基于有限元模型修正的损伤识别方法研究

通过实验方法测得结构的模态参数,建立结构系统的有限元基准(未损)模型,同时为了避免将初始的有限元模型误差误判为损伤,需对原始的基准模型进行有限元修正,建立基准解析模型.由于系统质量参数可以准确获得且在模型修正过程中保持质量不变,因此可将刚度参数作为修正对象,通过改变弹簧刚度,使其有限元动力分析的结果与实测结果尽量吻合.随后定义损伤变量(损伤引起刚度的变化率即灵敏度),借助刚性灵敏度实现对结构损伤位置与损伤程度的识别.     

直齿圆柱齿轮有限元模态模型修正

在对直齿圆柱齿轮结构动力学特性进行准确可靠的分析时,有限元模态模型的准确性至关重要。将初始有限元模型计算的固有频率和试验模态测量的固有频率进行对比,以ANSYS优化设计模块为基础,将有限元模态模型修正问题转化为求解直齿轮固有频率计算值与试验值的相对误差绝对值和的最小值问题。计算结果表明,有限元模态模型修正后前6阶固有频率最大相对误差由2.99%降为1.24%,显著提高了直齿轮有限元模态模型的精度且为有限元模态模型提供了相应的阻尼特性,保证了进一步的动力学特性预测分析的准确性。     

步兵战车车体结构有限元模型修正

针对某型步兵战车整车刚柔耦合发射动力学中柔性车体有限元模型精度低的问题,基于模态试验数据,应用支持向量机响应面模型修正理论对车体结构有限元模型进行了修正。应用ANSYS有限元分析软件对车体结构进行模态分析,提取前6阶模态的固有频率和振型。为验证模型,设计了模态试验方案,实测了车体结构的模态信息。基于有限元模型数据与实测数据的相对误差,采用支持向量机响应面模型修正方法对车体结构弹性模量和密度进行修正。模型确认结果和动力学模型应用结果表明,修正后的车体有限元模型精度有了大幅度提高,能更加真实地反映车体的结构特征,为射击精度分析提供了准确的模型基础。     

基于模态综合技术的结构有限元模型修正

由于结构的动力分析需要大量的计算时间和占用大量的计算机内存,常规的数值迭代计算方法难以实现,提出了基于模态综合技术的模型修正方法。该方法首先得到缩减后结构模型的频率与振型,并将该振型转换为缩减前模型物理坐标下的振型。然后,用缩减后模型的频率和转换后的振型,共同构成模型修正的优化目标函数,进而通过优化求解实现结构的模型修正。该方法既保证了计算精度又提高了模型修正的计算效率,使大型复杂结构的模型修正成为可能。最后,对某吊杆拱桥模型进行了动态测试和模型修正,验证了该算法的有效性。     

行波管模态分析与有限元模型修正

行波管的工作环境恶劣,其结构的振动特性对寿命及可靠性有着重要影响。应用有限元分析软件对行波管进行模态分析,并通过模态试验实测了行波管的模态参数,得到行波管前四阶固有频率和振型。对比分析仿真和试验结果,仿真计算的模态频率偏高。应用Kriging模型构建固有频率与材料参数的关系,将试验结果作为目标进行材料参数修正。修正后的仿真模型的计算精度有了大幅度提高,行波管结构的固有振动特性得到更加真实地反映,为进一步研究其动力学特性提供了更加准确的模型基础。     

基于结构参数不确定性的有限元模型修正

介绍了传统有限元模型修正及修正精度的评估方法,并简要说明了传统方法的不足之处。根据有限元模型修正的实际情况,提出了基于不确定性的有限元模型修正技术以及采用干涉面积法来对其修正精度进行评估。最后通过一个模型的数值仿真研究了基于不确定性的模型修正方法。     

基于试验模态的摆线齿轮有限元模型修正

精确的高质量有限元模型是进行结构动力学仿真的关键。基于Pro/E软件建立摆线齿轮参数化模型,通过初始有限元模态仿真和试验模态的结果对比,分析差异性原因,利用Hyperworks软件的参数优化功能,建立一个以固有频率的相对误差为修正目标,以结构的弹性模量、密度和泊松比等材料属性为修正参数的动力学优化问题。修正结果表明,摆线齿轮前六阶固有频率的最大相对误差由4.11%降为2.28%,有限元模型精度得到大幅提高,更加真实地反映结构特征,为进一步结构动态响应预测以及动态设计提供准确的模型基础。     

