Performance Analysis of an Aircraft Gas Turbine Engine using Particle Swarm Optimization

This paper presents the application of Particle Swarm Optimization (PSO) algorithm for optimization of the Gas Turbine Engine (GTE) fuel control system. In this study, the Wiener model for GTE as a block structure model is firstly developed. This representation is an appropriate model for controller tuning. Subsequently, based on the nonlinear GTE nature, a Fuzzy Logic Controller (FLC) with an initial rule base is designed for the engine fuel system. Then, the initial FLC is tuned by PSO with emphasis on the engine safety and time response. In this study, the optimization process is performed in two stages during which the Data Base (DB) and the Rule Base (RB) of the initial FLC are tuned sequentially. The results obtained from the simulation show the ability of the approach to achieve an acceptable time response and to attain a safe operation by limiting the turbine rotor acceleration.

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Application of particle swarm optimization in gas turbine engine fuel controller gain tuning

Engineering Optimization ◽

10.1080/0305215x.2011.576760 ◽

2012 ◽

Vol 44 (2) ◽

pp. 225-240 ◽

Cited By ~ 25

Author(s):

M. Montazeri-Gh ◽

S. Jafari ◽

M. R. Ilkhani

Keyword(s):

Particle Swarm Optimization ◽

Gas Turbine ◽

Particle Swarm ◽

Gas Turbine Engine ◽

Engine Fuel ◽

Swarm Optimization ◽

Turbine Engine ◽

Controller Gain

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A Novel Gas Turbine Engine Health Status Estimation Method Using Quantum-Behaved Particle Swarm Optimization

Mathematical Problems in Engineering ◽

10.1155/2014/302514 ◽

2014 ◽

Vol 2014 ◽

pp. 1-11 ◽

Cited By ~ 7

Author(s):

Xinyi Yang ◽

Wei Shen ◽

Shan Pang ◽

Benwei Li ◽

Keyi Jiang ◽

...

Keyword(s):

Particle Swarm Optimization ◽

Health Status ◽

Gas Turbine ◽

Estimation Method ◽

Particle Swarm ◽

Gas Turbine Engine ◽

Performance Model ◽

Estimation Accuracy ◽

Swarm Optimization ◽

Turbine Engine

Accurate gas turbine engine health status estimation is very important for engine applications and aircraft flight safety. Due to the fact that there are many to-be-estimated parameters, engine health status estimation is a very difficult optimization problem. Traditional gas path analysis (GPA) methods are based on the linearized thermodynamic engine performance model, and the estimation accuracy is not satisfactory on conditions that the nonlinearity of the engine model is significant. To solve this problem, a novel gas turbine engine health status estimation method has been developed. The method estimates degraded engine component parameters using quantum-behaved particle swarm optimization (QPSO) algorithm. And the engine health indices are calculated using these estimated component parameters. The new method was applied to turbine fan engine health status estimation and is compared with the other three representative methods. Results show that although the developed method is slower in computation speed than GPA methods it succeeds in estimating engine health status with the highest accuracy in all test cases and is proven to be a very suitable tool for off-line engine health status estimation.

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