Details
| Original language | English |
|---|---|
| Article number | 020904 |
| Number of pages | 8 |
| Journal | ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering |
| Volume | 7 |
| Issue number | 2 |
| Early online date | 23 Apr 2021 |
| Publication status | Published - Jun 2021 |
Abstract
This contribution proposes a strategy for performing fuzzy analysis of linear static systems applying a-level optimization. In order to decrease numerical costs, full system analyses are replaced by a reduced order model that projects the equilibrium equations to a small-dimensional space. The basis associated with the reduced order model is constructed by means of a single analysis of the system plus a sensitivity analysis. This reduced basis is enriched as the a-level optimization strategy progresses in order to protect the quality of the approximations provided by the reduced order model. A numerical example shows that with the proposed strategy, it is possible to produce an accurate estimate of the membership function of the response of the system with a limited number of full system analyses.
ASJC Scopus subject areas
- Engineering(all)
- Mechanical Engineering
- Engineering(all)
- Safety, Risk, Reliability and Quality
- Social Sciences(all)
- Safety Research
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In: ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering, Vol. 7, No. 2, 020904, 06.2021.
Research output: Contribution to journal › Article › Research
}
TY - JOUR
T1 - Application of a Reduced Order Model for Fuzzy Analysis of Linear Static Systems
AU - Valdebenito, Marcos A.
AU - Jensen, Héctor A.
AU - Wei, Pengfei
AU - Beer, Michael
AU - Beck, André T.
N1 - Funding Information: This research is partially supported by ANID (National Agency for Research and Development, Chile) under its program FONDE-CYT, Grant Nos. 1180271 and 1200087, and ANID+FAPESP (São Paulo State Foundation for Research, Brazil), Grant No. 2019/13080-9. The first author developed part of this work during a research stay at the Institute for Risk and Reliability (IRZ) of the Leibniz Universit€at Hannover, Germany. Both the first and third authors conducted this research under the auspice of the Alexander von Humboldt Foundation. This support is gratefully acknowledged by the authors.
PY - 2021/6
Y1 - 2021/6
N2 - This contribution proposes a strategy for performing fuzzy analysis of linear static systems applying a-level optimization. In order to decrease numerical costs, full system analyses are replaced by a reduced order model that projects the equilibrium equations to a small-dimensional space. The basis associated with the reduced order model is constructed by means of a single analysis of the system plus a sensitivity analysis. This reduced basis is enriched as the a-level optimization strategy progresses in order to protect the quality of the approximations provided by the reduced order model. A numerical example shows that with the proposed strategy, it is possible to produce an accurate estimate of the membership function of the response of the system with a limited number of full system analyses.
AB - This contribution proposes a strategy for performing fuzzy analysis of linear static systems applying a-level optimization. In order to decrease numerical costs, full system analyses are replaced by a reduced order model that projects the equilibrium equations to a small-dimensional space. The basis associated with the reduced order model is constructed by means of a single analysis of the system plus a sensitivity analysis. This reduced basis is enriched as the a-level optimization strategy progresses in order to protect the quality of the approximations provided by the reduced order model. A numerical example shows that with the proposed strategy, it is possible to produce an accurate estimate of the membership function of the response of the system with a limited number of full system analyses.
UR - http://www.scopus.com/inward/record.url?scp=85126837107&partnerID=8YFLogxK
U2 - 10.1115/1.4050159
DO - 10.1115/1.4050159
M3 - Article
VL - 7
JO - ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering
JF - ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering
SN - 2332-9017
IS - 2
M1 - 020904
ER -