Speaker
Description
Mechanism-based constitutive models for creep-fatigue analysis and the corresponding user material subroutines for finite element codes were developed for years by many research groups. However, the application of such models in the industry is limited by the availability of accurate experimental data and the time-consuming parameter identification.
The presentation focuses on the identification of material parameters with artificial neural networks (NNs) and the revision of a mechanism-based constitutive model for 9-12\%Cr heat-resistant steels. The use of NNs is realized in a Python environment using machine learning libraries Keras and TensorFlow. To test the revised constitutive model the Simcenter Nastran software, specifically a NXUMAT subroutine is used.
The results of multiple calculations, which are run for different stresses and temperatures, are compared with experimental data. The ability of the subroutine to work with variable mechanical and thermal loads and a complex geometry is tested by LCF and TMF tests. Moreover, a steam turbine rotor and an inner casing of the rotor under realistic boundary conditions are analyzed by the subroutine to test the ability to analyze the behavior of real components. The examples of benchmark tests and analysis of power plant components are presented.
