Browsing by Author "Saturday, Egbigenibo Genuine"

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    Creep-life usage analysis and tracking for industrial gas turbines
    (AIAA, 2017-07-14) Saturday, Egbigenibo Genuine; Li, Yiguang; Newby, Mike A.
    Creep-life usage analysis and tracking of first-stage turbine rotor blades of an aeroderivative industrial gas-turbine engine are investigated in this study. An engine performance model is created, and blade thermal and stress models are developed for the calculation of the blade material temperatures and stresses at different sections of the blade. A creep-life model is developed based on the Larson–Miller parameter method by taking inputs from the thermal and stress models. An integrated creep-life estimation system is developed by bringing together the engine performance model, the blade thermal and stress models, the creep-life model, and a data acquisition and preprocessing model. Relative creep-life consumption analysis using new concepts developed in this research is introduced for the analysis of creep-life consumption of the gas-turbine engine operating for a period of time; these concepts include equivalent creep life and equivalent creep factor. The developed algorithms have been applied to the creep-life tracking of an aeroderivative gas-turbine engine using its field test data. The results show that it is able to provide a quick evaluation and tracking of engine creep-life consumption and provide very useful information for gas-turbine operators to support their operation optimization and creep-life consumption monitoring.
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    ItemOpen Access
    Hot section components life usage analyses for industrial gas turbines
    (Cranfield University, 2015-11) Saturday, Egbigenibo Genuine; Li, Yiguang
    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.