Robust updating of uncertain damping models in structural dynamics for low- and medium-frequency ranges

This paper deals with the robust updating of uncertain computational models in the context of structural dynamics in the low- and medium-frequency ranges of composite sandwich panels for which experimental results are available. The uncertain computational model is constructed using the non-parametric probabilistic approach which takes into account model and data uncertainties. The formulation of the robust updating problem includes the effects of uncertainties and consists in minimizing a cost function with respect to an admissible set of updating parameters. Updating is performed in two steps using several cost functions and experimental results. The results of the robust updating problem show that the method proposed is efficient for updating the uncertain computational model in both low- and medium-frequency ranges.     

Optimal selection of artificial boundary conditions for model update and damage detection

Sensitivity-based model error localization and damage detection is hindered by the relative differences in modal sensitivity magnitude among updating parameters. The method of artificial boundary conditions is shown to directly address this limitation, resulting in the increase of the number of updating parameters at which errors can be accurately localized. Using a single set of FRF data collected from a modal test, the artificial boundary conditions (ABC) method identifies experimentally the natural frequencies of a structure under test for a variety of different boundary conditions, without having to physically apply the boundary conditions, hence the term “artificial”. The parameter-specific optimal ABC sets applied to the finite element model will produce increased sensitivities in the updating parameter, yielding accurate error localization and damage detection solutions. A method is developed for identifying the parameter-specific optimal ABC sets for updating or damage detection, and is based on the QR decomposition with column pivoting. Updating solution residuals, such as magnitude error and false error location, are shown to be minimized when the updating parameter set is limited to those corresponding to the QR pivot columns. The existence of an optimal ABC set for a given updating parameter is shown to be dependent on the number of modes used, and hence the method developed provides a systematic determination of the minimum number of modes required for localization in a given updating parameter. These various concepts are demonstrated on a simple model with simulated test data.     

SYMMETRIC INVERSE EIGENVALUE VIBRATION PROBLEM AND ITS APPLICATION

This paper summarises the authors' previous effort on inverse eigenvalue problem for linear vibrating systems described by a vector differential equation with constant coefficient matrices and non-proportional damping. The inverse problem of interest here is that of determining real symmetric coefficient matrices assumed to represent mass normalised velocity and position coefficient matrices, given a set of specified complex eigenvalues and eigenvectors. There are given two solutions of a symmetric inverse eigenvalue problem presented by Starek and Inman [1, 2].The theory of inverse eigenvalue problem is applied to the model updating problem. The goal of this paper is to recognise that the model updating problem is a subset of the inverse eigenvalue problem. The approach proposed here is to use the results of inverse eigenvalue problem to develop methods for model updating.Comments are made on how their procedure may be used to solve the damage detection problem.     

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.     

Finite element model updating and structural damage identification using OMAX data

The main limitations in the finite element (FE) model updating technique lie in the ability of the FE model to represent the true behavior of the structure (modelling problem), and in the ability to identify enough modal parameters with sufficient accuracy, especially for large structures that are tested in operational conditions (identification problem). In this paper, the identification problem is solved with an OMAX approach, where an artificial force is used in operational conditions and a structural model is identified that takes both the forced and the ambient excitation into account. From an extensive case study on a real three-span bridge, it is observed that, while updating the FE model using the experimental output-only data yields a good fit, discrepancies show up when the more extensive set of OMAX data is used for validation, or even for updating. It can be concluded that an OMAX approach not only increases the well-posedness of the updating problem, it also allows to detect potential inaccuracies in the FE model.     

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.     

