结构随机动力非线性反应的整体灵敏度分析

以随机参数或参数子集对目标函数总方差的相对贡献作为该参数或参数子集的整体随机灵敏度度量,定义了一类整体随机灵敏度指标。结合结构非线性随机反应分析的密度演化方法,给出了计算整体随机灵敏度指标的基本算法。该灵敏度指标物理意义明确、计算实施方便。以滞回结构非线性反应分析为例,考察了以位移反应和滞回耗能为目标函数的不同随机参数(子集)的灵敏度指标。分析表明,不同随机参数(子集)对不同目标函数的灵敏度差异甚大,并且随机参数可能对结构反应的总方差产生负贡献。     

Global analysis and model validation in nonlinear system identification

The analysis of parameterised nonlinear models is considered. In particular the emphasis is placed on models produced as a result of applying nonlinear system identification techniques. A qualitative approach is taken, calling upon ideas from bifurcation theory. When combined with an existing numerical method this approach enables the analysis of a variety of nonlinear model forms to be carried out. The combination of qualitative and numerical aspects provides a flexible framework for analysis and in addition adds a global perspective that is difficult to achieve using analytical methods alone. When combined with the well tried methods of system identification, the approach allows the validation of nonlinear models from a qualitative viewpoint to be carried out. The method contrasts well with the statistical model validation techniques traditionally used. The technique is applied to a number of examples.     

Global sensitivity analysis of asymmetric energy harvesters

Nonlinear Dynamics - Parametric variability is inevitable in actual energy harvesters. It can significantly affect crucial aspects of the system performance, especially in harvesting systems that...     

Parameter identification of nonlinear fractional-order systems by enhanced response sensitivity approach

Nonlinear Dynamics - The fractional-order derivative is a powerful and promising concept to describe many physical phenomena due to its heredity/memory feature. This paper aims to establish a...     

结构优化半解析灵敏度分析的改进算法

提出了结构半解析灵敏度分析的改进算法,该算法实现简便,对于设计变量摄动步长具有极佳的数值稳定特性。首先,从总体角度推导静力问题半解析法灵敏度分析新算法,提出了位移与应力灵敏度列式,并给出了算法实施途径;然后,将此思路推广于自振频率、屈曲临界荷载和瞬态响应等多种问题,提出了相应的计算步骤。以梁单元与壳单元等典型结构为例,开展了多个算例测试。测试表明,改进算法计算精度和效率均有提升,特别是设计变量步长有更大的数值稳定区域,为复杂工程结构形状优化的灵敏度分析提供了新途径。     

Shape design sensitivity analysis for stability of elastic continuum structures

This paper addresses a method for shape design sensitivity analysis of a buckling load in a continuous elastic body. The sensitivity formula for critical load is analytically derived and expressed in terms of shape variation, based on the continuum formulation of the stability problem. Though the buckling problem is more efficiently solved by structural elements such as a beam and shell, elastic solids have been chosen for the buckling analysis in this paper because solid elements can generally be used for any kind of structure whether it is thick or thin. The initial stress and buckling analysis is carried out by the commercial analysis code ANSYS. Sensitivity is then computed by using the mathematical package MATLAB with the results of ANSYS. Several problems including straight and curved beams under compressive load, ring under pressure load, thin-walled section and bottle shaped column are chosen in order to illustrate the efficiency of the presented method.     

Analysis of parameter sensitivity and experimental design for a class of nonlinear partial differential equations

The purpose of this work is to analyse the parameter sensitivity problem for a class of nonlinear elliptic partial differential equations, and to show how numerical simulations can help to optimize experiments for the estimation of parameters in such equations. As a representative example we consider the Laplace–Young problem describing the free surface between two fluids in contact with the walls of a bounded domain, with the parameters being those associated with surface tension and contact. We investigate the sensitivity of the solution and associated functionals to the parameters, examining in particular under what conditions the solution is sensitive to parameter choice. From this, the important practical question of how to optimally design experiments is discussed; i.e. how to choose the shape of the domain and the type of measurements to be performed, such that a subsequent inversion of the measured data for the model parameters yields maximal accuracy in the parameters. We investigate this through numerical studies of the behaviour of the eigenvalues of the sensitivity matrix and their relation to experimental design. These studies show that the accuracy with which parameters can be identified from given measurements can be improved significantly by numerical experiments. Copyright © 2005 John Wiley & Sons, Ltd.     

