In performance simulation of gas turbine, some iteration processes for searching the operating point, which should be matched through gas path components, would be needed. Therefore if the engine is very complicated, the efficiency of convergence is getting lower due to increasing the number of routines and iterations for matching procedure. Furthermore, there may be some problems in numerical calculation such as the tendency of divergence, instability, and increase of calculation time. In the traditional matching methods, after some variable parameters are initially assumed and then calculated, the calculation is iterated to converge within an appropriate error range by expressing the new variables with the previous calculation error function. Even though these traditional methods show comparatively reasonable results in performance simulation, there may be some unsatisfied results in particular cases. Moreover, in this case it can be more difficult in more precise calculation due to simplification by using various assumptions. Therefore in order to search the optimal matching point in performance simulation of gas turbine efficiently and quickly, the fuzzy approaches were applied. In performance simulation using the fuzzy logic, it was found that more effective engine operating points were found and calculation time was considerably reduced less than the traditional methods. Furthermore, MATLAB was used to apply the fuzzy logic easily as well as to make user-friendly circumstance in performance analysis.

1.
Sellers, J. F., and Daniele, C. J., 1975, “DYNGEN-A Program for Calculating Steady-State and Transient Performance of Turbojet and Turbofan Engines,” NASA TN D-7901.
2.
Palmer, J. R., and Yan, C. Z., 1985, “TURBOTRANS—A Programming Language for the Performance Simulation of Arbitrary Gas Turbine Engines With Arbitrary Control Systems,” International Journal of Turbo and Jet Engines: 19–28.
3.
Kurzke, J., 1998, “Manual GASTURB 8.0 for Windows—A Program to Calculate Design and Off-Design Performance of Gas Turbines,” technical report.
4.
Crosa
,
G.
et al.
,
1998
, “
Heavy-Duty Gas Turbine Plant Aerothermo Dynamic Simulation Using SIMULINK
,”
ASME J. Turbomach.
,
120
, pp.
550
560
.
5.
Cohen, H., Rogers, G. F. C., and Saravanamuttoo, H. I. H., 1996, Gas Turbine Theory, 4th Ed., Longman, London.
6.
Pilidis, P., 1996, “Gas Turbine Performance,” Cranfield Short Course Note, UK.
7.
Kong, C. D., 2000, “Propulsion System Integration of Turboprop Aircraft for Basic Trainer,” ASME Paper 00-GT-10.
8.
Na, J. J., 1996, “A Study on PT6A-62 Engine Install Performance Analysis,” Agency of Defense Development, Technical Report.
9.
Kong, C. D., and Ki, J. Y., 2001, “Performance Simulation of Turboprop Engine for Basic Trainer,” ASME Paper 00-GT-391.
10.
Tsoukalas, L. H., and Uhrig, R. E., 1997, “Fuzzy and Neural Approaches in Engineering,” Wiley, New York.
11.
Math Works, 2001, “MATLAB User Guide Ver. 6.1,” The Math Works, Inc., USA.
12.
Math Works, 2001, Fuzzy Logic Toolbox User Guide Ver. 2.0, The Math Works, Inc.
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