The role of anti-resonance frequencies from operational modal analysis in finite element model updating

Finite element model updating traditionally makes use of both resonance and modeshape information. The mode shape information can also be obtained from anti-resonance frequencies, as has been suggested by a number of researchers in recent years. Anti-resonance frequencies have the advantage over mode shapes that they can be much more accurately identified from measured frequency response functions. Moreover, anti-resonance frequencies can, in principle, be estimated from output-only measurements on operating machinery. The motivation behind this paper is to explore whether the availability of anti-resonances from such output-only techniques would add genuinely new information to the model updating process, which is not already available from using only resonance frequencies.This investigation employs two-degree-of-freedom models of a rigid beam supported on two springs. It includes an assessment of the contribution made to the overall anti-resonance sensitivity by the mode shape components, and also considers model updating through Monte Carlo simulations, experimental verification of the simulation results, and application to a practical mechanical system, in this case a petrol generator set.Analytical expressions are derived for the sensitivity of anti-resonance frequencies to updating parameters such as the ratio of spring stiffnesses, the position of the centre of gravity, and the beam's radius of gyration. These anti-resonance sensitivities are written in terms of natural frequency and mode shape sensitivities so their relative contributions can be assessed. It is found that the contribution made by the mode shape sensitivity varies considerably depending on the value of the parameters, contributing no new information for significant combinations of parameter values.The Monte Carlo simulations compare the performance of the update achieved when using information from: the resonances only; the resonances and either anti-resonance; and the resonances and both anti-resonances. It is found that the addition of anti-resonance information improves the updating performance for some combinations of parameter values, but does not improve the update in significant other regions.The simulated results are verified using resonance and anti-resonance frequencies measured on a steel beam test rig. The investigation is extended to include the updating of parameters of a petrol generator set. It is found that the contribution of the anti-resonances to the model update is heavily dependent on the geometry of the model and the choice of variables to be updated, suggesting that, for some models, the pursuit of anti-resonance information through expensive operational modal analysis may be inappropriate.     

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.     

三维线弹性连续体的结构拓扑优化设计

研究了基于水平集模型和SIMP方法的三维线弹性结构的拓扑优化方法，优化模型的目标是结构的柔度最小。引入水平集模型隐含描述具有复杂拓扑关系的三维线弹性结构的几何边界，以材料域的形状为变量进行灵敏度分析，构造水平集方程的速度函数，通过几何边界的演化获得结构的最优形状和拓扑。同时研究了基于SIMP方法的三维连续体结构的拓扑优化设计。对基于水平集模型和SIMP的拓扑优化方法的数值算例进行比较，结果表明，基于水平集的三维结构拓扑优化具有光滑的几何边界，数值算法稳定。     

On the use of damped updated FE model for dynamic design

Model updating techniques are used to update the finite element model of a structure, so that updated model predicts the dynamics of a structure more accurately. The application of such an updated model in dynamic design demands that it also predicts the effects of structural modifications with a reasonable accuracy. Most of the model updating techniques neglect damping and so these updated models cannot be used for predicting amplitudes of vibration at resonance and antiresonance frequencies. This paper deals with updating of the finite element model using the FRF data with damping identification using complex modal data and its subsequent use for predicting the effects of structure modifications. The updated model is obtained in two steps. In the first step, mass and stiffness matrices are updated using FRF-based model updating method. In the second step, damping is identified using updated mass and stiffness matrices, which are obtained in first step. Structural modifications in terms of mass and beam modifications are then introduced to evaluate the updated model for its usefulness in dynamic design.     

Interval model updating with irreducible uncertainty using the Kriging predictor

Interval model updating in the presence of irreducible uncertain measured data is defined and solutions are made available for two cases. In the first case, the parameter vertex solution is used but is found to be valid only for particular parameterisation of the finite element model and particular output data. In the second case, a general solution is considered, based on the use of a meta-model which acts as a surrogate for the full finite element mathematical model. Thus, a region of input data is mapped to a region of output data with parameters obtained by regression analysis. The Kriging predictor is chosen as the meta-model in this paper and is found to be capable of predicting the regions of input and output parameter variations with very good accuracy. The interval model updating approach is formulated based on the Kriging predictor and an iterative procedure is developed. The method is validated numerically using a three degree of freedom mass-spring system with both well-separated and close modes. A significant advantage of Kriging interpolation is that it enables the use of updating parameters that are difficult to use by conventional correction of the finite element model. An example of this is demonstrated in an experimental exercise where the positions of two beams in a frame structure are selected as updating parameters.     