形状设计灵敏度分析的改进的再生核质点法

基于物质导数概念和直接微分法,将再生核质点法应用于形状设计灵敏度分析（DSA）中。导出了基于无网格近似的灵敏度方程,特别强调了在考虑形状函数关于设计变量的物质导数时无网格方法与有限元法的不同。通过对RKPM形状函数及其物质导数进行矩式显式表述,提高了无网格方法的计算效率。对两个二维线弹性问题进行了位移灵敏度和应力灵敏度分析,计算结果与解析解吻合的很好;同时通过对通常的RKPM和改进的RKPM计算耗时的比较,显示了该方法不仅有效,而且可以显著地提高计算效率。     

A new semi-analytical technique for nonlinear systems based on response sensitivity analysis

In this paper, a new semi-analytical method, namely the time-domain minimum residual method, is proposed for the nonlinear problems. Unlike the existing approximate analytical method, this method does not depend on the small parameter and can converge to the exact analytical solutions quickly. The method is mainly threefold. Firstly, the approximate analytical solution of the nonlinear system \({\varvec{F\left( \ddot{x},{\dot{x}},x\right) }}={\varvec{0}}\) is expanded as the appropriate basis function and a set of unknown parameters, i.e., \({\varvec{x(t)}}\approx \sum _{i=0}^{N}{\varvec{a_i\chi _i(t)}}\). Then, the problem of solving analytical solutions is transformed into finding a set of parameters so that the residual \({\varvec{R}}={\varvec{F}}\left( \sum _{i=0}^{N}a_i\ddot{\chi }_i,\sum _{i=0}^{N}a_i{\dot{\chi }}_i,\sum _{i=0}^{N}a_i\chi _i\right) \) is minimum over a period, i.e., \(\underset{{\varvec{a}}\in {\mathscr {A}}}{\min }\int _{0}^T {\varvec{R}}({\varvec{a}},t)^{T} {\varvec{R}}({\varvec{a}},t) \mathrm {d} t\). The nonlinear equation \({\varvec{F\left( \ddot{x},{\dot{x}},x\right) }}={\varvec{0}}\) is regarded as the objective function to optimize, and the process of solving the analytic solution is transformed into a nonlinear optimization process. Finally, the optimization process is iteratively solved by the enhanced response sensitivity approach. Four numerical examples are employed to verify the feasibility and effectiveness of the proposed method.

     

Global sensitivity analysis based on high-dimensional sparse surrogate construction

Surrogate models are usually used to perform global sensitivity analysis(GSA) by avoiding a large ensemble of deterministic simulations of the Monte Carlo method to provide a reliable estimate of GSA indices. However, most surrogate models such as polynomial chaos(PC) expansions suffer from the curse of dimensionality due to the high-dimensional input space. Thus, sparse surrogate models have been proposed to alleviate the curse of dimensionality. In this paper, three techniques of sparse reconstruction are used to construct sparse PC expansions that are easily applicable to computing variance-based sensitivity indices(Sobol indices). These are orthogonal matching pursuit(OMP), spectral projected gradient for L_1 minimization(SPGL1), and Bayesian compressive sensing with Laplace priors. By computing Sobol indices for several benchmark response models including the Sobol function, the Morris function, and the Sod shock tube problem, effective implementations of high-dimensional sparse surrogate construction are exhibited for GSA.     

Nonlinear model standardization for the analysis and design of nonlinear systems with multiple equilibria

Nonlinear Dynamics - In engineering practice, a nonlinear system stable about several equilibria is often studied by linearizing the system over a small range of operation around each of these...     

