GA-based design-point performance adaptation and its comparison with ICM-based approach

Abstract

Accurate performance simulation and estimation of gas turbine engines is very useful for gas turbine manufacturers and users alike and such a simulation normally starts from its design-point. When some of the engine component parameters for an existing engine are not available, they must be estimated in order that the performance analysis can be started. Therefore, the simulated design-point performance of an engine may be slightly different from its actual performance. In this paper, a Genetic Algorithm (GA) based non-linear gas turbine design-point performance adaptation approach has been presented to best estimate the unknown component parameters and match available design-point engine performance. In the approach, the component parameters may be compressor pressure ratios and efficiencies, turbine entry temperature, turbine efficiencies, engine mass flow rate, cooling flows, by-pass ratio, etc. The engine performance parameters may be thrust and SFC for aero engines, shaft power and thermal efficiency for industrial engines, gas path pressures and temperatures, etc. To select the most appropriate to-be-adapted component parameters, a sensitivity analysis is used to analyze the sensitivity of all potential component parameters against the engine performance parameters. The adaptation approach has been applied to an industrial gas turbine engine to test the effectiveness of the approach. The approach has also been compared with a non-linear Influence Coefficient Matrix (ICM) based adaptation method and the advantages and disadvantages of the two adaptation methods have been compared with each other. The application shows that the sensitivity analysis is very useful in the selection of the to-be-adapted component parameters and the GA-based adaptation approach is able to produce good quality engine models at design-point. Compared with the non-linear ICM-based method, the GA-based performance adaptation method is more robust but slower in computation and relatively less accurate.