饱和地基上弹性基础的竖向振动特性研究

采用解析的方法研究了饱和地基上受一简谐竖向荷载作用下弹性基础的动力响应．在分析中,首先利用积分变换技术获得了饱和介质基本控制方程的变换解,然后基于基础-半空间完全放松接触、半空间表面完全透水或不透水的假设,建立了该动力混合边值问题的对偶积分方程,并把该对偶积分方程进一步化为易于数值求解的第二类Fredholm积分方程A·D2文末数值算例给出了动力柔度系数、位移和孔隙水压力随振动频域和土-基础体系物理力学参数特性的变化曲线．结果表明:饱和地基上弹性基础的动力响应完全不同于饱和地基上刚性圆板的动力响应．所用方法可用于研究波的传播、土-结构动力相互作用等许多问题．     

环境自然激励下一种结构损伤在线识别方法

提出了一种环境自然激励下工程结构损伤识别方法,该法仅仅通过结构的输出数据识别结构的损伤．首先在结构完好的情况下,通过结构的输出数据识别外部统计参数;然后在结构损伤情况下,通过结构的输出数据识别结构的损伤分布函数．给出了识别结构损伤的理论模型和计算方法．数值仿真结果表明,该方法具有较高的辨别率和计算精度,能够使用在桥梁和建筑的健康监测中．     

最小二乘法在地基土工参数获取中的应用

本文利用最小二乘的L-M方法建立了关于地基土工参数的优化计算模型,并基于MATLAB得出计算结果．从而证明利用最小二乘法可有效地获取土工参数．     

结构振动特性在线识别的理论模型和计算方法

环境自然激励作用下的大型结构动力特性在线识别方法受到广泛的关注,这个方法仅仅利用结构自然响应的被测试数据,识别结构动力特性．Ibrahim方法和ARMAV方法是基本的识别方法．该文研究了受随机激励作用动力学模型,给出了有别于传统谐波恢复的子空间分解识别方法．数值仿真结果表明,该方法对结构振动特性的识别具有较好的鲁棒性和较高的计算精度．     

二系悬挂条件下的车-路垂向耦合系统的动力模型

在车辆的走行过程中,上部与下部是相互作用和影响的．因此,轨道交通问题实际上就是线路下部结构和车辆系统的体系匹配问题．在一系悬挂条件下的车-路系统耦合动力分析模型基础上,考虑了包含转向架在内的车辆的实际构成和轨下基础包括路基和地基的参振特性,利用轨道维护标准模拟行走不平顺激励,通过位移相容条件,从理论上研究车辆-轨道-路基体系的动力相互作用,建立了二系悬挂条件下的车辆-轨道-路基系统的垂向动力分析模型．为高速铁路路基的动力特性分析和设计提供参考．     

关于HBV及其药物相互作用的反应扩散方程的参数识别

研究具有反应-扩散现象的HBV及其药物相互作用系统的参数识别问题,依该系统正问题解的性质,建立了参数识别问题的数学模型,论证了系统正问题解关于待识别参数的连续依赖性与参数识别问题最优解的存在性.     

基于模型缩聚的结构损伤统计识别

提出了一种基于有限元模型缩聚技术的结构损伤统计识别方法,该方法仅需要少量传感器的测量数据.首先基于模型缩聚技术建立确定性的损伤识别过程,然后利用摄动法将概率过程融入确定性的损伤识别中,从而得到了一种基于概率统计的结构损伤识别方法.该方法通过计算未知参数(如损伤构件的弹性特征)对于测量噪声的一阶与二阶偏导数,来得到这些未知参数的均值与协方差矩阵.文中不仅阐述了该方法的理论推导过程,而且通过一个门式框架的数值仿真研究,并结合Monte Carlo数值模拟技术验证了该文方法的正确性.     

一类海冰-海水耦合热力学系统参数辨识问题的最优性

针对一维的海冰-海水耦合热力学系统,以该系统中的物理参数为辨识量,以温度偏差为目标函数,建立了一个参数辨识模型,并证明了该问题最优解的存在性,从而为这类海冰-海水耦合热力学系统参数辨识问题的数值计算提供数学理论依据.     

轨道结构随机场模型与车辆-轨道耦合随机动力分析

将轨道结构视为一个参数随机系统,提出并建立了轨道结构的随机场模型.利用车辆-轨道耦合动力学的基本方法,将轨道系统有限单元模型与多刚体车辆模型相结合,建立了考虑铁路线路参数空-时随机变化的车辆-轨道动力计算模型.算例表明:所提出的方法较为可靠且高效;线路参数随机性对车辆-轨道系统的动力响应有明显的影响,随线路参数离散程度的增加,可能造成行车不安全、轨道损伤加剧等一些问题.     

平台—群柱基础对浪流冲击的动力反应分析

本文研究平台-群柱基础系统对浪流冲击的动力反应分析.文中考虑了群柱基础与水流和地基土的动力相互作用,由解析法给出了系统对浪流力激励的动力响应分析,给出了算例的位移响应结果,并讨论了系统参数对动力特性的影响.     

An efficient boundary element method for two-dimensional transient wave propagation problems

The boundary element method (BEM) has been recognized by its unique feature of requiring neither internal cells nor their associated domain integrals in the computation. The method preserves its elegance for transient problems by means of a certain time-stepping scheme that initiates the time integration always from the initial time. Unfortunately, this time-marching scheme becomes rather difficult to apply, because the computation time and storage requirement grow dramatically with the increasing number of time steps. This paper shows that a reduction of one half of the computation time as well as the storage requirement can be achieved by an efficient truncation scheme for two-dimensional transient wave propagation problems. In particular, a guiding parameter for the determination of the truncation limit is proposed, and the overall measure of the error with respect to the truncation guide parameter is established.     

