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An adaptive time-based global method for dynamic reanalysis
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AI
DOI：10.1007/s00158-013-0930-9.png)
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摘要
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The popular dynamic reanalysis methods, such as combined approximation (CA) mainly focus on frequency domain. Compared with time domain analysis, the main challenge for reanalysis methods is to calculate the responses in each iteration. Due to this difficulty, popular reanalysis methods are not available for time domain analysis, such as Newmark- and central different method (CDF). Therefore, a novel adaptive time-based global reanalysis (ATGR) algorithm for Newmark- method is suggested. If basis vectors are generated for predicting the response in each time step, computational cost of reanalysis should be significantly increased. To improve the efficiency, an adaptive reanalysis algorithm is suggested. Moreover, in order to enhance the accuracy of the popular combined algorithm (CA) reanalysis, a global strategy is suggested to construct basis vectors. Numerical examples show that accurate approximations are achieved efficiently for time domain problems.
Keyword:
Dynamic reanalysis
Newmark-beta method
Global approximation
Adaptive algorithm
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