• The KSFM Journal of Fluid Machinery
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• v.3 no.3 s.8
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• pp.19-24
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• 2000

It is well known that the finite element analysis often has an inaccuracy when it is in conflict with test results. Model updating is concerned with the correction of analytical model by processing records of response from test results. The famous one of the model updating methods is FRF sensitivity method. However, it has demerit that the solution is not unique. So, the neural network is recommended when an unique and exact solution is desired. The generalization ability of radial basis function neural network is used in model updating. As an application model, a cantilever and a rotor system are used. Specially the machined clearance($C_p$) of a journal bearing is updated.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2005.11a
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• pp.963-966
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• 2005

Model updating method is known to the area to correct finite element models by the results of the experimental modal analysis. Most common methods in model updating depend on a parametric model of the structure. In this case, the number of parameters is normally smaller than that of modal data obtained from an experiment. In order to overcome this limitation, many researchers are trying to get modal data as many as possible to date. 1 want to name this method multiple modified-system generation method. These Methods consist of direct system modification method and feedback controller method. The direct system modification Is to add a mass or stiffness on the original structure or perturb the boundary conditions. The feedback controller method is to make the closed food system with sensor and actuator so as to get the closed loop modal data. In this paper, we need to focus on the feedback controller method because of its simplicity. Several methods related the feedback controller methods are virtual passive controller (VPC) sensitivity enhancement controller (SEC) and mode decoupling controller (MDC). Among them, we will apply MDC to the model updating problem. MDC has various advantages compared with other controllers, such as VPC and SEC. To begin with, only the target mode can be changed without changing modal property of non-target modes. In addition, it is possible to fix any modes if the number of sensors is equal to that of the system modes. Finally, the required control power to achieve desired change of target mode is always lower than those of other methods such as VPC. However, MDC can make the closed loop system unstable when using incomplete modal data. So we need to take action to avoid undesirable instability from incomplete modal data. In this paper, we address the method to design the unique and robust MDD obtained from incomplete modal data. The associated simulation will be Incorporated to demonstrate the usefulness of this method.

• Journal of the Earthquake Engineering Society of Korea
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• v.10 no.6 s.52
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• pp.19-28
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• 2006

Most conventional model updating methods must use mathematical objective function with experimental modal matrices and analytical system matrices or must use information about the gradient or higher derivatives of modal properties with respect to each updating parameter. Therefore, most conventional methods are not appropriate for complex structural system such as bridge structures due to stability problem in inverse analysis with ill-conditions. Sometimes, moreover, the updated model may have no physical meaning. In this paper, a new FE model updating method based on a hybrid optimization technique using genetic algorithm (GA) and Holder-Mead simplex method (NMS) is proposed. The performance of hybrid optimization technique on the nonlinear problem is demonstrated by the Goldstein-Price function with three local minima and one global minimum. The influence of the objective function is evaluated by the case study of a simulated 10-dof spring-mass model. Through simulated case studies, finally, the objective function is proposed to update mass as well as stiffness at the same time. And so, the proposed hybrid optimization technique is proved to be an efficient method for FE model updating.

• Proceedings of the KSME Conference
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• 2001.06b
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• pp.335-340
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• 2001

It is important to model the mechanical structure precisely and reasonably in predicting the dynamic characteristics, controlling the vibration, and designing the structure dynamics. In the finite element modeling, the errors can be contained from the physical parameters, the approximation of the boundary conditions, and the element modeling. From the dynamic test, more precise dynamic characteristics can be obtained. Model updating using parameter modification is appropriate when the design parameter is used to analyze the input parameter like finite element method. Finite element analysis for cantilever and simply supported beams with uniform area and shape change are carried out as model updating examples. Mass and stiffness matrices are updated by comparing test and analytical modal frequencies. The result shows that the updated frequencies become closer to the test frequencies.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2001.11b
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• pp.1260-1265
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• 2001

