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Kriging模型和小波包能量谱的随机模型修正

针对随机模型修正精度和效率低的问题,提出一种基于Kriging模型和小波包能量谱的随机有限元模型修正方法。首先,假设模型待修正参数和响应特征均服从正态分布,将不确定性的模型修正转化为均值和标准差的修正;其次,将待修正参数作为Kriging模型输入,加速度频响函数经过小波包分解后提取的结点能量作为输出,引入政治优化算法优化相关系数以构造Kriging模型;然后,将最小化试验响应与预测响应之差的绝对值作为修正均值的目标函数,最小化交叉熵作为修正标准差的目标函数,通过政治优化算法先后修正参数均值和标准差;最后,以空间桁架结构为例,选取弹性模量和密度为待修正参数验证该方法的可行性。结果表明,所提方法能够有效地修正结构参数均值和标准差,修正后的参数均值、标准差的误差分别低于0.1%、3.5%。     

信息融合与响应面联合的渡槽结构模型修正

针对大型渡槽损伤诊断研究中有限元模型精度问题,提出一种基于信息融合与响应面法(response surface method,简称RSM)的有限元模型修正方法,能够兼顾优化目标和参数优化两个环节。以某灌区渡槽为研究对象,建立初始有限元模型,采用试验设计方法构建待修正参数与特征频率的设计空间,经过参数筛选后建立设计样本的响应面模型;引入自适应噪声的完整集成经验模态分解和奇异值分解(complete ensemble empirical mode decomposition with adaptive noise-singular value decompose,简称CEEMDAN-SVD)降噪方法联合方差贡献率数据级融合算法,对正常运行工况下覆盖三跨槽身振测信号进行多通道融合处理以获取完整有效的实测频率,并以此为目标值对响应面模型进行最优化求解,实现多跨渡槽有限元模型的参数优化。结果表明,修正后的有限元模型计算出的频率与实测频率吻合较好,最大误差为-4.38%,有效解决了大型渡槽结构有限元模型的精度问题,为后续结构损伤诊断等仿真研究提供基准模型。     

基于运行模态分析的单梁起重机模型修正研究

为了进行模型的故障监测和诊断,需要一个更准确的单梁起重机模型来定位和量化故障。因此,有必要对已建立的有限元模型进行修正。在对传感器布置和激振位置进行分析的基础上,通过运行模态分析获得了单梁起重机的模态参数。在Ansys Workbench建立初始有限元模型后,基于灵敏度分析确定待修正参数,对待修正参数进行试验设计,建立二次多项式响应面。在响应面评价合格后,根据实际模态参数利用NSGA-Ⅱ算法对目标函数进行全局寻优,获得更加精确的初始条件。最后利用锤击法试验模态分析与修正后模型的谐响应分析对比,验证已修正模型的精度。     

考虑边界条件不确定性的塔机有限元模型修正

由于在大型塔机的模型修正中为简化计算,往往忽略了边界条件的不确定性,导致修正结果精度不够或修正计算收敛困难,为此提出一种考虑边界条件不确定性的塔机有限元模型修正方法。首先,以某型塔机为研究对象,进行整机风致振动实测,获取结构的实测动态特性;其次,建立考虑边界条件不确定性的塔机有限元模型,基于二次响应面法对该有限元模型进行修正;最后,将模型修正前后的计算结果与实测值进行对比。结果表明,考虑边界条件的影响能有效提高修正的精度,对结构的边界条件进行修正后的有限元模型能复现实测频段内的模态,也能以一定精度预测实测频段外的模态,证实了所提出的考虑边界条件不确定性的修正方法的有效性。     

最优阶次多项式响应面法及其在模型修正中的应用

提出一种构造最优阶次多项式响应面方法,通过多元回归分析参数对响应不同阶次下的拟合精度与显著度,建立判断多项式各参数最优拟合阶次的方法,解决了常规多项式响应面法中阶次固定且统一的限制,提升了代理模型的精度.将响应面法应用于有限元模型修正中,建立基于最优阶次响应面法有限元模型修正的理论框架,提高了修正的效率,并保证了修正后模型的精度.以功能函数为例,验证了最优阶次多项式响应面法可实现更高精度的模型替代.以GARTEUR飞机的实测模态修正初始有限元模型,阐述了响应面构建以及模型修正的过程,并有效修正了初始模型误差,减少了经典模型修正迭代中调用有限元软件计算灵敏度的时间,证明了最优阶次多项式响应面可成功应用于有限元模型修正中.     

