• Journal of Aerospace System Engineering
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• v.7 no.1
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• pp.8-18
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• 2013

Recently, the advanced condition monitoring methods such as the model-based method and the artificial intelligent method have been applied to maximize the availability as well as to minimize the maintenance cost of the aircraft gas turbines. Among them the non-linear GPA(Gas Path Analysis) method and the GA(Genetic Algorithms) have lots of advantages to diagnose the engines compared to other advanced condition monitoring methods such as the linear GPA, fuzzy logic and neural networks. Therefore this work applies both the non-linear GPA and the GA to diagnose AE3007 turbofan engine for an aircraft, and in case of having sensor noise and bias it is confirmed that the GA is better than the GPA through the comparison of two methods.

• Journal of the Korean Society of Propulsion Engineers
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• v.27 no.1
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• pp.37-48
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• 2023

In this study, performance analysis modeling of two spool mixed flow type turbofan engine according to steady-state and transient is performed. The target engine is selected as F100-PW-229 from Pratt & Whitney, and main engine components including fan, high pressure compressors, combustion, high pressure turbines, low pressure turbines, mixer, convergent-divergent nozzle are modeled. The cooling effect of turbine through secondary flow path are considered in engine simulation model. We develop in-house Matlab/Simulink-based engine performance analysis program capable of analyzing internal engine state and compare it with GASTURB which is generally used as a commercial engine analysis program.