Abstract
Concurrent engineering has obtained increasing attention to solve the design problems of multidisciplinary systems. In practical engineering, there are epistemic uncertainties during whole design cycle of complex systems. Especially in earlier design phases, the effects of epistemic uncertainties are not usually easy to be quantified. It is because design information is insufficient. Furthermore, commonly used probability theory is also not suitable to be utilized. In this situation, epistemic uncertainties will be introduced unavoidably by mathematical models or simulation tools and may affect the performance of complex system significantly. To solve this problem, evidence theory is introduced and combined with the collaborative optimization method in this study. An evidence-based collaborative reliability optimization method is also proposed. Evidence theory is a powerful approach to handle epistemic uncertainties by Plausibility and Belief. Meanwhile, collaborative optimization is widely utilized in the concurrent design of complex systems. An aircraft conceptual design problem is utilized to show the application of the proposed method.
Highlights
The probability theory is one of the classical methods which is commonly used in reliability-based multidisciplinary design optimization (RBMDO)
The uncertainty measure Bel is utilized here to estimate the reliability constraints based on expert opinions
An aircraft conceptual design problem is solved to show the application of the given strategy
Summary
Reliability-based multidisciplinary design optimization (RBMDO) is becoming a focus of concurrent design for complex systems.[1,2,3,4,5,6,7,8,9,10] The probability theory is one of the classical methods which is commonly used in RBMDO. Keywords Concurrent engineering, multidisciplinary systems, epistemic uncertainties, evidence theory, collaborative optimization Evidence theory has obtained more attentions in practical engineering.[9,17,18,19,20] It can analysis epistemic uncertainties using human thought process, and provide corresponding descriptions dependent on incomplete or conflicting information.[17] In this study, evidence theory is introduced and combined with collaborative optimization (CO) to solve concurrent design problems of multidisciplinary systems.
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