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A high-performance calculation scheme for stochastic dynamic problems
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DOI：10.1016/j.ymssp.2022.110073.png)
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摘要
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The goal of stochastic dynamic problems is to efficiently and accurately calculate statistical moments and random dynamic responses. Thus, a high-performance calculation scheme posed to achieve the goal. First, to enhance the accuracy of the statistical moments, we proposed an adaptive weights quasi-Monte Carlo (AWQMC) method. Compared to the integration weights of the traditional quasi-Monte Carlo method, the adaptive weights can better reflect discrepancy of the sample points to obtain more accurate statistical moments. Additionally, adaptive weights only depend on the sample set, which can be applied to other stochastic problems. Second, the reduced-order model technology is introduced to improve the calculation efficiency of random dynamic responses. Combining of the Galerkin method, a novel reduced order model based on deterministic reduced bases is proposed for random dynamic system. Then, an efficient dynamic equation solver is constructed based on the deterministic reduced bases and the Newmark method. Finally, according to the three numerical examples, the performance calculation scheme combined the AWQMC method and the efficient dynamic equation solver can significantly improve the accuracy and efficiency of statistical moments stochastic dynamic problems.
Keyword:
Stochastic dynamic problems
High-performance calculation scheme
Adaptive weights quasi -Monte Carlo method
Deterministic reduced bases
Reduced -order model
期刊
IF:
8.9
论文数:
1.3W
被引数:
6.6W
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NeuroReport
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