Life Fraction Hardening Applied to a Modified Theta Projection Creep Model for a Nickel-Based Super-Alloy
This paper presents the application of a life fraction hardening rule to the analytical calculation of creep in hot section components. Accurate prediction of creep is critical to assuring the mechanical integrity of heavy-duty, industrial gas turbine (IGT) hardware. The accuracy of such predictions depend upon both the creep models assumed and how those models are implemented in a finite element solution. A modified theta projection creep model for a nickel-based super alloy was presented in a previous paper as an accurate simulation of creep behavior [1]. Application of such a user defined creep law depends upon definition of a hardening rule in the form of either an explicit or an implicit integration scheme in order to calculate incremental strains during any time increment. Time hardening is the simplest and least computationally intensive of the two most common hardening rules, but does not correctly show the effect of changing stresses or temperatures. Strain hardening may provide the most accurate solution, but the creep models are too complex to invert, which results in highly iterative and computationally intensive solutions. A life fraction hardening rule has been presented in other works [2] as a compromise between time hardening and strain hardening. Life fraction hardening is presented here as a highly efficient and accurate means of calculating incremental creep strain when applied to a modified theta projection creep model. A user creep subroutine was defined using a state variable to represent the strain life fraction at any time. By using the time to tertiary creep as the denominator for the life fraction, no new material constants are needed to relate to creep failure. The start of tertiary creep is effectively considered to be a failure. Additional design insight can be provided through the inclusion of other state variables to calculate temperature margins at current conditions. Material testing with changing stress levels will be used to help validate the technique. A simplified example of the technique is presented in the paper. More accurate creep predictions allow our company to improve the structural integrity of its turbine blades and vanes.Copyright © 2014 by Alstom Technologie AG
A method to characterize asymmetrical three-stage creep of ordinary refractory ceramics and its application for numerical modelling
Abstract During operation, thermomechanical stresses occur in refractory linings. Under elevated stress and temperatures, these ceramics experience primary creep, which can further proceed to the secondary and tertiary creep stages. This necessitates a characterization of their three-stage creep behavior. Hence, two advanced uniaxial tensile and compressive creep testing devices are utilized. The Norton-Bailey creep equations and an inverse identification procedure are applied for the evaluation of the creep curves. To account for the full three-stage creep behavior in thermomechanical modelling activities, a creep-stage transition criterion is identified and subsequently implemented together with the Norton-Bailey creep-strain rate representations in a new developed creep model. The finite element simulation results from different creep testing procedures are in accordance with the corresponding experimental results of a magnesia-chromite refractory ceramic. The study also reveals the temperature-dependent asymmetrical creep behavior of the material in terms of the creep-strain rates and critical creep strains.
A Short-Term and High-Resolution Distribution System Load Forecasting Approach Using Support Vector Regression With Hybrid Parameters Optimization
This paper proposes an approach for distribution system load forecasting, which aims to provide highly accurate short-term load forecasting with high resolution utilizing a support vector regression (SVR) based forecaster and a two-step hybrid parameters optimization method. Specifically, because the load profiles in distribution systems contain abrupt deviations, a data normalization is designed as the pretreatment for the collected historical load data. Then an SVR model is trained by the load data to forecast the future load. For better performance of SVR, a two-step hybrid optimization algorithm is proposed to determine the best parameters. In the first step of the hybrid optimization algorithm, a designed grid traverse algorithm (GTA) is used to narrow the parameters searching area from a global to local space. In the second step, based on the result of the GTA, particle swarm optimization is used to determine the best parameters in the local parameter space. After the best parameters are determined, the SVR model is used to forecast the short-term load deviation in the distribution system. The performance of the proposed approach is compared to some classic methods in later sections of this paper.