Adaptive Surrogate Modeling for Time-Dependent Multidisciplinary Reliability Analysis

Abstract

Multidisciplinary systems with transient behavior under time-varying inputs and coupling variables pose significant computational challenges in reliability analysis. Surrogate models of individual disciplinary analyses could be used to mitigate the computational effort; however, the accuracy of the surrogate models is of concern, since the errors introduced by the surrogate models accumulate at each time-step of the simulation. This paper develops a framework for adaptive surrogate-based multidisciplinary analysis (MDA) of reliability over time (A-SMART). The proposed framework consists of three modules, namely, initialization, uncertainty propagation, and three-level global sensitivity analysis (GSA). The first two modules check the quality of the surrogate models and determine when and where we should refine the surrogate models from the reliability analysis perspective. Approaches are proposed to estimate the potential error of the failure probability estimate and to determine the locations of new training points. The three-level GSA method identifies the individual surrogate model for refinement. The combination of the three modules facilitates adaptive and efficient allocation of computational resources, and enables high accuracy in the reliability analysis result. The proposed framework is illustrated with two numerical examples.