Global solution for coupled nonlinear Klein-Gordon system

The globed solution for a coupled nonlinear Klein-Gordon system in two-dimensional space was studied. First, a sharp threshold of blowup and global existence for the system was obtained by constructing a type of cross-constrained variational problem and establishing so-called cross-invariant manifolds of the evolution flow. Then the result of how small the initial data for which the solution exists globally was proved by using the scaling argument.     

基于自适应交叉近似边界元法的快速声学灵敏度分析

基于自适应交叉近似边界元法构造一组快速声学灵敏度分析方法,其中,声学灵敏度分析分别采用直接微分法和伴随变量法;而自适应交叉近似算法被用以克服常规边界元法的高计算量和高存储量的固有缺点。自适应交叉近似算法在迭代求解之前对边界元系数矩阵进行压缩存储,可以在降低存储量的同时提高求解效率。在声学灵敏度分析中,通过直接使用求解未知边界状态值时保存的压缩系数矩阵,可以进一步提高求解效率。数值算例验证了所构造的方法的计算精度和求解效率,以及在大规模声场问题的最优化分析中的应用潜力。     

A nonlinear subspace-prediction error method for identification of nonlinear vibrating structures

The industrial structural systems always contain various kinds of nonlinear factors. Recently, a number of new approaches have been proposed to identify those nonlinear structures. One of the promising methods is the nonlinear subspace identification method (NSIM). The NSIM is derived from the principals of the stochastic subspace identification method (SSIM) and the internal feedback formulation. First, the nonlinearities in the system are regarded as internal feedback forces to its underlying linear dynamic system. The linear and nonlinear components of the identified system can be decoupled. Second, the SSIM is employed to identify the nonlinear coefficients and the frequency response functions of the underlying linear system. A typical SSIM always consists of two steps. The first step makes a projection of certain subspaces generated from the data to identify the extended observability matrix. The second one is to estimate the system matrices from the identified observability matrix. Since the calculated process of the NSIM is non-iterative and this method poses no additional problems on the part of parameterization, the NSIM becomes a promising approach to identify nonlinear structural systems. However, the result generated by the NSIM has its deficiency. One of the drawbacks is that the identified results calculated by the NSIM are not the optimal solutions which reduce the identified accuracy. In this study, a new time-domain subspace method, namely the nonlinear subspace-prediction error method (NSPEM), is proposed to improve the identified accuracy of nonlinear systems. In the improved version of the NSIM, the prediction error method (PEM) is used to reestimate those estimated coefficient matrices of the state-space model after the application of NSIM. With the help of the PEM, the identified results obtained by the NSPEM can truly become the optimal solution in the least square sense. Two numerical examples with local nonlinearities are provided to illustrate the effectiveness and accuracy of the proposed algorithm, showing advantages with respect to the NSIM in a noise environment.     

Optimum design and sensitivity analysis of piezoelectric truss structures

This paper is concerned with the optimum design and sensitivity analysis methods of piezoelectric intelligent trusses on the structural stiffness and free-vibration frequency. On the basis of the finite element method and taking into account the mechanical-electric coupling effect under electric load and mechanical load, the computational formula of sensitivities of structural displacement and free-vibration frequency with respect to the size and shape design variables are proposed. A new kind of design variable of the electric voltage and the calculating method of displacement sensitivity with respect to the electric voltage variable is presented. The new method for structural deformation control by optimizing the voltages of piezoelectric active bars is given. The numerical methods of optimum design and sensitivity analysis for piezoelectric truss structures are implemented in JIFEX software. Numerical examples demonstrate the effectiveness of the methods and the program. Project supported by the Special Funds for National Key Basic Research of China (No. G1999032805) and the Foundation for University Key Teachers by the Ministry of Education of China.     

