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Finite Element Model Updating Method Study Based On The Genetic Algorithm

Posted on:2008-07-22Degree:MasterType:Thesis
Country:ChinaCandidate:Q ZhouFull Text:PDF
GTID:2178360245996818Subject:General and Fundamental Mechanics
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In the development process of aerospace vehicle, the analysis and improvement of structure model help improve the mechanical environment of the model to achieve the required performance requirements. Finite element model updating technology use the experiment data of a simple structure to update the model, and obtain more accurate finite element model. Thus, it may save some large-scale structure experiments, reduce development costs, and shorten their development cycle.In the model updating the objective function which is constructed is usually in the characteristics of highly nonlinear and a number of local extremum points, this means that the traditional optimization search methods such as gradient method and Newton's iterative method are very difficult to find the global optimum.Genetic algorithm in this aspect is of great advantage. Structural theory/experimental modeling and related analysis, dynamic reduction, structure finite element model updating based on the frequency response function, substructure element method are studied in this dissertation about the key point of structure finite element model updating based on the genetic algorithm. In the model updating process, selecting updating parameters are crucial steps. Parameters updating method and substructure element method are applied in the dissertation, the updating parameters are Young's modulus and density in the parameters updating method; the updating parameters are the relative coefficient between the substructure elements in the substructure element method.The objective function directly based on the frequency response function which is widely concerned ever since the 90's is investigated. Finite element model updating based on modal and the combination of frequency response function and modal is studied, as well as the cantilever was updated. Each of them compare with the objective function base frequency response function. The results show that multi-objective optimization functions have better results. The identification damping matrix by frequency response function matrix has the better results than the calculation damping matrix by the damping ratio has. And the research results show that the noise has a minimal impact on the updating results for the model updating based genetic algorithm, genetic algorithm has a strong anti-noise capability. A cantilever is updated by substructure element method with multi-objective optimization Pareto genetic algorithm, and there is a better updating result.
Keywords/Search Tags:finite elment model updating, genetic algorithm, frequency response function, multi-objective optimization
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