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Structural and Multidisciplinary Optimization

Erschienen in:

08.12.2016 | RESEARCH PAPER

Interval prediction of responses for uncertain multidisciplinary system

verfasst von: Xiaojun Wang, Ruixing Wang, Xianjia Chen, Lei Wang, Xinyu Geng, Weichao Fan

Erschienen in: Structural and Multidisciplinary Optimization | Ausgabe 6/2017

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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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Titel: Interval prediction of responses for uncertain multidisciplinary system
verfasst von: Xiaojun Wang
Ruixing Wang
Xianjia Chen
Lei Wang
Xinyu Geng
Weichao Fan
Publikationsdatum: 08.12.2016
Verlag: Springer Berlin Heidelberg
Erschienen in: Structural and Multidisciplinary Optimization / Ausgabe 6/2017
Print ISSN: 1615-147X
Elektronische ISSN: 1615-1488
DOI: https://doi.org/10.1007/s00158-016-1601-4

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