Stefan Linn

Robust Methods for Creep Fatigue Analysis of Power Plant Components Under Cyclic Transient Thermal Loading

The aim of this paper is to apply robust mechanisms-based material laws to the analysis of typical high-temperature power plant components during an idealized start-up, hold time and shut-down sequence under a moderate temperature gradient. Among others a robust constitutive model is discussed, which is able to reflect inelastic deformation, hardening/recovery, softening and damage processes at high temperature. The model is applied for a creep analysis of advanced 9–12%CrMoV heat resistant steels and calibrated in particular case against experimental data for 10%CrMoV steel type. For a steam temperature profile transient heat transfer analysis of an idealized steam turbine component is performed providing the temperature field. From the subsequent structural analysis with the inelastic constitutive model local stress and strain state variations are obtained. As an outcome a multi-axial thermo-mechanical fatigue (TMF) loading loop for one or several loading cycles can be generated. They serve as input for a fatigue life assessment based on the generalized damage accumulation rule, whose results come close to reality. In addition, the accuracy of a simplified method which allows a rapid estimation of notch stresses and strains using a notch assessment rule (NAR) [1] based on Neuber approach is examined.Copyright

Evaluation of Property Scatter of Nickel Base Alloy IN 738 LC

The nickel base alloy IN 738 LC is in use for gas turbine blades since more than 25 years. In high temperature creep testing the conventionally cast alloy exhibits a comparably large property scatter which requires high safety margins in design and dimensioning and subsequently causes an incomplete exploitation of the materials potential. The reasons for this property scatter were investigated and traced back to different influencing factors. Parallel to investigations on the microstructure of post-exposure material and conventional scatter band analysis, artificial neural networks were successfully applied to discover relations between the chemical composition of the individual melt and the position of the corresponding test results within the global scatter band. Recommendations for a lifting of the lower scatter band boundary and the mean curve are derived.