Aerospace and Electronic Systems Magazine July 2018 - 5

and corresponding simulators, which are valid for estimating both
steady-state and transient behaviors of the engine. He indicated
that using linear characteristics of the components is not suitable
for modeling and the results of the linear model have more errors than nonlinear one. In addition, Chacartegui et al. [6] worked
on real-time simulation and control of medium size gas turbines
during transient operation. They used zero-dimensional conservation equations applied to each component of the engine for steadystate performance calculations and intermediate plena to account
for unsteady effects. Benini and Giacometti [7] did some research
on the design, manufacturing, and operation of a small turbojetengine. They designed a small turbojet engine with 200 Newton
static-thrust. They used a compressor with a 2.66:1 compression
ratio operating at 60,000 rpm, an annular combustion chamber
with direct-flow, and a single stage turbine with 950K Turbine Inlet Temperature (TIT) [7].

USING DATA MINING METHODS FOR STUDYING GAS
TURBINE ENGINES
Other studies show the usage of artificial neural networks (ANN)
in modeling gas turbine engines [12]-[18] and in the present work,
two different types of these networks multilayer perceptron (MLP)
and radial basis function (RBF) neural networks are employed.
Some researchers used MLP and RBF networks for gas turbine
monitoring and diagnosis applications [12], [13]. Bartolini et al.
[16] applied ANN methods to: (1) complete performance diagrams
for unavailable experimental data; (2) assess the influence of ambient parameters on performance; and (3) analyze and predict emissions of pollutants in the exhausts. They also investigated sensitivity of the machine's behavior in different ambient conditions.
Using the ANN method, they completed performance maps by filling information gaps in experimental data and, also, they evaluated
the effects of ambient conditions on performance of the engine. In
addition, Yoon et al. [17] analyzed performance deterioration of a
micro gas turbine and used neural network to predict deteriorated
component characteristics. They set up a program to simulate the
operation of a micro gas turbine and then the deterioration of each
component (compressor, turbine, and recuperator). They modeled
changes in the components' characteristic parameters, such as comJULY 2018

pressor and turbine efficiency, their flow capacities, recuperator effectiveness, and pressure drop. They gave measurable performance
parameters as inputs to the neural network and then characteristic
parameters of each component were predicted and compared with
original data. They also reported prediction accuracy decrease
caused when a lower number of input parameter are used.
On the other hand, Asgari et al. [18] investigated ANN based
system identification for a single-shaft gas turbine. They compiled
a comprehensive computer program code in a MATLAB environment and created and trained different ANN models with two-layer
feed-forward MLP structure. Their code consisted of various training functions, different number of neurons, as well as a variety
of transfer (activation) functions for hidden and output layers of
the network. They showed that the optimal model for a two-layer
network with MLP structure, consisted of 20 neurons in its hidden
layer and used trainlm as its training function, as well as tansig and
logsid as its transfer functions for hidden and output layers. Also,
they observed that trainlm has a superior performance in terms of
minimum mean squared error compared with other training functions.
Also, some work has been performed on implementing the regression method in gas turbine engine. Memon et al. [19] carried
out research on the optimization of the gas turbine engine by using
multiple polynomial regression (MPR). They investigated effects
of important operating parameters like compressor inlet temperature, TIT, and pressure ratio on overall cycle performance and CO2
emissions. They used MPR models to correlate the performance
characteristics like net power output, fuel consumption, energy and
exergy efficiencies, and CO2 emissions and operating parameters.
Then, they optimized the operating parameters.
In view of the fact that excessive turbine temperatures result
in decreased life or catastrophic failure, EGT is one of the most
critical parameters in a gas turbine [20]. In older machines, when
temperatures were not as high, turbine inlet temperature was measured directly. In the current generation of machines, combustor
discharge temperatures are too high for the type of instrumentation
available so, intermediate stage or EGT is used as a turbine inlet
temperature indicator [20].
Some of the research showed the effects of different parameters on the amount of EGT [21]. Upon bleed extraction, down-

IEEE A&E SYSTEMS MAGAZINE

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