Journal article
An inverse analysis method for design optimization with both statistical and fuzzy uncertainties
Structural and multidisciplinary optimization, Vol.37(2), pp.107-119
12/2008
DOI: 10.1007/s00158-007-0225-0
Abstract
If the statistical data for the input uncertainties are sufficient to construct the distribution function, the input uncertainties can be treated as random variables to use the reliability-based design optimization (RBDO) method; otherwise, the input uncertainties can be treated as fuzzy variables to use the possibility-based design optimization (PBDO) method. However, many structural design problems include both input uncertainties with sufficient and insufficient data. This paper proposes a new mixed-variable design optimization (MVDO) method using the performance measure approach (PMA) for such design problems. For the inverse analysis, this paper proposes a new most probable/possible point (MPPP) search method called maximal failure search (MFS), which is an integration of the enhanced hybrid mean value method (HMV+) and maximal possibility search (MPS) method. This paper also improves the HMV+ method using an angle-based interpolation. Mathematical and physical examples are used to demonstrate the proposed inverse analysis method and MVDO method.
Details
- Title: Subtitle
- An inverse analysis method for design optimization with both statistical and fuzzy uncertainties
- Creators
- Liu Du - Department of Mechanical & Industrial Engineering, College of Engineering The University of Iowa Iowa City IA 52242 USAK Choi - Department of Mechanical & Industrial Engineering, College of Engineering The University of Iowa Iowa City IA 52242 USA
- Resource Type
- Journal article
- Publication Details
- Structural and multidisciplinary optimization, Vol.37(2), pp.107-119
- Publisher
- Springer-Verlag; Berlin/Heidelberg
- DOI
- 10.1007/s00158-007-0225-0
- ISSN
- 1615-147X
- eISSN
- 1615-1488
- Language
- English
- Date published
- 12/2008
- Academic Unit
- Mechanical Engineering
- Record Identifier
- 9984064584402771
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