Probabilistic Life Assessment and Aged Materials Testing for Service Feedback of Gas Turbine Components

The goal of this project is to improve lifetime model prediction for Ni-based superalloy power plant components. The microstructure and properties of parts exposed in the field for up to 32,000 hours will be characterized to determine the evolution of key lifetime damage parameters. Synergy between deterministic and probabilistic lifetime models will also be evaluated. Siemens will select the parts exposed in the field to be characterized, and will conduct lifetime assessment using their internal probabilistic model. Reliability of key components of the power plant such as steam or gas turbines and generators is of prime importance. Many utilities are interested in extending the life of turbine-generator components to reduce costs while maintaining safe operating conditions. During operation, these materials undergo different metallurgical degradation processes due to complex thermomechanical loadings and corrosion in aggressive environments. So remaining life assessment of these components/materials is essential for the lifetime extension of aged units through repair work, continuous inspection, and replacement of the degraded parts. The project focus is to improve available lifetime prediction models using data obtained from based nickel-based superalloy power plant components that have undergone long-term service. Technical objectives include: Evaluate the complementarity between deterministic and probabilistic models for gas turbine material systems, with a focus on Haynes 282 of interest in the 600-760 °C temperature range for A-USC program and between 800-950 °C for the gas turbine combustor section. Characterize the microstructure and mechanical and thermal properties of components that have operated in power plants for periods of time between 8,000h and 32,000h. Use the microstructural characterization data to validate the lifetime models based on the service history of the components.