A kind of numerical model combined with genetic algorithm and back propagation neural network for creep-fatigue life prediction and optimization of double-layered annulus metal hydride reactor and verification of ASME-NH code

Abstract

The safety and stability of metal hydride reactor (MHR) structure are significantly important for normal operations of hydrogen isotope storage and supply system. Moreover, considering that the experiments of studying the fatigue and creep properties of MHR structure are always time, money, difficulty and energy consuming. To further improve the service life of MHR as well as lower the failure risk, this paper proposes a system integration method based on ASME code validation, which combines genetic algorithm (GA) and back propagation neural network (BPNN) for rapid assessment of creep-fatigue life (CFL) and optimal design of structural parameters. First, numerical simulation is applied to perform thermal-mechanical coupled finite element analysis and verified by ASME code. Then, parametric sensitivity analysis and experimental design of parameter-based training set based on Taguchi method were performed, and response values are obtained from physical models combined with numerical simulations. Finally, a parametric optimization procedure combining BPNN and GA is developed based on the training data. In the workflow, parametric data flow is passed through the experimental design, numerical simulation, prediction and optimization processes. The results show that the proposed BPNN proxy model based on ASME code validation has good performance in predicting the life of MHR. Based on this, the life of the MHR is improved by 8% compared to the reference sample.