Evaluation of the adjoint sensitivity analysis for the identification of multibody system parameters

In the context of multibody simulation (MBS) system parameter identification within acceptable time can be challenging. One main difficulty is the huge amount of degrees of freedom in multibody systems and therefore the large number of dependent variables. The present work deals with the evaluation of different approaches that can be applied to typical parameter identification problems in MBS. A very powerful possibility is given by the adjoint sensitivity analysis that allows to reduce the computation effort dramatically. (© 2014 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim)     

一类弱耦合动力系统的参数识别问题

研究一类弱耦合反应－扩散动力系统的参数识别问题。通过构造上下解，证明了反应－扩散方程组解的存在惟一性；给出了求解参数识别问题的最优化系，从而可以选取适当的梯度法或者共轭梯度法，实现对系统参数的识别。     

估计海冰厚度函数的一种辨识方法

提出一种采用海冰和海水温度观测数据来估计海冰厚度的辨识方法, 避免了因使用厚度数据所带来的种种局限性. 首先建立一个拟线性海冰-海水热力学系统, 得到了系统解的存在唯一性; 然后以该系统中描述海冰厚度函数的参数为辨识量, 以系统输出的温度和实际观测温度的偏差为目标泛函, 建立了以目标泛函为最小的参数辨识模型; 最后构造了以半隐式差分格式、遗传算法和Hooke-Jeeves算法相结合的数值算法, 得到了海冰厚度函数, 并对辨识量做了敏感性分析. 结果表明: 这种方法是有效可行的.     

Dynamical level set method for parameter identification of nonlinear parabolic distributed parameter systems

This article considers a dynamical level set method for the identification problem of the nonlinear parabolic distributed parameter system, which is based on the solvability and stability of the direct PDE (partial differential equation) in Sobolev space. The dynamical level set algorithms have been developed for ill-posed problems in Hilbert space. This method can be regarded as a asymptotical regularization method as long as a certain stopping rule is satisfied. Hence, the convergence analysis of the method is established similar to the proof of convergence of asymptotical regularization. The level set converges to a solution as the artificial time evolves to infinity. Furthermore, the proposed level set method is proved to be stable by using Lyapunov stability theorem, which is constructed in my previous article.Numerical tests are discussed to demonstrate the efficacy of the dynamical level set method, which consequently confirm the level set method to be a powerful tool for the identification of the parameter.     

Parameter identification for steady-state groundwater flow

This paper presents a parameter identification method and describes its practical application to the estimation of groundwater flow transmissivity. The Newton-Raphson method is used. In determining the parameters, an important consideration is the way the unknown variables are selected. Methods making use of the same well for measuring and suction are particularly effective as regards computation. The distribution of measuring wells is investigated using actual data.     

一种面向效率的系统多序参量识别方法

针对社会组织系统演化过程中效率性要求,基于系统演化有效性识别及评估,提出能够反映系统演化过程效率的多序参量识别方法,为提升系统演化过程效率提供可操作的技术参考。通过产出/投入视角分析多序参量形成过程,结合其对系统演化过程的影响,给出基于产出/投入的系统演化过程中多序参量效率原理;应用改进的数据包络分析方法和已有指标体系,建立面向效率的系统演化过程多序参量识别模型。最后,通过算例验证方法的科学有效性。     

Data-driven recursive least squares methods for non-affined nonlinear discrete-time systems

Aiming at identifying nonlinear systems, one of the most challenging problems in system identification, a class of data-driven recursive least squares algorithms are presented in this work. First, a full form dynamic linearization based linear data model for nonlinear systems is derived. Consequently, a full form dynamic linearization-based data-driven recursive least squares identification method for estimating the unknown parameter of the obtained linear data model is proposed along with convergence analysis and prediction of the outputs subject to stochastic noises. Furthermore, a partial form dynamic linearization-based data-driven recursive least squares identification algorithm is also developed as a special case of the full form dynamic linearization based algorithm. The proposed two identification algorithms for the nonlinear nonaffine discrete-time systems are flexible in applications without relying on any explicit mechanism model information of the systems. Additionally, the number of the parameters in the obtained linear data model can be tuned flexibly to reduce computation complexity. The validity of the two identification algorithms is verified by rigorous theoretical analysis and simulation studies.     

Distributionally robust parameter identification of a time-delay dynamical system with stochastic measurements

In this paper, we consider a parameter identification problem involving a time-delay dynamical system, in which the measured data are stochastic variable. However, the probability distribution of this stochastic variable is not available and the only information we have is its first moment. This problem is formulated as a distributionally robust parameter identification problem governed by a time-delay dynamical system. Using duality theory of linear optimization in a probability space, the distributionally robust parameter identification problem, which is a bi-level optimization problem, is transformed into a single-level optimization problem with a semi-infinite constraint. By applying problem transformation and smoothing techniques, the semi-infinite constraint is approximated by a smooth constraint and the convergence of the smooth approximation method is established. Then, the gradients of the cost and constraint functions with respect to time-delay and parameters are derived. On this basis, a gradient-based optimization method for solving the transformed problem is developed. Finally, we present an example, arising in practical fermentation process, to illustrate the applicability of the proposed method.