Improving the evaluation efficiency of failure probability for large and complex systems using an innovative active set strategy

Abstract

Reliability-based design optimization (RBDO) is a well-known design procedure for solving uncertainties and seeking better design, but evaluation of its failure probabilistic constraints involves an unbearable computational burden. In this study, the innovative active set strategy (ASS) for the reliability index approach is proposed to improve the efficiency and robustness of evaluating probabilistic constraints for RBDO by establishing an ASS and defining an update factor. The innovative ASS is defined using inequality to effectively perform reliability analysis and obtain the feasible most probable failure point in the inner loop. Meanwhile, a defined update factor is applied to eliminate the unnecessary probability constraints and renew the active constraints in the outer loop. The inner loop and outer loop are synchronized to enhance computational efficiency during the whole optimization process. Five commonly used examples are presented, demonstrating that the ASS is a superior efficient and robust method for a large-scale system.

KEYWORDS:

• Uncertainties
• reliability-based design optimization
• reliability index approach
• active set strategy

Data availability statement

The data that support the findings of this study are available in [Mendeley Data] at [DOI:10.17632/xhsn3t2ybp.1], reference number.

Acknowledgement

The support of the National Natural Science Foundation of China [grant number 51679056] is greatly appreciated.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This study was supported by the National Natural Science Foundation of China [grant number 51679056].