It is important to model the mechanical structure precisely and reasonably in predicting the dynamic characteristics, controlling the vibration, and designing the structural dynamics. In the finite element modeling, the errors can be contained from the physical parameters, the approximation of the boundary conditions, and the element modeling, From the dynamic test. more precise dynamic characteristics can be obtained. Model updating using parameter modification is appropriate when the design parameter is used to analyze the input parameter like finite element method. Finite element analysis for free-free-free-free(FFFF) and clamped-free-free-free(CFFF) plate with uniform area and shape change are carried out as model updating examples, Mass and stiffness matrices are updated by comparing test and analytical modal frequencies. The result shows that the updated frequencies become closer to the test frequencies.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.24 no.5 s.176
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• pp.1133-1145
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• 2000

It is necessary to develop an efficient analysis method to identify the dynamic characteristics of a large mechanical structure and update its finite element model. That is because these processes need the huge computation of a large structure and iterative estimation due to the use of the first- order sensitivity. To efficiently carry out these processes, a new method, called the generalized free-interface mode sensitivity method, has been proposed in the authors' preceeding paper. This method is based on substructuring approach such as a free-interface method and a generalized synthesis algorithm. In this paper, the proposed method is applied to the model updating of a car body structure to verify its accuracy and reliability for a large mechanical structure.

• Structural Engineering and Mechanics
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• v.88 no.5
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• pp.493-500
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• 2023

The multiparameter eigenvalue method can be used to solve the damped finite element model updating problems. This method transforms the original problems into multiparameter eigenvalue problems. Comparing with the numerical methods based on various optimization methods, a big advantage of this method is that it can provide all possible choices of physical parameters. However, when solving the transformed singular multiparameter eigenvalue problem, the proposed method based on the generalised inverse of a singular matrix has some computational challenges and may fail. In this paper, more details on the transformation from the dynamic model updating problem to the multiparameter eigenvalue problem are presented and the structure of the transformed problem is also exposed. Based on this structure, the rigorous mathematical deduction gives the upper bound of the number of possible choices of the physical parameters, which confirms the singularity of the transformed multiparameter eigenvalue problem. More importantly, we present a row and column compression method to overcome the defect of the proposed numerical method based on the generalised inverse of a singular matrix. Also, two numerical experiments are presented to validate the feasibility and effectiveness of our method.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2004.04a
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• pp.49-56
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• 2004

Model updating is a very active research field, in which significant efforts has been invested in recent years. Model updating methodologies are invariably successful when used on noise-free simulated data, but tend to be unpredictable when presented with real experimental data that are-unavoidably-corrupted with uncorrected noise content. In this paper, Reanalysis using frequency response functions for correlating and updating dynamic systems is presented. A transformation matrix is obtained from the relationship between the complex and the normal frequency response functions of a structure. The transformation matrix is employed to calculate the modified damping matrix of the system. The modified mass and stiffness matrices are identified from the normal frequency response functions by using the least squares method. One simulated system is employed to illustrate the applicability of the proposed method. The result indicate that the damping matrix of correlated finite element model can be identified accurately by the proposed method. In addition, the robustness of the new approach uniformly distributed measurement noise Is also addressed.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.17 no.4 s.121
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• pp.324-332
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• 2007

Sensor placement method for damage identification has been developed for model updating using Taguchi method. In order to select the optimal sensor location, the analysis of variance of objective function using orthogonal array was carried out. Then, modal data at the selected locations were used for damage identification using model updating. The numerical model of a cantilever beam was used in order to compare the damage identification results with conventional sensor location method.

• Smart Structures and Systems
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• v.26 no.1
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• pp.63-75
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• 2020

Substructure pseudo-dynamic hybrid simulation (SPDHS) combining the advantages of physical experiments and numerical simulation has become an important testing method for evaluating the dynamic responses of structures. Various parameter identification methods have been proposed for online model updating. However, if there is large model gap between the assumed numerical models and the real models, the parameter identification methods will cause large prediction errors. This study presents an ANN (artificial neural network) method based on forgetting factor. During the SPDHS of model updating, a dynamic sample window is formed in each loading step with forgetting factor to keep balance between the new samples and historical ones. The effectiveness and anti-noise ability of this method are evaluated by numerical analysis of a six-story frame structure with BRBs (Buckling Restrained Brace). One BRB is simulated in OpenFresco as the experimental substructure, while the rest is modeled in MATLAB. The results show that ANN is able to present more hysteresis behaviors that do not exist in the initial assumed numerical models. It is demonstrated that the proposed method has good adaptability and prediction accuracy of restoring force even under different loading histories.