Academic literature on the topic 'Finite element modal updating'

The main approach of this thesis was to develop a mathematical model that represents a rotary machine. Experimental data was used to define a finite element model (FEM). In order to obtain the experimental data, the rotary machine had to be balanced. An impact hammer test made it possible to obtain frequency response functions (FRF). The frequency response functions were curvefitted in order to obtain the mode shapes and natural frequencies. Mathematical models have been created with ABAQUS and Matlab. For the Matlab Model the assumption has been made that the rotor machine consists of a specific number of beam elements. The FEM matrices have been reduced with the Guyan Reduction Method to coincide with the DOFs of the experiment. Applying the method of the least square to an Error Function made it possible to obtain new values for the stiffness and damping of the bearings (). This made it possible to update the mathematical model. By applying the Model Assumption Criterion the theoretical model and those detected from the experimental measurement could be validated. The correlation for Mode Shapes 1 could be improved from 0.6647 to 0.8186 and for Mode Shape 2 from 0.0209 to 0.4208. Therefore, the created method could be proven to work. Additionally the whole theory has been validated with a very simplified model.

This thesis addresses the problem of identifying the modal properties of a system based only on measurements of the system responses. This situation is frequently encountered in structural dynamics and is particularly relevant for systems where the in-service excitation is not artificially reproducible. The inherent non-linearities in these systems mean that the modal properties estimated using traditional input/output techniques will be different to those exhibited in operation. A common example from the literature is an aircraft in flight where the modal properties are heavily influenced by the operating point, i.e. the combination of load, speed, altitude etc., at which the aircraft is travelling. The process of identifying the modal properties of systems in-service is called Operational Modal Analysis (OMA). Not knowing the input complicates the analysis. Most of the techniques in the literature overcome the lack of knowledge about the unmeasured excitations by assuming they are both spatially and frequentially white, i.e. of equal magnitude and with a flat autospectrum. This thesis presents a new technique for OMA which relaxes these constraints, requiring only that the system is excited by a so called cyclostationary input with a unique cyclic frequency, and that the log spectrum of the second order component of this input is frequentially smooth, as will be explained. Such systems include vehicles with internal combustion engines as the vibration from such an engine exhibits cyclostationary statistics. In this thesis, the technique is applied to a laboratory test rig and a passenger train both using an artificial input, and to a race car using the engine as the excitation. By combining cyclostationary signal processing and the concept of the cepstrum, the technique identifies the resonances and anti-resonances in the transfer functions between each response and the cyclostationary source. These resonances and antiresonances can be used to regenerate Frequency Response Functions (FRFs) and it is shown how the unknown scaling of the system can be recovered by employing finite element model updating in conjunction with this regeneration. In addition, the contribution made to model updating by the anti-resonances is also investigated. Finally, the potential of OMA to inform a model updating process is demonstrated using an experimental case study on a diesel railcar.

Initial forms of analytical models created to simulate real engineering structures may generally yield dynamic response predictions different than those obtained from experimental tests. Since testing a real structure under every possible excitation is not practical, it is essential to transform the initial mathematical model to a model which reflects the characteristics of the actual structure in a better way. By using structural model updating techniques, the initial mathematical model is adjusted so that it simulates the experimental measurements more closely. In this study, a sensitivity-based finite element (FE) model updating method using experimental frequency response (FRF) data is presented. This study bases on a technique developed in an earlier study on the computation of the so-called Mis-correlation Index (MCI) used for identifying the system matrices which require updating. MCI values are calculated for each required coordinate, and non-zero numerical values indicate coordinates carrying error. In this work a new model updating procedure based on the minimization of this index is developed. The method uses sensitivity approach. FE models are iteratively updated by minimizing MCI values using sensitivities. The validation of the method is realized through some case studies. In order to demonstrate the application of the method for real systems, a real test data obtained from the modal test of a scaled aircraft model (GARTEUR SM-AG19) is used. In the application, the FE model of the scaled aircraft is updated. In the case studies the generic software developed in this study is used along with some commercial programs.

The characterization of damping is important in making accurate predictions of both the true response and the frequency response of any device or structure dominated by energy dissipation. The process of modeling damping matrices and experimental verification of those is challenging because damping can not be determined via static tests as can mass and stiffness. Furthermore, damping is more difficult to determine from dynamic measurements than natural frequency. However, damping is extremely important in formulating predictive models of structures. In addition, damping matrix identification may be useful in diagnostics or health monitoring of structures. The objective of this work is to find a robust, practical procedure to identify damping matrices. All aspects of the damping identification procedure are investigated. The procedures for damping identification presented herein are based on prior knowledge of the finite element or analytical mass matrices and measured eigendata. Alternately, a procedure is based on knowledge of the mass and stiffness matrices and the eigendata. With this in mind, an exploration into model reduction and updating is needed to make the problem more complete for practical applications. Additionally, high performance computing is used as a tool to deal with large problems. High Performance Fortran is exploited for this purpose. Finally, several examples, including one experimental example are used to illustrate the use of these new damping matrix identification algorithms and to explore their robustness.<br>Ph. D.