使用应变模态和遗传算法的有限元模型修正方法

提出了采用应变模态置信度为待修正响应特征的有限元模型修正方法。应变模态置信度是评价有限元仿真与试验测试结果相关性的方法,可以为模型修正提供全局的频率误差信息和局部的应变相关性信息。首先,介绍了应变模态和有限元模型修正的相关理论方法;然后,以某航空加筋壁板结构为对象,通过仿真分析和"仿真试验"获得结构的应变模态频率以及对应的应变振型,进一步计算频率误差和应变模态置信度误差;最后,基于两种误差构造模型修正的目标函数,采用遗传算法对目标函数进行优化,修正结构中的待修正参数,并将修正后参数代入模型,验证所提方法的正确性和有效性。结果表明:所采用的方法获得的修正后有限元模型具有复现修正响应特征的能力,并且对于未修正频段内的响应也具有较好的预测能力。     

基于灵敏度分析的刀形天线有限元模型修正

准确可靠的有限元模型是结构动态特性分析、设计改进的基础,文中利用模态试验得到的模态参数对某刀形天线进行有限元模型修正.首先建立天线结构的参数化模型,然后通过灵敏度分析选择合适的设计参数作为后续优化对象,利用计算与试验的模态频率之间的相对误差构造加权的优化目标函数,最后应用1阶优化方法修正结构的有限元模型.修正后有限元模型的模态频率最大相对误差降低至10%以内,模态置信度(MAC)均大于0.8.该修正模型可用于后续的动力学分析.     

使用有效模态质量和遗传算法的有限元模型修正

提出将模态频率和有效模态质量构造的残差作为遗传算法的目标函数进行结构动力学有限元模型修正的方法。有效模态质量不但可以为结构动力学响应分析提供一种判断模态贡献程度的方法,而且能够为有限元模型修正提供更多的信息量。介绍了有效模态质量的概念和基于遗传算法的结构动力学模型修正理论,在此基础上采用仿真算例验证了所提出方法的正确性和有效性。仿真结果显示,模型修正后参数最大误差为-0.062%,不管是在修正频段内还是修正频段外,频率和有效模态质量的均方误差都小于0.025%。研究表明,使用有效模态质量和遗传算法的结构动力学有限元模型修正是有效可行的。     

New iterative method for model updating based on model reduction

Model reduction technique is usually employed in model updating process. Here, a new iterative method associating the model updating method with the model reduction technique is investigated. Using the traditional iterative method, the errors resulted from replacing the reduction matrix of the experimental model with that of the finite element (FE) model are not fully considered, which needs more iterations and computing time. In order to reduce the errors produced in the replacement, a new iterative method is proposed based on the traditional method, in which the correction term related to the errors is added. The comparisons between the traditional iterative method and the proposed iterative method are shown by model updating examples of solar panels and both of these two iterative methods combine the cross-model cross-mode (CMCM) method and the succession-level approximate reduction (SAR) technique. The results indicate that the convergence rate and the computing time of the new method are significantly superior to those of the traditional iterative method with or without noise.     

Model selection in finite element model updating using the Bayesian evidence statistic

This paper considers the problem of finite element model (FEM) updating in the context of model selection. The FEM updating problem arises from the need to update the initial FE model that does not match the measured real system outputs. This inverse system identification-problem is made even more complex by the uncertainties in modeling some of the structural parameters. Such uncertainty often results in a number of competing forms of FE models being proposed which leads to lack of consensus in the field. A model can be formulated in a number of ways; by the number, the location and the form of the updating parameters. We propose the use of a Bayesian evidence statistic to help decide on the best model from any given set of models. This statistic uses the recently developed stochastic nested sampling algorithm whose by-product is the posterior samples of the updated model parameters. Two examples of real structures are each modeled by a number of competing finite element models. The individual model evidences are compared using the Bayes factor, which is the ratio of evidences. Jeffrey's scale is then used to determine the significance of the model differences obtained through the Bayes factor.     

Data fusion of acceleration and angular velocity for improved model updating

In the traditional finite element (FE) model updating, translational responses, such as acceleration, have generally been employed to identify the structural properties. However, the boundary conditions of a structure are associated with both translational and rotational DOFs. Thus, the combinational measurement of translational and rotational responses (e.g., angular velocity) would increase accuracy of FE model updating of structures, especially in identifying their boundary conditions. This paper proposes data fusion of translational and rotational responses for improved system identification using FE model updating technique. In the proposed method, the accelerometers and gyroscopes are installed in between and near the supports of a structure, respectively, and FE model updating is carried out using the natural frequencies, the translational mode shapes obtained from accelerations, and the rotational mode shapes obtained from angular velocities. Numerical and experimental verifications are carried out on simply-supported beam structures. The verifications show that the proposed FE model updating strategy based on the data fusion results in more accurate assessment of both structural properties and boundary conditions than the traditional FE model updating using translational responses only.     

