About this Abstract |
Meeting |
MS&T24: Materials Science & Technology
|
Symposium
|
Computational Materials for Qualification and Certification
|
Presentation Title |
Towards a Probabilitic Model for the Assessment of Gas Turbine Components |
Author(s) |
Peter Gumbsch, Jan Riza Radners, Christoph Riza Schweizer, Michael Riza Schlesinger, Stefan Eckmann, Malek Al-Ameri, Christian Amann, Kai Kadau |
On-Site Speaker (Planned) |
Peter Gumbsch |
Abstract Scope |
Intermittent renewable energy sources are becoming increasingly important. This creates a demand for more flexible gas turbine operation conditions. Therefore, the number of startup and shutdown cycles – the low cycle fatigue (LCF) loading of a gas turbine component – increases substantially. This work investigates the LCF performance of a widely used rotor blade material, to support more flexible power plant operation. One of the goals of this multi-year government funded project is the development of a probabilistic life model for cast MAR-M247 including defects such as porosity. A comprehensive test program on defective and defect-free specimens at realistic engine conditions was conducted. A first deterministic fatigue life model that accounts for material defects is proposed. Defects and their location in a component always come with a variety of uncertainties. Their influence must be understood and cast into additional model parameters for a probabilistic approach. |