A general methodology for reliability-based robust design optimization of computation-intensive engineering problems

As for complex engineering structures, their single deterministic solutions need to be calculated with the help of computation-intensive finite element software, and the corresponding computation time always lasts very long. When referring to robust design optimization of complex engineering structures, the above expensive job should be repeatedly carried out for a considerable amount of time during the full optimization process. Hence, such repetitive jobs may be difficult to complete for most engineering applications. Aiming at this problem, the paper proposes a general methodology for reliability-based robust design optimization (RBRDO). Firstly, the improved formulation of the RBRDO problem is proposed based on the conventional RBRDO form, and the original probabilistic constraint functions in the improved formulation are changed into the deterministic ones by means of employing the performance measure approach (PMA). Secondly, the above-mentioned improved RBRDO problem is approximately replaced by a new sequence of approximate suboptimizations. For each suboptimization, its original PMA functions are replaced by the approximate explicit form only concerning the deterministic design variables rather than random variables. In this way, each suboptimization is deterministic. Furthermore, the trust-region method is adopted to ensure that the new sequence of suboptimization converge to the original improved RBRDO problem. Lastly, a test problem and three applications are adopted to demonstrate the effectiveness and efficiency of the above-proposed methods. PACS(optional, as per journal): 75.40.-s; 71.20.LP

Automated summary

This paper presents a general methodology for reliability-based robust design optimization, addressing the computational complexity and lengthy computation time in complex engineering structures. Through improved formulation and approximate suboptimization sequence, the approach converts original probabilistic constraints into deterministic functions and ensures convergence to the original problem. The effectiveness and efficiency of the proposed methods are demonstrated through a test problem and three applications.