Title :
Creep rupture forecasting for high performance energy systems
Author :
Chatzidakis, Stylianos ; Alamaniotis, M. ; Tsoukalas, L.H.
Author_Institution :
Sch. of Nucl. Eng., Purdue Univ., West Lafayette, IN, USA
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.
Keywords :
creep fracture; failure (mechanical); failure analysis; iron alloys; materials science computing; mechanical engineering computing; molybdenum alloys; neural nets; nickel alloys; steel; Grade 91 steel; Hastelloy XR; artificial neural networks; creep rupture forecasting; failure forecasting; failure mechanisms; high performance energy systems; high-temperature-high-load materials; library construction; nonlinear capability; training process; Artificial neural networks; Creep; Metals; Stress; Temperature measurement; Training; creep rupture; high performance energy systems; neural networks;
Conference_Titel :
Information, Intelligence, Systems and Applications, IISA 2014, The 5th International Conference on
Conference_Location :
Chania
DOI :
10.1109/IISA.2014.6878824
