A New Approach to Solve Uncertain Multidisciplinary Design Optimization Based on Conditional Value at Risk

Design optimization of complex engineering problems often involves multiple disciplines or subsystems that usually exist couplings or data interactions with each other. Multidisciplinary design optimization (MDO) is an advanced methodology to deal with such problems. Besides, uncertainty is a crucial factor affecting the performance of complex systems. Therefore, uncertain MDO (UMDO) is the focus of current engineering design research. This article proposes a novel (UMDO) method based on the conditional value at risk (CVaR) as a supplement and alternative scheme to traditional (UMDO) approaches. First, the number of multidisciplinary analyses of complex systems was reduced using collaboration models. Second, metamodels were constructed to simulate data interaction between multidisciplinary systems. Then, an approximate method for CVaR under uncertainty risk analysis was derived. A UMDO framework based on CVaR was constructed. The optimization process was driven by the gradient-based Monte Carlo simulation method. Finally, three different complexity examples verified the accuracy and efficiency of the proposed approach.