基于响应面方法的多喷嘴引射器有限元模型修正

针对某多喷嘴引射器在脉动载荷作用下的动力学响应预测问题,对其有限元模型进行了修正,减少了材料属性、边界条件等对计算结果的影响。首先开展了引射器有限元模态分析,获得了初始模态分析频率;采用了多点激励多点响应锤击法进行了模态试验,获取了试验模态频率;基于有限元模型进行了误差分析,确定了材料密度、弹性模量、质量点质量等修正参数,通过中心复合设计方法确定了样本空间,构建了多目标响应面并对待修正参数进行了约束优化,得到了修正参数的最优解;最后,使用修正后的参数进行了有限元分析,获得了修正后的模态分析频率,并通过动力响应计算进行了模型确认。研究结果表明:修正后模态分析频率与模态试验频率(前三阶)误差均值由修正前的8.01%减小到2.81%,该修正方法能够显著提高有限元模态分析精度。     

基于加速度频响函数的改进响应面模型修正

为提高模型修正精度,将加速度频响函数引入到改进的响应面模型修正当中.首先分别采用模态参与度准则和有效独立法确定试验最佳激励点和测量点,然后根据待修正参数选取样本点,计算其对应的加速度频响函数,构造初始响应面模型;其次利用三倍方差准则,对预测值进行检验,剔除远离响应面的离群点;再优化初始响应面模型得到最优解作为新的样本点...     

基于高速列车转向架系统的数值模型修正

在结构动力学分析中,复杂机械结构动态特性的数值仿真具有重要的意义。研究了基于组合部件分析的高速列车转向架系统的数值模型修正,基于模型相关性分析和灵敏度分析的组件数值模型修正,验证了模型各个部件模型的可靠性和动力学响应的精确性。在有限元模型预修正的基础上,选择合适的待修正参数,进行后续的参数型有限元模型修正。     

逆响应面法用于磨机传动系统的模型修正

为了获得机械结构的精确模型,采用以逆响应面的有限元模型修正理论为基础的方法。逆响应面法直接拟合出设计参数（密度、弹性模量等）关于特征参数（固有频率等）的显式表达式,以特征参数目标值带入逆响应面函数得到修正后的设计参数值,相较于响应面法,无需迭代寻优,减少计算工作量。以齿轮轴和磨机传动轴为例,实现了以逆响应面法为基础的有限元模型修正,并计算修正前后模态频率误差,结果显示：逆响应面法模型修正效果较优于响应面法,进而验证了逆响应面法在机械结构动力学特性分析中的有效性。     

考虑结构参数不确定性的随机模型修正方法

提出了一种随机模型修正方法以确定结构不确定性参数的概率统计特性,使得模型修正的应用更符合工程实际。将随机模型修正过程分解为一组确定性修正过程,利用蒙特卡罗仿真得到的响应样本并结合响应面模型的快速运算特性,构造优化反演过程来求得各个样本所对应的一组参数值,进而基于大量样本统计得到参数的均值和方差。所提出方法经过一组试验钢板的验证,准确求得了钢板厚度和材料参数的均值和方差,说明了方法的可行性和可靠性。     

基于近似似然的频响函数不确定性模型修正

贝叶斯模型修正框架下,以频响函数作为目标,提出了一种使用近似似然函数的不确定性模型修正方法。相比于模态参数,频响函数包含了结构更加充分的信息,用于结构动力学模型修正时有诸多优点,但现有的不确定性模型修正方法并不能很好地实现将频响函数作为目标进行修正。针对此问题,介绍了频响函数和贝叶斯框架下的不确定性模型修正理论,基于近似贝叶斯计算提出了一种近似似然函数,可适用于频响函数作为目标进行不确定性修正。将提出的似然函数应用到三自由度数值和H型非对称梁的有限元模型修正算例中,并结合DREAM算法对不确定性参数进行识别。研究结果表明：修正后参数的上、下限与目标值相差无几,修正后模型的频响函数与目标值几乎重合,在一定噪声水平下仍具有较好的修正效果,验证了所提方法的有效性。     

