Creep rupture forecasting for high performance energy systems

Abstract

The non-linear capabilities of artificial neural networks to model the dynamics of creep rupture and failure mechanisms are exploited to achieve failure forecasting in high performance energy systems. The proposed approach forecasts the time to rupture due to creep mechanism and consists of the library construction, the experimental data and measurements necessary for the training process, the measurements gathered during operation and the artificial neural network. The methodology is demonstrated on experimental data gathered for this purpose, for two frequently applied high-temperature/high-load materials, namely Grade 91 steel and Hastelloy XR. The results obtained demonstrate the capability of the proposed methodology to apply artificial neural networks to forecast the time to rupture and improve safety and efficiency of high performance systems.