A Study on Computational Efficiency Improvement of Novel SORM Using the Convolution Integration

Cited 19 time in webofscience Cited 0 time in scopus
  • Hit : 604
  • Download : 0
This paper proposes to apply the convolution integral method to the novel second-order reliability method (SORM) to further improve its computational efficiency. The novel SORM showed better accuracy in estimating the probability of failure than conventional SORMs by utilizing a linear combination of noncentral or general chi-squared random variables. However, the novel SORM requires significant computational time when integrating the linear combination to calculate the probability of failure. In particular, when the dimension of performance functions is higher than three, the computational time for full integration increases exponentially. To reduce this computational burden for the novel SORM, we propose to obtain the distribution of the linear combination using the convolution and to use the distribution for the probability of failure estimation. Since it converts an N-dimensional full integration into one-dimensional integration, the proposed method is computationally very efficient. Numerical study illustrates that the accuracy of the proposed method is almost the same as the full integral method and Monte Carlo simulation (MCS) with much improved efficiency.
Publisher
ASME
Issue Date
2018-02
Language
English
Article Type
Article
Keywords

DIMENSION REDUCTION METHOD; RELIABILITY-ANALYSIS; DESIGN OPTIMIZATION; PROBABLE POINT; APPROXIMATION

Citation

JOURNAL OF MECHANICAL DESIGN, v.140, no.2, pp.24501

ISSN
1050-0472
DOI
10.1115/1.4038563
URI
http://hdl.handle.net/10203/238739
Appears in Collection
ME-Journal Papers(저널논문)
Files in This Item
There are no files associated with this item.
This item is cited by other documents in WoS
⊙ Detail Information in WoSⓡ Click to see webofscience_button
⊙ Cited 19 items in WoS Click to see citing articles in records_button

qr_code

  • mendeley

    citeulike


rss_1.0 rss_2.0 atom_1.0