Surrogate model based sequential sampling estimation of conformance probability for computationally expensive systems: application to fire safety science

Abstract

The use of complex simulation systems has become common practice when physical experiments are not feasible or when too few are feasible. The statistical modelling of numerical experiments with kriging models yields a probabilistic decision framework to assess the probability of failure of the system. Combining fast low-fidelity simulations with costly high-fidelity simulations has proved an efficient method to decrease the burden of costly simulations when predicting the output of a system. In addition, sequential design is commonly used to estimate the probability of failure of a system modelled by kriging. In this work, a methodology is derived to benefit from sequential design in a multi-fidelity framework to predict the probability of failure of a computationally expensive system and its uncertainty. The methodology is applied to a fire safety engineering case study to assess the probability of non-conformity of a smoke control system from complex numerical fire tools.