Abstract
Performance deterioration in gas turbine engines (GTEs) depends on various factors in the ambient and the operating conditions. For example, humidity condensation at the inlet duct of a GTE creates water mist, which affects the fouling phenomena in the compressor and varies the performance. In this paper, the effective factors on the short-term performance deterioration of a GTE are identified and studied. GTE performance level is quantified with two physics-based performance indicators, calculated from the recorded operating data from the control system of a GTE over a full time between overhaul (TBO) period. A regularized particle filtering (RPF) framework is developed for filtering the indicator signals, and an adaptive neuro-fuzzy inference system (ANFIS) is then trained with the filtered signals and the effective ambient and the operating conditions, i.e., the power, the air mass flow, and the humidity condensation rate. The trained ANFIS model is then run to simulate the GTE performance deterioration in different conditions for system identification. The extracted behavior of the system clearly shows the dependency of the trend of performance deterioration on the operating conditions, especially the humidity condensation rate. The developed technique and the results can be utilized for GTE performance prediction, as well as for suggesting the optimum humidity supply at the GTE intake to control the performance deterioration rate.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.