Statistical updating of finite element model with Lamb wave sensing data for damage detection problems

Health monitoring of large structures with embedded, distributed sensor systems is gaining importance. This study proposes a new probabilistic model updating method in order to improve the damage prediction capability of a finite element analysis (FEA) model with experimental observations from a Lamb-wave sensing system. The approach statistically calibrates unknown parameters of the FEA model and estimates a bias-correcting function to achieve a good match between the model predictions and sensor observations. An experimental validation study is presented in which a set of controlled damages are generated on a composite panel. Time-series signals are collected with the damage condition using a Lamb-wave sensing system and a one dimensional FEA model of the panel is constructed to quantify the damages. The damage indices from both the experiments and the computational model are used to calibrate assumed parameters of the FEA model and to estimate a bias-correction function. The updated model is used to predict the size (extent) and location of damage. It is shown that the proposed model updating approach achieves a prediction accuracy that is superior to a purely statistical approach or a deterministic model calibration approach.     

一种夹层板结构车厢的有限元简化模型及模态分析

以某型车的车厢夹层复合板结构为例,研究了夹层板结构的有限元建模方法,建立了车厢的有限元简化模型并进行了模态分析。与传统上使用的板梁单元相比,单元自由度数减少了66.7%,大大缩短了分析时间,提高了计算效率。     

有限元建模中的几何清理问题

在有限元建模中，直接将较复杂的实体模型进行有限元造型将会遇到很多困难，必须先对模型进行几何清理，对实体进行系统、有效的几何清理，可以快速、高质地实现有限元网格的划分。文章基于HyperMesh的有限元造型，进行了几何清理问题的分析，得到了几何清理的原则、应注意的事项和几何清理过程中亟待解决的问题。     

The sensitivity method in finite element model updating: A tutorial

The sensitivity method is probably the most successful of the many approaches to the problem of updating finite element models of engineering structures based on vibration test data. It has been applied successfully to large-scale industrial problems and proprietary codes are available based on the techniques explained in simple terms in this article. A basic introduction to the most important procedures of computational model updating is provided, including tutorial examples to reinforce the reader’s understanding and a large scale model updating example of a helicopter airframe.     

有限元建模中离散单元问题研究

在离散实体模型时,通过合理的选择单元类型,恰当的使用特殊单元,能快速、高效地进行有限元造型,以提高求解精度、准确性及加快收敛速度,同时提高后置处理的可信度。     

An improved updating parameter selection method and finite element model update using multiobjective optimisation technique

Finite element model updating is a procedure to minimise the differences between analytical and experimental results and is usually posed as an optimisation problem. In model updating process, one requires not only satisfactory correlations between analytical and experimental results, but also maintaining physical significance of updated parameters. For this purpose, setting up of an objective function and selecting updating parameters are crucial steps in model updating. These require considerable physical insight and usually trial-and-error approaches are common to use. In conventional model updating procedures, an objective function is set as the weighted sum of the differences between analytical and experimental results. But the selection of the weighting factors is not clear since the relative importance among them is not obvious but specific for each problem. In this work, multiobjective optimisation technique is introduced to extremise several objective terms simultaneously. Also the success of finite element model updating depends heavily on the selection of updating parameters. In order to avoid an ill-conditioned numerical problem, the number of updating parameters should be kept as small as possible. Such parameters should be selected with the aim of correcting modelling errors and modal properties of interest should be sensitive to them. When the selected parameters are inadequate, then the updated model becomes unsatisfactory or unrealistic. An improved method to guide the parameter selection is suggested.     

Perturbation methods for the estimation of parameter variability in stochastic model updating

The problem of model updating in the presence of test-structure variability is addressed. Model updating equations are developed using the sensitivity method and presented in a stochastic form with terms that each consist of a deterministic part and a random variable. Two perturbation methods are then developed for the estimation of the first and second statistical moments of randomised updating parameters from measured variability in modal responses (e.g. natural frequencies and mode shapes). A particular aspect of the stochastic model updating problem is the requirement for large amounts of computing time, which may be reduced by making various assumptions and simplifications. It is shown that when the correlation between the updating parameters and the measurements is omitted, then the requirement to calculate the second-order sensitivities is no longer necessary, yet there is no significant deterioration in the estimated parameter distributions. Numerical simulations and a physical experiment are used to illustrate the stochastic model updating procedure.