Abstract:
Industrial gas turbines generally operate at a bit stable power levels and the hot section
critical components, especially high pressure turbine blades are prone to failure due to
creep. In some cases, plants are frequently shut down, thus, in addition to creep low cycle
fatigue failure equally sets in. Avoiding failure calls for proper monitoring of how the lives
of these components are being consumed. Efforts are thus being made to estimate the life of
the critical components of the gas turbine, but, the accuracy of the life prediction methods
employed has been an issue. In view of the above observations, in this research, a platform
has been developed to simultaneously examine engine life consumption due to creep,
fatigue and creep-fatigue interaction exploiting relative life analysis where the engine life
calculated is compared to a reference life in each failure mode. The results obtained are life
analysis factors which indicate how well the engine is being operated.
The Larson-Miller Parameter method is used for the creep life consumption analysis, the
modified universal slopes method is applied in the low cycle fatigue life estimation while
Taira's linear accumulation method is adopted for creep-fatigue interaction life calculation.
Fatigue cycles counting model is developed to estimate the fatigue cycles accumulated in
any period of engine operation. Blade thermal and stress models are developed together
with a data acquisition and pre-processing module to make the life calculations possible.
The developed models and the life analysis algorithms are implemented in PYTHIA,
Cranfield University's in-house gas turbine performance and diagnostics software to ensure
that reliable simulation results are obtained for life analysis.
The developed life analysis techniques are applied to several months of real engine
operation data, using LM2500+ engine operated by Manx Utilities at the Isle of Man to test
the applicability and the feasibility of the methods. The developed algorithms provide quick
evaluation and tracking of engine life. The lifing algorithms developed in this research
could be applied to different engines. The relative influences of different factors affecting
engine life consumption were investigated by considering each effect on engine life
consumtion at different engine operation conditions and it was observed that shaft power
level has significant impact on engine life consumption while compressor degradation has
more impact on engine life consumption than high pressure turbine degradation. The lifing
methodologies developed in this work will help engine operators in their engine conditions
monitoring and condition-based maintenance.