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Interval prediction of responses for uncertain multidisciplinary system

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Abstract

Considering that numerous sample data points are required in the probabilistic method, a non-probabilistic interval analysis method can be an alternative when the information is insufficient. In the paper, new strategies, which are iterative algorithm based interval uncertainty analysis methods (IA-IUAMs), are developed to acquire the bounds of the responses in multidisciplinary system. Two iterative processes, Jacobi iteration and Seidel iteration, are applied in the new methods respectively. The Jacobi iteration based interval uncertainty analysis method (JI-IUAM) utilizes the strategy of concurrent subsystem analysis to improve computational efficiency while the Seidel iteration based interval uncertainty analysis method (SI-IUAM) can accelerate convergence by utilizing the newest information. Both IA-IUAMs are able to evaluate the bounds of responses accurately and quickly. The presented methods are compared with general sensitivity analysis based interval uncertainty analysis method (SIUAM) and conventional Monte Carlo simulation approach (MCS). The validity and efficiency of the new methods are demonstrated by two numerical examples and two engineering examples. Results show that, on the one hand, IA-IUAMs are more efficient than MCS by avoiding hundreds of system analyses, on the other hand, IA-IUAMs are more accurate and have a wider range of application than SIUAM by avoiding linear approximation and global sensitivity calculation.

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Acknowledgments

The authors would like to thank the major research project (No. MJ-F-2012-04), Defense Industrial Technology Development Program (No. JCKY2013601B001, No. JCKY2016601B001), the China Postdoctoral Science Foundation (2016 M591038) and National Nature Science Foundation of the P. R. China (No. 11372025, No. 11432002, No. 11572024) for the financial supports. Besides, the authors wish to express their sincere thanks to the reviewers for their useful and constructive comments.

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Authors and Affiliations

  1. Institute of Solid Mechanics, Beihang University, Beijing, 100083, China

    Xiaojun Wang, Ruixing Wang, Xianjia Chen, Lei Wang, Xinyu Geng & Weichao Fan

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  1. Xiaojun Wang
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  2. Ruixing Wang
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  3. Xianjia Chen
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  4. Lei Wang
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Correspondence to Lei Wang.

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Wang, X., Wang, R., Chen, X. et al. Interval prediction of responses for uncertain multidisciplinary system. Struct Multidisc Optim 55, 1945–1964 (2017). https://doi.org/10.1007/s00158-016-1601-4

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