Journal
STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION
Volume 40, Issue 1-6, Pages 165-175Publisher
SPRINGER
DOI: 10.1007/s00158-008-0348-y
Keywords
Multidisciplinary design optimization; Aleatory uncertainty; Epistemic uncertainty; Mixed Variables (random and fuzzy variables); Multidisciplinary Design Optimization (MVMDO); Sequential optimization and reliability assessment (SORA)
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Funding
- National Natural Science Foundation of China [50775026]
- National High Technology Research and Development Program of China [2007AA04Z403]
- Doctoral Program of Higher Education of China [20060614016]
- Provincial Key Technologies R&D Program of Sichuan [07GG012-002]
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In engineering design, to achieve high reliability and safety in complex and coupled systems (e.g., Multidisciplinary Systems), Reliability Based Multidisciplinary Design Optimization (RBMDO) has been received increasing attention. If there are sufficient data of uncertainties to construct the probability distribution of each input variable, the RBMDO can efficiently deal with the problem. However there are both Aleatory Uncertainty (AU) and Epistemic Uncertainty (EU) in most Multidisciplinary Systems (MS). In this situation, the results of the RBMDO will be unreliable or risky because there are insufficient data to precisely construct the probability distribution about EU due to time, money, etc. This paper proposes formulations of Mixed Variables (random and fuzzy variables) Multidisciplinary Design Optimization (MVMDO) and a method of MVMDO within the framework of Sequential Optimization and Reliability Assessment (MVMDO-SORA). The MVMDO overcomes difficulties caused by insufficient information for uncertainty. The proposed method enables designers to solve MDO problems in the presence of both AU and EU. Besides, the proposed method can efficiently reduce the computational demand. Examples are used to demonstrate the proposed formulations and the efficiency of MVMDO-SORA.
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