Open AccessA Reliability based Multidisciplinary Design Optimization Method with Multi-Source Uncertainties
Author(s) -
Chao Fu,
Jihong Liu,
Weiran Xu,
Hongliang Yu
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1654/1/012043
Subject(s) - multidisciplinary design optimization , reliability (semiconductor) , multidisciplinary approach , computer science , uncertainty quantification , reliability engineering , set (abstract data type) , optimization problem , engineering optimization , uncertainty analysis , mathematical optimization , machine learning , engineering , mathematics , algorithm , simulation , social science , power (physics) , physics , quantum mechanics , sociology , programming language
The complexity of engineering systems is increasing greatly, and more coupled disciplines and multi-source uncertainties are involved in the design and development of complex engineered systems (CESs). Actually, uncertainties are ubiquitous in design of CESs, which can be mainly classified into alteatory uncertainty (AU) and epistemic uncertainty (EU). To gain high reliability and safety of CESs, the Reliability-based MDO (RBMDO) which considers uncertainties of design variables and parameters has become a hot research topic. In this paper, the quantification of aleatory and epistemic uncertainties based on the probability theory and convex set-theory, multidisciplinary comprehensive reliability evaluation index, adaptive collaborative optimization strategy based on the intelligent optimization algorithm, sequential multidisciplinary reliability analysis method based on the concurrent subspace optimization (CSSO) and performance measure approach (PMA), and hierarchical and hybrid sequential optimization and reliability assessment (HSORA) RBMDO strategy under multi-source uncertainties have been researched. All of the researches can expand and improve the RBMDO theoretical system, and also provide the effective method for the design and optimization of CESs under multi-source uncertainties.