Integrating Distribution-Free Adaptive Kriging-based Reliability Analysis with SQP for Optimal Design Considering Uncertainties

Abstract

Reliability-Based Design Optimization (RBDO) methods are commonly used in many engineering problems and practical applications. Different from deterministic optimization (DO) methods, which do not concern the variability and uncertainty of design parameters (DP) or random parameters (RP), address only the performances but not the reliability and robustness. RBDO methods evaluate probabilistic constraints and take the variability and uncertainty of DPs and RPs into account by combining the reliability theory and optimization. The type of reliability analysis methods and the manner which they are used in conjunction with optimization algorithms strongly affect computational efficiency. In our recently research, we developed a new distribution-free adaptive Kriging-based Reliability analysis, removing dependency on sample pool and implementation of Hollow-Hypersphere Space (HHs) reduction technique. Results indicate that the proposed method could give better efficiency not only in terms of function evaluation but also in terms of Kriging evaluation. Hence, this research further incorporates optimization and the reliability analysis method we have developed, to form a single-loop RBDO for searching an engineering optimal design under uncertainties. Several examples are adopted to demonstrate the accuracy and efficiency of the proposed RBDO.