考虑模型偏差的结构动力学模型修正

针对结构有限元模型修正后仍可能存在模型偏差的问题,提出用待修正参数的不确定性来表征模型偏差的有限元模型修正方法。首先,基于响应面方法识别得到待修正参数的最优值,并通过计算结果与试验结果比较获得模型偏差;然后,基于响应面模型并结合灵敏度分析计算得到模型偏差对待修正参数的影响,从而得到考虑模型偏差后待修正参数的区间;最后,通过一个悬臂梁工程实例的模型修正,验证了笔者所提出方法的可行性。结果表明,考虑模型偏差的修正可以提高模型可靠性。     

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。     

NUMERICAL VALIDATION OF COMPUTATIONAL MODEL FOR SHEET CAVITATING FLOWS

A computational modeling for the sheet cavitating flows is presented. The cavitation model is implemented in a viscous Navier-Stokes solver. The cavity interface and shape are determined using an iterative procedure matching the cavity surface to a constant pressure boundary. The pressure distribution, as well as its gradient on the wall, is taken into account in updating the cavity shape iteratively. Numerical computations are performed for the sheet cavitating flows at a range of cavitation numbers across the hemispheric headform/cylinder body with different grid numbers. The influence of the relaxation factor in the cavity shape updating scheme for the algorithm accuracy and reliability is conducted through comparison with other two cavity shape updating numerical schemes. The results obtained are reasonable and the iterative procedure of cavity shape updating is quite stable, which demonstrate the superiority of the proposed cavitation model and algorithms.     

有限元模型BERMAN修正算法的改进

BERMAN修正方法基于无阻尼系统的模态正交条件 ,利用实验模态矩阵和特征值对有限元理论模型进行修正。指出该方法存在的 2个缺陷 ,给出一种改进后的BERMAN修正方法 ,并编写了相应的迭代程序。通过修正一个实际例子表明 ,改进后的BERMAN修正方法有可取之处。     

Identifying axial load patterns using space frame FEMs and measured vibration data

The axial loading of a space frame may need to be quantified, perhaps for improvement of a finite element model (FEM) to better represent the structural dynamics or to ascertain how close the structure is to buckling. The coexistence of compressive and tensile forces in a space frame causes certain frequencies to increase with respect to load while others decrease. This intricate behaviour has been modelled in the FEM of a bi-tetrahedral space frame through consideration of the geometric stiffness, which accounts for stiffness changes in the loaded members. Updating the load pattern in the FEM using Newton's method (traditional sensitivity-based model updating) brings the model frequencies closer to those physically measured from the real bi-tetrahedral frame and thus provides identification of the axial loads. This load pattern is a predetermined set of frame axial forces in equilibrium. Such a constraint means that the extent of loading can be described by just one scalar updating parameter, an improvement upon former methods that updated member forces as independent parameters. When compared to the loads measured using strain gauges, the loads identified by model updating are seen to offer approximations of the actual loading. Difficulties such as modelling joint behaviour are discussed. The present work extends a series of numerical studies on load updating published by the authors by offering a demonstration of load pattern identification using physically measured vibration data from a real space frame.     

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.     

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.     

Adaptive regularization parameter optimization in output-error-based finite element model updating

In finite element (FE) model updating, regularization methods are required to alter the ill-conditioned system of equations towards a well-conditioned one. The present study addresses the regularization parameter determination when implementing the Tikhonov regularization technique in output-error-based FE model updating. As the output-error-based FE model updating results in a nonlinear least-squares problem which requires iteration for solution, an adaptive strategy that allows varying value of the regularization parameter at different iteration steps is formulated, where the optimal regularization parameter at each iteration step is determined based on the computationally efficient minimum product criterion (MPC). The performance of MPC in output-error-based FE model updating is examined and compared with the commonly used L-curve method (LCM) and the generalized cross validation (GCV) through numerical studies of a truss bridge using noise-free and noise-corrupted modal data. It is shown that MPC is effective and robust in determining the regularization parameter compared with the other two methods, especially when noise-corrupted data are used. The adaptive strategy is more efficient than the fixed strategy that uses a constant value of the regularization parameter throughout the iteration process.