Comparative study of damped FE model updating methods

Most of finite element (FE) model updating techniques do not employ damping matrices and hence, cannot be used for accurate prediction of complex frequency response functions (FRFs) and complex mode shapes. In this paper, a detailed comparison of two approaches of obtaining damped FE model updating methods are evaluated with the objective that the FRFs obtained from damped updated FE models is able to predict the measured FRFs accurately. In the first method, damped updating FE model is obtained by complex parameter-based updating procedure, which is a single-step procedure. In the second method, damped updated model is obtained by the FE model updating with damping identification, which is a two-step procedure. In the first step, mass and stiffness matrices are updated and in the second step, damping matrix is identified using updated mass and stiffness matrices, which are obtained in the previous step. The effectiveness of both methods is evaluated by numerical examples as well as by actual experimental data. Firstly, a study is performed using a numerical simulation based on fixed–fixed beam structure with non-proportional viscous damping model. The numerical study is followed by a case involving actual measured data for the case of F-shaped test structure. The updated results have shown that the complex parameter-based FE model updating procedure gives better matching of complex FRFs with the experimental data.     

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.     

Finite element model updating based on eigenvalue and strain energy residuals using multiobjective optimisation technique

The numerical results from a finite element (FE) model often differ from the experimental results of real structures. FE model updating is often required to identify and correct the uncertain parameters of FE model and is usually posed as an optimisation problem. Setting up of an objective function, selecting updating parameters and using robust optimisation algorithm are three crucial steps in FE model updating. In this paper, a multiobjective optimisation technique is used to extremise two objective functions simultaneously which overcomes the difficulty of weighing the individual objective function of more objectives in conventional FE model updating procedure. Eigenfrequency residual and modal strain energy residual are used as two objective functions of the multiobjective optimisation. Only few updating parameters are selected on the basis of the prior knowledge of the dynamic behaviours of the structure and eigenfrequency sensitivity study. The proposed FE model updating procedure is first applied to the simulated simply supported beam. This case study shows that the methodology is robust with an effective detection of assumed damaged elements. The procedure is then successfully applied to the updating of a precast continuous box girder bridge that was tested on field under operational conditions.     

建立斜拉桥基准有限元模型的新方法与实现

在进行斜拉桥监测与检测研究时,为了建立准确而可靠的基准有限元模型,使其索力、位移等趋于监测或检测结果,需进行模型修正工作,但各种模型修正方法多数需要进行迭代运算,不仅计算工作量巨大,而且有时难以实现预期目标。针对这一问题,提出了一种使用Ansys与Matlab软件,利用影响矩阵和优化算法相结合进行模型修正的新方法。该方法不需要迭代,可获得索力、位移等参数,且与实测值相吻合。通过实例对该方法进行了验证,证明了该方法可行并易于实现。     

RESULTS OBTAINED BY MINIMISING NATURAL FREQUENCY AND MAC-VALUE ERRORS OF A BEAM MODEL

This paper reports the procedure followed by the ‘LTAS-Vibrations et Identification des Structures’ research group to generate a low-order finite element (FE) model of the GARTEUR SM-AG19 structure. The model is made of beam elements, local inertia and rigid body elements. The philosophy of the updating method is first based on the correlation of the experimental data with the results of the FE model and on the localisation of errors in the model. Updating parameters are then selected using eigenvalue sensitivity and model error localisation analyses. After updating, the quality of the FE model is assessed in terms of accuracy of the response prediction to structural modifications.     

基于扫频响应反推黏弹性材料分数阶导数模型

提出了基于测试获得的扫频响应反推黏弹性材料分数阶导数模型参数的方法。在同时考虑黏弹性材料阻尼和剩余等效黏性阻尼的基础上,建立了基础激励作用下黏弹性复合板动力学方程,给出了求解振动响应的方法。提出利用基于灵敏度的匹配计算实现分数阶导数模型参数辨识的方法。以贴敷ZN-1型黏弹性材料的悬臂钛板为例,辨识得到了该黏弹性材料的分数阶导数模型。将结果代入到黏弹性阻尼板振动响应分析模型中,通过理论与实验的对比,证明了辨识参数的合理性。