Review of finite element model updating methods for structural applications

Multiobjective-clustering-based Optimal Heterogeneous Sensor Placement Method for Thermo-mechanical Load Identification

https://zenodo.org/record/5520607/files/Manuscript.pdf

Nonlinear model updating through a hierarchical Bayesian modeling framework

Dynamic sensitivity-based finite element model updating for nonlinear structures using time-domain responses

Finite element explicit dynamics simulation of motion and shedding of jujube fruits under forced vibration

Robustly identifying nonlinear mechanical systems is generally a challenging task, and this is particularly true when the structure under test exhibits nonlinear behaviors related to both stiffness...

Identification of Nonlinear Stiffness and Damping Parameters Using a Hybrid Approach

Model updating plays an important role in dynamics modeling with high accuracy, which is widely used in mechanical engineering. In this paper, a model updating method using frequency response function (FRF) is proposed based on Sherman–Morrison formula, in which only the initial FRFs and parameter perturbations are employed to calculate the sensitivity avoiding repeated finite element (FE) analyses and improving the computational efficiency. Firstly, the sensitivity of FRFs to the design parameters is calculated by Sherman–Morrison formula based on the QR decomposition of the system dynamic stiffness matrix variation after parameter perturbations, then the influence of damping on the amplitude of FRFs is considered to select an appropriate frequency range, and finally conduct the model updating according to the sensitivity of the FRFs. By employing simulation examples of a truss and a solar wing and the experiment of an aluminum frame, the updating error is still within ±1.00% in the condition of 5% random white noise, which shows the proposed method has high accuracy and a certain anti-noise capability. When only a few numbers of frequency points are selected near the resonance peak of the FRFs, the result shows that selecting the appropriate frequency range and points can reduce the computational cost. The results of the experiment study show that the proposed method can effectively identify the structural parameters. The above results verify the feasibility and effectiveness of proposed model updating method using FRFs.

Model Updating Using Frequency Response Functions Based on Sherman–Morrison Formula


 Sensitivity analysis of transient response plays a crucial role in structural dynamics optimization. In this paper, the sensitivity analysis method for calculating the first-order derivatives of the nonlinear transient response using the real-imaginary perturbations (RIP) in the complex domain is proposed. Independent design parameters are synchronously perturbed using real and imaginary perturbations, respectively. The complex variable finite element method is employed to compute the complex transient response of the perturbed model. The nonlinear transient response sensitivities with respect to each parameter are obtained by separately extracting the real and imaginary responses. The computational accuracy and efficiency of the proposed method are demonstrated by employing a nonlinear multidegree-of-freedom system and a cantilever beam with nonlinear elastic supports. Results show that the response sensitivity with respect to multiple design parameters is obtained accurately by using the proposed method, the computational efficiency of which is increased compared with the complex variable method (CVM). The stability of the transient response sensitivity is significantly affected by the parameter perturbations in the real and imaginary parts. The RIP-imaginary-based response sensitivity is feasible even for an extremely small perturbation in the imaginary part; the RIP-real-based response sensitivity is stable only within a limited real perturbation range.

Sensitivity Analysis of Nonlinear Transient Response Based on Perturbation in the Complex Domain

The invention provides a multi-sensor additional mass elimination method in a modal test. A multi-sensor mass influence elimination formula in the modal test is deducted, the number of sensors arranged in the modal test and the sequence for eliminating the mass of the sensors are determined, and frequency response functions for eliminating the influence of the mass of the sensors are calculated one by one according to the elimination formula based on actual measurement frequency response functions. According to the method, firstly, the sensor mass influence elimination formula is deducted based on the Sherman-Morrison formula, the required frequency response functions are actually measured according to the formula and substituted into the formula in sequence for elimination one by one, and finally elimination of the mass of multiple sensors is realized. According to the method, measuring errors caused by additional mass of the sensors in the contact type measuring method in the modal test are eliminated, the unfavorable influence of the mass of multiple sensors on the frequency response functions is eliminated through the processing of actual measurement frequency response function signals, and the practical engineering significance is achieved.

Multi-sensor additional mass elimination method in modal test

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