Sequential approach to reliability analysis of multidisciplinary analysis systems

The goal of this study is to present an efficient strategy for reliability analysis of multidisciplinary analysis systems. Existing methods have performed the reliability analysis using nonlinear optimization techniques. This is mainly due to the fact that they directly apply multidisciplinary design optimization (MDO) frameworks to the reliability analysis formulation. Accordingly, the reliability analysis and the multidisciplinary analysis (MDA) are tightly coupled in a single optimizer, which hampers the use of recursive and function-approximation-based reliability analysis methods such as the first-order reliability method (FORM). In order to implement an efficient reliability analysis method for multidisciplinary analysis systems, we propose a new strategy named sequential approach to reliability analysis for multidisciplinary analysis systems (SARAM). In this approach, the reliability analysis and MDA are decomposed and arranged in a sequential manner, making a recursive loop. The key features are as follows. First, by the nature of the recursive loop, it can utilize the efficient advanced first-order reliability method (AFORM). It is known that AFORM converges fast in many cases and requires only the value and the gradient of the limit-state function. Second, the decomposed architecture makes it possible to execute concurrent subsystem analyses for both the reliability analysis and MDA. The concurrent subsystem analyses are conducted by using the global sensitivity equation (GSE). The efficiency of the SARAM method was verified using two illustrative examples taken from the literatures. Compared with existing methods, it showed the least number of subsystem analyses over the other methods while maintaining accuracy.  

An efficient strategy for reliability-based multidisciplinary design optimization using BLISS

This paper presents an efficient reliability-based multidisciplinary design optimization (RBMDO) strategy. The conventional RBMDO has tri-level loops: the first level is an optimization in the deterministic space, the second one is a reliability analysis in the probabilistic space, and the third one is the multidisciplinary analysis. Since it is computationally inefficient when high-fidelity simulation methods are involved, an efficient strategy is proposed. The strategy [named probabilistic bi-level integrated system synthesis (ProBLISS)] utilizes a single-level reliability-based design optimization (RBDO) approach, in which the reliability analysis and optimization are conducted in a sequential manner by approximating limit state functions. The single-level RBDO is associated with the BLISS formulation to solve RBMDO problems. Since both the single-level RBDO and BLISS are mainly driven by approximate models, the accuracy of models can be a critical issue for convergence. The convergence of the strategy is guaranteed by employing the trust region–sequential quadratic programming framework, which validates approximation models in the trust region radius. Two multidisciplinary problems are tested to verify the strategy. ProBLISS significantly reduces the computational cost and shows stable convergence while maintaining accuracy.