Details
Original language | English |
---|---|
Pages (from-to) | 2593-2608 |
Number of pages | 16 |
Journal | Structural and Multidisciplinary Optimization |
Volume | 64 |
Issue number | 4 |
Early online date | 12 Aug 2021 |
Publication status | Published - Oct 2021 |
Abstract
This paper proposes a novel method to address the challenges associated with reliability-based design optimization (RBDO) of a class of problems, namely design of a linear system with random structural parameters subject to stochastic excitation. This method effectively estimates the failure probability as an explicit function of the design variables by representing the failure probability function (FPF) as a weighted average of sample values, which are generated by means of a single reliability analysis. The resulting estimation of the FPF is then used to decouple the target RBDO problem into a deterministic optimization problem, which can be solved by any appropriate deterministic optimization algorithm. In addition, a sequential approximate optimization framework is adopted to iteratively seek the solution of the RBDO problem. Several examples are provided to demonstrate the high accuracy and efficiency of the proposed method.
Keywords
- Decoupling approach, High dimensions, Importance sampling, Linear dynamic system, Reliability-based optimization, Uncertain systems
ASJC Scopus subject areas
- Engineering(all)
- Control and Systems Engineering
- Computer Science(all)
- Software
- Computer Science(all)
- Computer Science Applications
- Computer Science(all)
- Computer Graphics and Computer-Aided Design
- Mathematics(all)
- Control and Optimization
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In: Structural and Multidisciplinary Optimization, Vol. 64, No. 4, 10.2021, p. 2593-2608.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Efficient reliability-based optimization of linear dynamic systems with random structural parameters
AU - Yuan, Xiukai
AU - Gu, Jian
AU - Wu, Mingying
AU - Zhang, Feng
N1 - Funding Information: Our deepest gratitude goes to Prof. Marcos A. Valdebenito (from Universidad Adolfo Ibáñez, Chile) for his careful review and revision that have helped improving this paper. Funding Information: This work was supported in part by the Natural Science Foundation of Shanxi Province (2019JM-377), the Fundamental Research Funds for the Central Universities (310202006zy007), NSAF (Grant No. U1530122), the Aeronautical Science Foundation of China (Grant No. ASFC-20170968002) and the Fundamental Research Funds for the Central Universities of China (XMU, 20720180072).
PY - 2021/10
Y1 - 2021/10
N2 - This paper proposes a novel method to address the challenges associated with reliability-based design optimization (RBDO) of a class of problems, namely design of a linear system with random structural parameters subject to stochastic excitation. This method effectively estimates the failure probability as an explicit function of the design variables by representing the failure probability function (FPF) as a weighted average of sample values, which are generated by means of a single reliability analysis. The resulting estimation of the FPF is then used to decouple the target RBDO problem into a deterministic optimization problem, which can be solved by any appropriate deterministic optimization algorithm. In addition, a sequential approximate optimization framework is adopted to iteratively seek the solution of the RBDO problem. Several examples are provided to demonstrate the high accuracy and efficiency of the proposed method.
AB - This paper proposes a novel method to address the challenges associated with reliability-based design optimization (RBDO) of a class of problems, namely design of a linear system with random structural parameters subject to stochastic excitation. This method effectively estimates the failure probability as an explicit function of the design variables by representing the failure probability function (FPF) as a weighted average of sample values, which are generated by means of a single reliability analysis. The resulting estimation of the FPF is then used to decouple the target RBDO problem into a deterministic optimization problem, which can be solved by any appropriate deterministic optimization algorithm. In addition, a sequential approximate optimization framework is adopted to iteratively seek the solution of the RBDO problem. Several examples are provided to demonstrate the high accuracy and efficiency of the proposed method.
KW - Decoupling approach
KW - High dimensions
KW - Importance sampling
KW - Linear dynamic system
KW - Reliability-based optimization
KW - Uncertain systems
UR - http://www.scopus.com/inward/record.url?scp=85112363928&partnerID=8YFLogxK
U2 - 10.1007/s00158-021-03011-0
DO - 10.1007/s00158-021-03011-0
M3 - Article
AN - SCOPUS:85112363928
VL - 64
SP - 2593
EP - 2608
JO - Structural and Multidisciplinary Optimization
JF - Structural and Multidisciplinary Optimization
SN - 1615-147X
IS - 4
ER -