磁驱动高速飞片实验样品设计

利用理论与数值模拟相结合的方法对磁驱动飞片实验样品的尺寸设计进行了分析探讨,给出了横 向尺寸设计的估算公式、长度设计原则、影响厚度设计的主要因素等。     

An inverse algorithm for the identification and the sensitivity analysis of the parameters governing micropolar elasto-plastic granular material

The article concerns the complex determination process of the material parameters governing micropolar granular material with elasto-plastic material properties. Proceeding from a gradient-based method, we split the total set of parameters and the overall identification procedure into two major categories. These are, firstly, the identification of the parameters of a standard non-polar elasto-plastic continuum, and, secondly, the determination of the remaining parameters governing the micropolar part of the constitutive model. While the first set of parameters can be obtained from homogeneous triaxial tests on, e. g., granular, cohesive-frictional materials like sand, the second set can only be determined from inhomogeneous tests, such as biaxial tests including the onset and the development of shear bands. Following this, one can obtain the first part of the identification process from a simple inverse algorithm applied to the elasto-plastic material model of non-polar solids, while the second part requires a fully inverse computation in the sense of a back analysis of the underlying boundary-value problem. In the present article, this procedure is carried out by use of the semi-discrete sensitivity analysis. Finally, the whole model is applied to the data of Hostun sand taken at the universities of Grenoble and Stuttgart. Dedicated to Professor Franz Ziegler on the occasion of his 70th birthday.     

Model determination for nonlinear state-based system identification

A complete methodology for robust nonlinear system identification is derived and illustrated through example. A proven state estimation algorithm is utilized in conjunction with a modified version of a stepwise regression approach to successfully determine the nonlinear dynamics in a “known” truth simulation without a priori knowledge of the system model. First, Minimum Model Error (MME) estimation is derived and illustrated through example. MME is a robust state estimation routine that provides, in addition to smooth states, an estimate of the unmodeled system dynamics is determined from noisy measurement data of known variance. Next, an Analysis of Variance (ANOVA) model correlation routine where a modified version of a forward stepwise procedure is derived and implemented. The ANOVA approach to model acceptance is well documented primarily in social science literature, but has been sparsely written about for engineering applications. This paper shows a significant improvement in nonlinear model identification when used in conjunction with MME estimation.     

Global bifurcation and chaos analysis in nonlinear vibration of spur gear systems

The global homoclinic bifurcation and transition to chaotic behavior of a nonlinear gear system are studied by means of Melnikov analytical analysis. It is also an effective approach to analyze homoclinic bifurcation and detect chaotic behavior. A generalized nonlinear time varying (NLTV) dynamic model of a spur gear pair is formulated, where the backlash, time varying stiffness, external excitation, and static transmission error are included. From Melnikov method, the threshold values of the control parameter for the occurrence of homoclinic bifurcation and onset of chaos are predicted. Additionally, the numerical bifurcation analysis and numerical simulation of the system including bifurcation diagrams, phase plane portraits, time histories, power spectras, and Poincare sections are used to confirm the analytical predictions and show the transition to chaos.     

Identification of material parameters for inelastic constitutive models: statistical analysis and design of experiments

In this paper we introduce stochastic methods to describe the influence of scattering test data on the identification of material parameters. We employ the viscoplastic constitutive model of Chan, Bodner, and Lindholm in its uniaxial form. The available test data result from three types of experiments performed at 600 °C on AINSI SS316 stainless steel, namely creep tests, constant strain rate tension tests with intermediate relaxation, and cyclic tension–compression tests. Each test has been performed with 12 specimens at different strain rates and stress rates respectively. However, for a serious statistical evaluation a larger number of experiments is required. In order to increase the number of tests we introduce stochastic simulations based on time series analysis which generate artificial data with the same stochastic behaviour as the experimental data. The method of stochastic simulation presents a widely accepted technique in engineering which does not add complexity to the process of parameter identification, but allows an investigation of the confidence in the fits of the material parameters. To keep the computation time for the identification of the material parameters as low as possible, very efficient numerical methods have to be implemented. The methods applied here for integration and nonlinear optimization are briefly introduced. The optimization strategy contains stochastic elements. Furthermore, we apply the method of statistical design of experiments to derive which combination of tests yields the most important information for an effective identification of material parameters.