On-line Bayesian Model Updating and Model Selection of a Piece-wise Model for the Creep-Growth Rate Prediction of a Nuclear Component

Abstract

This work presents an application of the recently-developed Sequential Ensemble Monte Carlo sampler in performing on-line Bayesian model updating for the Prognostics Health Management of a passive component of an Advanced Reactor. The passive component involves a stainless-steel material subjected to a thermal creep deformation whose growth rate is modelled by a continuous piece-wise model consisting of 3 models, each representing a creep-growth stage.

There are 2 investigations done in this research. For the first investigation, the model identification capability of the Sequential Monte Carlo sampler is evaluated in identifying the most probable model for each creep-growth stage. For the second investigation, the on-line Bayesian model updating procedure via the aforementioned sampler is then undertaken. In addition, a method is proposed where the model updating approach will be done for each model sequentially across the different creep-growth stage. This process involves utilising information of the boundary conditions obtained from the model output interval at the transition times to determine the prior bounds for each model parameter to be updated. This method seeks to minimise the discontinuity in the updated piece-wise model at the transition times. From there, the Remaining Useful Life analysis on the component is performed.