Papers by Keyword: Steam Turbine Rotor

Abstract: The element-free method is a new numerical method, which requires only nodal data and whose shape functions are continual and differentiable. The element-free method employs moving least-square approximants to approximate original functions. In this paper, discrete equations of axial symmetry problem are obtained by variational principle and Gaussian quadrature. Several numerical examples indicate that the element-free method can obtain more accurate results about these problems, moreover, results and their gradients are continuous in the entire domain and post-processing to obtain a smooth gradient field is total unnecessary. Finaly, the element-free method is applied to heat conduction problems for steam turbine rotor.

Abstract: Influences of aging on the creep rupture properties of super-clean 9%CrMoV steel and 1%CrMoV steel, the heat resistant steels for steam turbine rotors of thermal power plants, are investigated. Using the as-received and the aging-treated materials of the two steels, creep rupture tests are carried out at 566°C. Creep rupture lives, creep fracture modes as well as the microstructural changes of the specimens are examined. It is made clear that the creep strength and the microstructural stability of super-clean 9%CrMoV steel are superior to those of 1%CrMoV steel in long-term services.

Abstract: Baysesian Neural Network approach for predicting the temper embrittlement of steam turbine rotor in service was proposed. The FATT50 (the fracture appearance transition temperature) of the rotors was predicted as a function of ratio of the two peak current densities (Ipr / Ip ) tested by electrochemical potentiodynamic reaction method, temperature of electrolyte, J-factor and grain size ( N ). A database was obtained from the test of electrochemical potentiodynamic reaction and Charpy impact. The Bayesian neural network technique was used for modeling of temper embrittlement. The neural network shows a more precise prediction of temper embrittlement of rotor steels than the prediction using multiple linear regression. The training error and verifying error is with the scatter of ±20°C. The results show that, for the temper embrittlement of rotor steels prediction, the prediction model based on Bayesian neural network is feasible and effective.