identification method which is an input–output system identification approach. The performance of the proposed updating method is evaluated through numerical and experimental applications on a large-scale three-story reinforced concrete frame with masonry infills. The test structure was subjected to seismic base excitations of increasing amplitude at a large outdoor shake-table. A nonlinear FE model of the test structure has been calibrated to match the time-varying modal parameters of the test structure identified from measured data during a seismic base excitation. The accuracy of the proposed nonlinear FE model updating procedure is quantified in numerical and experimental applications using different error metrics. The calibrated models predict the exact simulated response very accurately in the numerical application, while the updated models match the measured response reasonably well in the experimental application.     

Dynamic modeling and identification of a slider-crank mechanism

In this paper, Hamilton's principle, Lagrange multiplier, geometric constraints and partitioning method are employed to derive the dynamic equations of a slider-crank mechanism driven by a servomotor. The formulation is expressed by only one independent variable and considers the effects of mass, external force and motor electric inputs. Comparing the dynamic responses between the experimental results and numerical simulations, the dynamic modeling gives a wonderful interpretation of a slider-crank mechanism. The parameters of many industrial machines are difficult to obtain if these machines cannot be taken apart. In this paper, a new identification method based on the real-coded genetic algorithm (RGA) is presented to identify the parameters of a slider-crank mechanism. The method promotes the calculation efficiency very much, and is calculated by the real-code without the operations of encoding and decoding. The results of numerical simulations and the experiments prove that the identification method is feasible. Finally, the experimental results by the RGA and the recursive least squares (RLS) are also compared.     

Variational Bayesian identification and prediction of stochastic nonlinear dynamic causal models

In this paper, we describe a general variational Bayesian approach for approximate inference on nonlinear stochastic dynamic models. This scheme extends established approximate inference on hidden-states to cover: (i) nonlinear evolution and observation functions, (ii) unknown parameters and (precision) hyperparameters and (iii) model comparison and prediction under uncertainty. Model identification or inversion entails the estimation of the marginal likelihood or evidence of a model. This difficult integration problem can be finessed by optimising a free-energy bound on the evidence using results from variational calculus. This yields a deterministic update scheme that optimises an approximation to the posterior density on the unknown model variables. We derive such a variational Bayesian scheme in the context of nonlinear stochastic dynamic hierarchical models, for both model identification and time-series prediction. The computational complexity of the scheme is comparable to that of an extended Kalman filter, which is critical when inverting high dimensional models or long time-series. Using Monte-Carlo simulations, we assess the estimation efficiency of this variational Bayesian approach using three stochastic variants of chaotic dynamic systems. We also demonstrate the model comparison capabilities of the method, its self-consistency and its predictive power.     

Nonlinear structural identification by the “Reverse Path” spectral method

When dealing with nonlinear dynamical systems, it is important to have efficient, accurate and reliable tools for estimating both the linear and nonlinear system parameters from measured data. An approach for nonlinear system identification widely studied in recent years is “Reverse Path”. This method is based on broad-band excitation and treats the nonlinear terms as feedback forces acting on an underlying linear system. Parameter estimation is performed in the frequency domain using conventional multiple-input–multiple-output or multiple-input–single-output techniques. This paper presents a generalized approach to apply the method of “Reverse Path” on continuous mechanical systems with multiple nonlinearities. The method requires few spectral calculations and is therefore suitable for use in iterative processes to locate and estimate structural nonlinearities. The proposed method is demonstrated in both simulations and experiments on continuous nonlinear mechanical structures. The results show that the method is effective on both simulated as well as experimental data.     

立方非线性Schr?dinger方程的动力学性质研究及其解模式的漂移

采用辛算法数值求解一维立方非线性Schr?dinger方程，研究了随着非线性参数的变化立方非线性Schr?dinger方程的动力学性质和解的模式的漂移.数值结果表明，随着非线性参数的增加解模式的漂移速度越来越快. 关键词： 动力学性质 相轨线 解模式的漂移 辛算法     

基于指数加权-核在线序列极限学习机的混沌系统动态重构研究

利用指数加权在线核序列极限学习机(exponential weighted online sequential extreme learning machine with kernel, EW-KOSELM)辨识算法,开展了针对混沌动力学系统的动态重构研究. EW-KOSELM算法将核递归最小二乘(kernel recursive least squares, KRLS)算法直接延伸至在线ELM (extreme learning machine)框架中,通过引入遗忘因子削弱了旧数据的影响,并基于"固定预算(fixed-budget, FB)"内存技术,应对在线核学习算法所固有的规模不断增长的计算困难.将所提辨识算法应用于Duffing-Ueda振子的混沌动力学系统数值仿真实例中,对基于FB-EW-KOSELM的辨识模型与原系统的动态性能进行了定性与定量的分析校验,定性校验准则是基于对比辨识模型与原系统吸引子(轨迹嵌入)、庞加莱映射、分岔图、极限环完成的,定量校验准则包括对比辨识模型与原系统的李雅普诺夫指数与关联维.进一步将其分别应用于来自测量蔡氏电路产生双涡卷吸引子与螺旋吸引子的实测数据实验及某一实际混沌电路所产生的时间序列中,对于具有低信噪比的实测电压或电流数据还需进行了小波降噪预处理.通过分析辨识模型重构吸引子,实验结果表明,FB-EW-KOSELM算法具有良好的动态重构性能,能精确地再生出展示混沌动态行为的过程非线性模型,且具有与原混沌系统非常接近的动态不变性指标.     

On the nonlinear primary resonances of a piezoelectric laminated micro system under electrostatic control voltage

In this article, a comprehensive nonlinear analysis for a piezoelectric laminated micro system around its static deflection is presented. This static deflection is created by an electrostatic DC control voltage through an electrode plate. The micro system beam is assumed as an elastic Euler-Bernoulli beam with clamped-free end conditions. The dynamic equations of this model have been derived by using the Hamilton method and considering the nonlinear inertia, curvature, piezoelectric and electrostatic terms. The static and dynamic solutions have been achieved by using the Galerkin method and the multiple-scales perturbation approach, respectively. The results are compared with numerical and other existing experimental results. By studying the primary resonance excitation, the effects of different parameters such as geometry, material and excitations voltage on the system?s softening and hardening behaviors are evaluated. In a piezoelectrically actuated micro system it was showed that because of existence of curvature and inertia nonlinear terms a small change in excitation amplitude can lead to the formation and expansion of nonlinear response. In this paper, it is demonstrated that by applying an electrostatic DC control voltage, these nonlinearities can be controlled and altered to a linear domain. This model can be used to design a nano or micro-scale smart device.     

Identification and updating of loading in frameworks using dynamic measurements

This paper is concerned with the effect of structural loading on dynamic performance. This topic is recognised as being of importance when validating finite element (FE) models with experimental data. A strategy for including axial load effects in a model updating procedure is developed. The method can be used to identify loading in structural frameworks using measured dynamic data.The effectiveness of the new method is demonstrated by means of case studies involving both simulated and experimental data. The theoretical study allows aspects of the sensitivity of the method to realistic levels of experimental noise to be studied as well as the way in which dynamic load identification can be enhanced with static measurements. The experimental case study proves the practical success of the technique. Updated axial load parameters are compared with static measurements of the same quantities.